Research Article EndEffector Trajectory Tracking Control of Space Robot with L 2 Gain Performance


 Quentin Shanon Barber
 2 years ago
 Views:
Transcription
1 Mathematical Problems in Engineering Volume 5, Article ID 7534, 9 pages Research Article EndEffector Trajectory Tracking Control of Space Robot with L Gain Performance Haibo Zhang,, Dayi Wang,, Chunling Wei,, andbingxiao 3 Beijing Institute of Control Engineering, Beijing 9, China National Key Laboratory of Science and Technology on Space Intelligent Control, Beijing 9, China 3 CollegeofEngineering,BohaiUniversity,Jinzhou3,China Correspondence should be addressed to Haibo Zhang; Received 7 April 5; Accepted 6 September 5 Academic Editor: Xinggang Yan Copyright 5 Haibo Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents a novel solution to the control problem of endeffector robust trajectory tracking for space robot. External disturbance and system uncertainties are addressed. For the considered robot operating in freefloating mode, a Chebyshev neural network is introduced to estimate system uncertainties and external disturbances. An adaptive controller is then proposed. The closedloop system is guaranteed to be ultimately uniformly bounded. The key feature of this proposed approach is that, by choosing appropriate control gains, it can achieve any given small level of L gain disturbance attenuation from external disturbance to system output. The tracking performance is evaluated through a numerical example.. Introduction With the development and launch of spacecraft, the function of spacecrafts is becoming more and more complex. As a result, any component failure will deteriorate spacecraft s performance and sometimes even make the planned mission totally terminate. Aiming to decrease economic loss induced by spacecraft failures, onorbit servicing has received considerable attentions. However, due to the harsh operating environment such as high temperature, it is very difficult for astronauts to accomplish orbital works. This makes space robot become the best option to accomplish orbital repair. Additionally, the space robot can also perform other onorbit servicing missions such as repair, assembly, refueling, and/or upgrade of spacecraft. This leads to development of space robot techniques [ 6]. For space robot, the endeffector control in the presence of uncertain kinematics and dynamics is becoming one of the challenges that need to be addressed. In [7], the problem of uncertain kinematics in space robot s endeffector was investigated. In [8], a feedback control approach was presented to accomplish position and attitude control maneuver. The uncertainties in endeffector were addressed, and experimental results were given to verify the effectiveness of the proposed controller. Taking control input saturation of endeffector s actuator into consideration, an adaptive controller was developed to perform trajectories tracking [9]. The tracking error was governed to be semiglobally asymptotic stable. On the other hand, on the standpoint of tracking control in mission space, many researchers have developed many effective control algorithms. In [], an adaptive controller was presented to achieve tracking control of endeffector, and uncertain kinematics and dynamics were solved. The controller was able to guarantee the asymptotic stability of the closedloop system, and ground test was also conducted to demonstrate its effectiveness. In [], an adaptive control scheme without velocity measurements was developed. The demand of decreasing numbers of measurement sensors was satisfied, and the closedloop tracking system was governed tobestable.in[,3],anothernoveladaptivecontrollerwas also synthesized in the presence of the dynamics of actuators, uncertain kinematics, and dynamics. The preceding approaches were proposed based on the assumption that the dynamic model can be linearized. However, this assumption would not be satisfied for space robot. As a result, the above control methodologies were not applicable to space robot. Moreover, in the above nonlinear controller design, the developed controllers can only ensure
2 Mathematical Problems in Engineering the stability of the resulted system. They were not able to achieve disturbance attenuation. It greatly limits the application of those schemes. To achieve tracking control with disturbance attenuation, L gain control approach is one of the most applied techniques [4 8]. Inspired by the great performance of L gain control, this work will investigate the problem of trajectory tracking control of space robot endeffector. Uncertain kinematics and dynamics will be addressed. To solve those uncertainties, Chebyshev neural network will be used to approximate those uncertainties, and an adaptive controller will be developed to compensate for these uncertainties. The main contribution of this work is that the desired trajectories can be followed with high accuracy, and L gain performance is achieved in the presence of external disturbances. Thispaperisorganizedasfollows.InSectionwerecall some necessary notation, definitions, preliminary results, and the mathematical model used to investigate space robot endeffector trajectory tracking control problem. The control solution with L gain performance is presented in Section 3. Section 4 demonstrates the application of the proposed control scheme to a space robot. Conclusions are given in Section 5.. Preliminaries and Problem Formulation The notation adopted in this paper is fairly standard. Let R (resp., R + ) denote the set of real (resp., positive real) numbers. The set of m by n real matrices is denoted as R m n. For a given vector, denotes the vector Euclidean norm; for a given matrix, represents its induced Euclidean norm, and F denotes the matrix Frobenius norm. Tr( ) denotes the trace operator... Definition. Our main results relay on the following stability definitions for a given nonlinear system: ξ (t) = f (ξ,t) + g (ξ,t) d, () y = h (ξ,t), where f(ξ,t): R n R + R n are locally Lipschitz and piecewise continuous in t and d R s is an exogenous disturbance, while y R m is the system output. We denote by ξ(x,t,t)the solution to the nonlinear system () with the initial state x and initial time t. Definition (see [8]). Let γ > be a given constant; then system()issaidtobeachievedwithl gain disturbance attenuation level of γ from external disturbance d to output y,ifthefollowinginequalityholds: V (t) V() γ t d t dυ y dυ, () where V R is a Lyapunov candidate function to be chosen... System Description of Space Robot. Consider nlink space robot with each joint driven by a dedicated, armaturecontrolled dc motor and operating in a freefloating mode. Define x R m (m n) as the endeffector positive and attitude vector; then the space robot kinematics and dynamics can be described as x =[J G (q, q) +ΔJ G (q, q)] q m, (3) [M (q) +ΔM (q)] q m + C (q, q) q m +ΔC (q, q) q m = τ m + τ d, where x =[v T e ω T e ]T R m denotes the generalized velocity vector of the endeffector; here v e and ω e are the velocity and the angular velocity of the endeffector, respectively. The vector q = [q T q T m ]T R n is the generalized coordinates. The term J G (q, q) R n denotes the generalized but known/nominal Jacobian matrix, while ΔJ G (q, q) R n is the uncertain Jacobian matrix. M (q) R n n is the nominal inertia matrix; ΔM(q) R n n is the uncertain inertia. C (q, q) R n is the nominal vector of Coriolis and centrifugal forces, and ΔC(q, q) R n denotes its uncertain part. τ m R n is the vector of control torque, and τ d R n is the vector of external disturbance. To control the plant (3)(4) successfully, the following assumption is assumed to be valid throughout this paper. Assumption. The nominal Jacobian matrix J G (q, q) R n and the matrix M (q) Rn n are bounded. There exist two positive scalars Δ JG R + and Δ M R + such that J G (q, q) Δ JG and M (q) Δ M,respectively..3. Chebyshev Neural Network. The Chebyshev neural network (CNN) [9] has been shown to be capable of universally approximating any welldefined functions over a compact set to any degree of accuracy. Therefore, CNN will be used to estimate the uncertain terms in the space robot dynamics. The CNN structure employed in this paper is with single layer and the Chebyshev polynomial basis function. This basis function is a set of Chebyshev differential equations and generated by the following twoterm recursive formula: T i+ (x) =xt i (x) T i (x), (5) T (x) =. In this paper, T (x) = x is chosen. Define X = [x x x m ] T ; then the Chebyshev polynomial equation can be described as Θ (X) =[,T (x ),...,T N (x ),...,T (x m ),..., (6) T N (x m )], where n is the order of Chebyshev polynomial chosen and Θ(X) is called the Chebyshev polynomial basis function. As a result, for any continuous nonlinear function vector f Ni (X) R n, it can be approximated by CNN as f Ni (X) = (W i )T Θ i (X) + ε i, (7) where ε i R n + is the bounded CNN approximation error, W i is an optimal weight matrix, and Θ i (X) is the Chebyshev polynomial basis function. (4)
3 Mathematical Problems in Engineering 3 Assumption 3. The optimal weight matrix W i is bounded. That is, there exists a positive constant W Mi R + such that tr((w i )T W i ) W Mi..4. Problem Statement. The objective of the proposed design methodology is to construct a control input function such that the endeffector trajectory state x of the controlled system is capable of tracking a desired reference trajectory x d R n in spite of the existence of system uncertainties and external disturbances. 3. EndEffector Trajectory Tracking Control Design with Uncertain Kinematics and Dynamics Because the system dynamics described in (3)(4) cannot be linearized, CNN will be applied in this section to approximate the unknown system dynamics which can be not linearized in the system. Then, an adaptive backstepping control law will be presented to achieve trajectory tracking control for the space robot endeffector. Moreover, the tracking performance is evaluated by L gain from external disturbance/system uncertainties to the system outputs of the robot and desired trajectories. 3.. Control Law Design with L Gain Performance. In the controller design, it is assumed that the trajectory of the space robot s endeffector is always within the Path Independent Workspace (PIW). All the points in the PIW are guaranteed not to have dynamic singularities. As a result, it can ensure that J G (q, q) R n will be always invertible. Define the trajectory tracking error as z = x x d. (8) Combining with the dynamics (3), it leads to the time derivative of z as z = x x d = J G q m + f x d, (9) where f (q, q) =ΔJ G (q, q) q m denotes the uncertain kinematics. To remove the effect of the above uncertain kinematics, CNNisusedtoapproximatef (q, q);thatis, f =(W )T Θ + ε, () where ε R n + is the bounded CNN approximation error, W is an optimal weight matrix, and Θ is the Chebyshev polynomial basis function. To accomplish controller design, a virtual control input is q m = J G ( k z + x d Ŵ T Θ ), () where k R + is a constant and Ŵ T is the estimate of the term (W )T in (). Additionally, define an error vector for q m and q m ;thatis, z = q m q m. () From the dynamics (4), one has z = q m q m = M (τ m + τ d C )+f q m, (3) where f (q, q) = q m + M (ΔC Mq m) denotes the uncertain dynamics. As the same technique applied to handle with uncertain kinematics, CNN will also be applied to approximate f (q, q).itthusfollowsthat f =(W )T Θ + ε, (4) where ε R n + is the bounded CNN approximation error, W is an optimal weight matrix, and Θ is the Chebyshev polynomial basis function. Introduce two new variables s = x x d +k (x x d ) and ŝ = J G q m + Ŵ T Θ x d +k (x x d ). (5) Then, it leads to s ŝ = W T Θ + ε,whereŵ i is the estimate of the optimal weight matrix W i and W i = W i Ŵ i is the estimate error, i=,. Theorem 4. Consider the space robot system described by (3) (4) with external disturbance and system uncertainties; design τ m as τ m = C + M ( k z J G T z J GTŝ) + M ( Ŵ T Θ + q m ). (6) Let Ŵ i be updated by Ŵ = Θ ξ z T η ξ z Ŵ, (7) Ŵ = Θ ξ z T η ξ z Ŵ, (8) where k R + and R + are two control gains and ξ R +, ξ R +, η R +,andη R + are parameters for the adaptive laws. Suppose that the control parameters are chosen such that k λ + 4γ + η W M, (9) B k λ + 4γ + Δ 4γ Δ J G + η W M, () where λ, λ,andγ are positive constants. Then, the closedloop attitude tracking system is guaranteed to be ultimately uniformly bounded. The L gain disturbance attenuation level is achieved. Moreover, when there is no external disturbance, the closedloop system is asymptotically stable. 3.. Stability Analysis. For the introduced variables, applying () and (5) (7), the dynamics for z and z can be rewritten as z = k z + J G z + W T Θ + ε, z = J T G z k z J GTŝ + M τ d + W T Θ ε. ()
4 4 Mathematical Problems in Engineering Proof of Theorem 4. Choose a Lyapunov candidate function as V= i= Calculating the time derivative of V yields [z T i z i +ξ i tr { W T i W i }]. () V=z T [ k z + J G z + W T Θ + ε ] + z T [ J T G z k z J GTŝ + M τ d] + z T [ W T Θ + ε ]+ξ tr { W T W } +ξ tr { W T W }. According to the properties of matrix trace, one has z T i W T i Θ i = tr (z T i W T i Θ i)=tr ( W T i Θ iz T i ) tr ( W T i Ŵi) tr ( W T i W i )+ tr ((W i )T W i ) tr ( W T i W i ) = tr ((W i )T W i ) tr ( W T i W i ). Applying () and (5), it can be obtained that (3) (4) J G z = J G ( q m q m )=ŝ; (5) here W i = Ŵ i is used. Based on Assumption, it leaves (3) as follows: V k z (k + Δ J G ) z + η + η z tr {W T W } η z tr { W T W } z tr {W T W } η z tr { W T W } +Δ B zt τ d + z T ε + z T ε. From Assumption 3, inequality (6) can be simplified as V (k η W M ) z (k + Δ J G η W M ) z η z W η F z W F μ z μ z +Δ B zt τ d + z T ε + z T ε, (6) (7) where μ =k η W M / and μ =k + Δ J G η W M /. Position in yaxis (m) Position in xaxis (m) Figure:Thedesiredtrajectory(solidline)andtheactualtrajectory (dashed line) of endeffector with k =and k =5. DefinelumpeddisturbanceasΓ = [τ T d εt ε T ]T and system output as y =[λ z T H= V+y T y γ Γ T Γ (μ λ 4γ ) z ( Δ B γ z γ τ d ) λ z T ]T ;then B (μ λ 4γ Δ 4γ ) z (8) ( γ z γ ε ) ( γ z γ ε ). With the choice of γ and the control gains in (9)(), it results in μ λ + 4γ, μ λ + 4γ + Δ (9) B 4γ. Then, it can be obtained from (8) that H= V+y T y γ Γ T Γ. (3) By integrating inequality (3) from to t,itcanbeshownthat V (t) V() γ t Γ t dυ y dυ. (3) Applying Definition, it can be concluded that the trajectory tracking is performed with L gain disturbance attenuation level, and the closedloop system is ultimately uniformly bounded. Thereby the proof is completed here.
5 Mathematical Problems in Engineering (a) The initial response (b) The steadystate behavior Figure : The position tracking error of the endeffector with k =and k =5.. Velocity tracking error (m/s) 4 Velocity tracking error (m/s) (a) The initial response (b) The steadystate behavior Figure 3: The velocity tracking error of the endeffector with k =and k =5. It should be stressed that the smaller the value of γ is, the better the disturbance attenuation capability will be obtained. To evaluate the L gain disturbance attenuation capability, the following index is defined: Additionally, because the desired trajectory x d,thecnn approximation error ε i, i=,, and the external disturbance are bounded, there will exist a positive constant Γ max such that Γ Γ max.usingtheinequalityh, it yields γ = y dt Γ dt. (3) From (3), it is known that smaller γ will lead to better disturbance attenuation performance. V y +γ Γ max λ z λ z +γ Γ max. (33)
6 6 Mathematical Problems in Engineering Physical name Space robot link Control gains The order of CNN The initial value of optimal weight matrix External disturbance Table : Simulations parameters. Value m =4, m =3, m =4, b =.5 a =.5, a =.5, b =.5, I = 6.667, I =.5, I =.333 k =, k =5, η =.5, η =.7, ξ =.3, ξ =.5, =. n=3 W() = 55 6 τ d =a[ sin(n c t) cos(n c t) 4 sin(n c t)] T n c =., a =. The estimate of the weight Then, one has lim t z γγ max /λ and lim t z γγ max /λ. It can thus obtain that z and z are bounded. More specifically, when Γ =,itfollowsthat z and z. As a result, the endeffector trajectory will asymptotically follow the desired trajectory. W e W e Figure 4: The estimate of the optimal weight matrix (Ŵ = W e and Ŵ = W e )withk =and k =5. 4. Numerical Example To test the proposed controller, a twolink space robot operating in a freefloating mode is numerically simulated. The trajectory tracking control for its endeffector is performed. The main physical parameters, control gains, and external disturbances are listed in Table. The desired trajectory is a circle in XY plane with its radius equal to m. 4.. Response by Using Different Control Gains. With application of the proposed approach, Figure shows that the controller successfully accomplishes the trajectory following mission of the space robot endeffector. As the position tracking error shown in Figure, good steadystate performance is guaranteed with minor overshoot. The velocity tracking error of endeffector is shown in Figure 3. Vibration with highfrequency is seen. That is induced by external disturbances. The corresponding estimates of the optimal weight matrix when using CNN to handle system uncertainties are illustratedinfigure4.itisgottoknowthatthoseestimatesof CNN are all bounded. As summarized in Theorem 4, the trajectory tracking performance is dependent on the control gains. Hence, simulation by using different control gains is further carried out. Figures 5, 6, and 7 show the trajectory tracking error by using k =, k =5; k =3, k =5;andk =5, k =5, respectively. From Figures 5 7, it is seen that larger value of k will lead to fast convergence rate of the tracking error. Figure 8 shows the control performance by using k =and k =5.Itisobtainedfromthoseresultsthatlargervalueofk cannot increase the response rate of the system when k has a fixed value. Therefore, to ensure that the actual trajectory of the endeffector can follow the desired trajectory in a faster rate, the designer should choose k, k to satisfy (7) and (), Figure 5: The position tracking error of the endeffector with k = and k =5. respectively. At this time, choosing larger k will result in that the desired trajectory will be followed in a shorter time. However, the maximum control effort of actuator should be takenintoaccountwhenchoosingk. 4.. Performance in the Absence of External Disturbances. In this case, an ideal condition is considered. That is, there are no external disturbances acting on the space robot. By using
7 Mathematical Problems in Engineering Figure 6: The position tracking error of the endeffector with k = 5 and k =5. Figure 8: The position tracking error of the endeffector with k =3 and k = Position in yaxis (m) Position in xaxis (m).5 Figure 7: The position tracking error of the endeffector with k = and k =5. Figure 9: The desired trajectory (solid line) and the actual trajectory (dashed line) of endeffector in the absence of external disturbances. 5. Conclusions the proposed control law, the control performance is shown in Figures 9. Those results demonstrate the conclusion in Theorem 4 that an asymptotic tracking can be guaranteed in the absence of external disturbances. Comparing Figures 4 withfigures, respectively, fewer overshoots are obtained in the absence of disturbances compared to those in the presence of external disturbances. The problem of endeffector trajectory tracking control was investigated for a space robot working in freefloating mode by incorporating the criterion of a tracking performance given by L gain constraint in controller synthesis. External disturbance and system uncertainties were addressed. The proposed adaptive control approach was able to achieve high tracking performance even in the presence of uncertain kinematics and dynamics. The closedloop tracking system
8 8 Mathematical Problems in Engineering (a) The initial response (b) The steadystate behavior Figure : The position tracking error of endeffector in the absence of external disturbances..3 Velocity tracking error (m/s) 4 Velocity tracking error (m/s) (a) The initial response (b) The steadystate behavior Figure : The velocity tracking error of endeffector in the absence of external disturbances. was ensured to be global uniform ultimate bounded stable with the L gain less than any given small level. Moreover, when the space robot was not under the effect of any disturbance, the desired trajectory can be asymptotically followed. It should be pointed out that actuators are assumed to run normally when implementing the proposed approach. However, this assumption may not be satisfied in practice. As one of future works, trajectory tracking control with fault tolerant capability should be carried out for space robot s endeffector. Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.
9 Mathematical Problems in Engineering 9 The estimate of the weight W e W e Figure : The estimate of the optimal weight matrix (Ŵ = W e and Ŵ = W e ) in the absence of external disturbances. Acknowledgments This work was supported partially by the National Natural Science Foundation of China (Project nos and 65737) and the Foundation of the National Key Laboratory of Science and Technology on Space Intelligent Control (Project no. 94C594C595). The authors highly appreciate the preceding financial supports. The authors wouldalsoliketothankthereviewersandtheeditorfortheir valuable comments and constructive suggestions that helped to improve the paper significantly. References [] A. FloresAbad, O. Ma, K. Pham, and S. Ulrich, A review of space robotics technologies for onorbit servicing, Progress in Aerospace Sciences, vol. 68, pp. 6, 4. [] W. Xu, B. Liang, and Y. Xu, Survey of modeling, planning, and ground verification of space robotic systems, Acta Astronautica,vol.68,no.,pp ,. [3] D.L.Akin,J.C.Lanef,B.J.Roberts,andS.R.Weisman, Robotic capabilities for complex space operations, in Proceedings of the AIAA Space Conference and Exposition, pp., Albuquerque, NM, USA, August. [4] L. M. Capisani, A. Ferrara, A. F. de Loza, and L. M. Fridman, Manipulator fault diagnosis via higher order slidingmode observers, IEEE Transactions on Industrial Electronics, vol. 59, no., pp ,. [5] S. Kim, A. Shukla, and A. Billard, Catching objects in flight, IEEE Transactions on Robotics, vol.3,no.5,pp.49 65, 4. [6] S. Kim and A. Billard, Estimating the nonlinear dynamics of freeflying objects, Robotics and Autonomous Systems, vol.6, no. 9, pp. 8,. [7]C.C.Cheah,S.Kawamura,andS.Arimoto, Feedbackcontrol for robotic manipulator with uncertain kinematics and dynamics, in Proceedings of the IEEE International Conference on Robotics and Automation, vol.4,pp ,Leuven, Belgium, May 998. [8] C. C. Cheah, M. Hirano, S. Kawamura, and S. Arimoto, Approximate Jacobian control for robots with uncertain kinematics and dynamics, IEEE Transactions on Robotics and Automation,vol.9,no.4,pp.69 7,3. [9] W. E. Dixon, Adaptive regulation of amplitude limited robot manipulators with uncertain kinematics and dynamics, in Proceedings of the American Control Conference (AAC 4), pp , Boston, Mass, USA, July 4. [] C. C. Cheah, C. Liu, and J. J. E. Slotine, Approximate Jacobian adaptive control for robot manipulators, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 4), vol. 3, pp , IEEE, New Orleans, La, USA, AprilMay 4. [] X. Liang, X. Huang, M. Wang, and X. Zeng, Adaptive taskspace tracking control of robots without taskspace and jointspacevelocity measurements, IEEE Transactions on Robotics, vol. 6, no. 4, pp ,. [] C. C. Cheah, C. Liu, and J. J. E. Slotine, Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models, IEEE Transactions on Automatic Control,vol.5,no.6,pp.4 9,6. [3] L. Cheng, Z.G. Hou, and M. Tan, Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model, Automatica, vol. 45, no., pp. 3 38, 9. [4] A. J. van der Schaft, L gain analysis of nonlinear systems and nonlinear statefeedback H control, IEEE Transactions on Automatic Control,vol.37,no.6,pp ,99. [5] M. Fujita, H. Kawai, and M. W. Spong, Passivitybased dynamic visual feedback control for threedimensional target tracking: stability and Lgain performance analysis, IEEE Transactions on Control Systems Technology,vol.5,no.,pp. 4 5, 7. [6] C. Ishii, T. Shen, and Z. Qu, Lyapunov recursive design of robust adaptive tracking control with L gain performance for electricallydriven robot manipulators, International Control,vol.74,no.8,pp.8 88,. [7] H. K. Khalil and J. Grizzle, Nonlinear Systems, Macmillan, New York, NY, USA, 99. [8] A. van der Schaft, L Gain and Passivity Techniques in Nonlinear Control, Communications and Control Engineering, Springer, London, UK, nd edition,. [9] A.M. Zou and K. D. Kumar, Adaptive attitude control of spacecraft without velocity measurements using Chebyshev neural network, Acta Astronautica, vol. 66, no. 56, pp ,.
10 Advances in Operations Research Volume 4 Advances in Decision Sciences Volume 4 Applied Mathematics Algebra Volume 4 Probability and Statistics Volume 4 The Scientific World Journal Volume 4 International Differential Equations Volume 4 Volume 4 Submit your manuscripts at International Advances in Combinatorics Mathematical Physics Volume 4 Complex Analysis Volume 4 International Mathematics and Mathematical Sciences Mathematical Problems in Engineering Mathematics Volume 4 Volume 4 Volume 4 Volume 4 Discrete Mathematics Volume 4 Discrete Dynamics in Nature and Society Function Spaces Abstract and Applied Analysis Volume 4 Volume 4 Volume 4 International Stochastic Analysis Optimization Volume 4 Volume 4
DesignSimulationOptimization Package for a Generic 6DOF Manipulator with a Spherical Wrist
DesignSimulationOptimization Package for a Generic 6DOF Manipulator with a Spherical Wrist MHER GRIGORIAN, TAREK SOBH Department of Computer Science and Engineering, U. of Bridgeport, USA ABSTRACT Robot
More informationAdaptive Control Using Combined Online and Background Learning Neural Network
Adaptive Control Using Combined Online and Background Learning Neural Network Eric N. Johnson and SeungMin Oh Abstract A new adaptive neural network (NN control concept is proposed with proof of stability
More informationForce/position control of a robotic system for transcranial magnetic stimulation
Force/position control of a robotic system for transcranial magnetic stimulation W.N. Wan Zakaria School of Mechanical and System Engineering Newcastle University Abstract To develop a force control scheme
More informationOperational Space Control for A Scara Robot
Operational Space Control for A Scara Robot Francisco Franco Obando D., Pablo Eduardo Caicedo R., Oscar Andrés Vivas A. Universidad del Cauca, {fobando, pacaicedo, avivas }@unicauca.edu.co Abstract This
More informationOptimization of PID parameters with an improved simplex PSO
Li et al. Journal of Inequalities and Applications (2015) 2015:325 DOI 10.1186/s1366001507852 R E S E A R C H Open Access Optimization of PID parameters with an improved simplex PSO Jimin Li 1, YeongCheng
More informationExample 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum. asin. k, a, and b. We study stability of the origin x
Lecture 4. LaSalle s Invariance Principle We begin with a motivating eample. Eample 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum Dynamics of a pendulum with friction can be written
More informationAdaptive Control for Robot Manipulators Under Ellipsoidal Task Space Constraints
22 IEEE/RSJ International Conference on Intelligent Robots and Systems October 72, 22. Vilamoura, Algarve, Portugal Adaptive Control for Robot Manipulators Under Ellipsoidal Task Space Constraints Keng
More informationA Control Scheme for Industrial Robots Using Artificial Neural Networks
A Control Scheme for Industrial Robots Using Artificial Neural Networks M. Dinary, AbouHashema M. ElSayed, Abdel Badie Sharkawy, and G. Abouelmagd unknown dynamical plant is investigated. A layered neural
More information4 Lyapunov Stability Theory
4 Lyapunov Stability Theory In this section we review the tools of Lyapunov stability theory. These tools will be used in the next section to analyze the stability properties of a robot controller. We
More informationCONTRIBUTIONS TO THE AUTOMATIC CONTROL OF AERIAL VEHICLES
1 / 23 CONTRIBUTIONS TO THE AUTOMATIC CONTROL OF AERIAL VEHICLES MINH DUC HUA 1 1 INRIA Sophia Antipolis, AROBAS team I3SCNRS Sophia Antipolis, CONDOR team Project ANR SCUAV Supervisors: Pascal MORIN,
More informationResearch Article Stability Analysis for HigherOrder Adjacent Derivative in Parametrized Vector Optimization
Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 2010, Article ID 510838, 15 pages doi:10.1155/2010/510838 Research Article Stability Analysis for HigherOrder Adjacent Derivative
More informationResearch Article The General Traveling Wave Solutions of the Fisher Equation with Degree Three
Advances in Mathematical Physics Volume 203, Article ID 65798, 5 pages http://dx.doi.org/0.55/203/65798 Research Article The General Traveling Wave Solutions of the Fisher Equation with Degree Three Wenjun
More informationMotion Control of 3 DegreeofFreedom DirectDrive Robot. Rutchanee Gullayanon
Motion Control of 3 DegreeofFreedom DirectDrive Robot A Thesis Presented to The Academic Faculty by Rutchanee Gullayanon In Partial Fulfillment of the Requirements for the Degree Master of Engineering
More informationIntelligent Mechatronic Model Reference Theory for Robot Endeffector
, pp.165172 http://dx.doi.org/10.14257/ijunesst.2015.8.1.15 Intelligent Mechatronic Model Reference Theory for Robot Endeffector Control Mohammad sadegh Dahideh, Mohammad Najafi, AliReza Zarei, Yaser
More informationLecture 7: Finding Lyapunov Functions 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.243j (Fall 2003): DYNAMICS OF NONLINEAR SYSTEMS by A. Megretski Lecture 7: Finding Lyapunov Functions 1
More informationResearch Article TwoPeriod Inventory Control with Manufacturing and Remanufacturing under Return Compensation Policy
Discrete Dynamics in Nature and Society Volume 2013, Article ID 871286, 8 pages http://dx.doi.org/10.1155/2013/871286 Research Article TwoPeriod Inventory Control with Manufacturing and Remanufacturing
More informationA linear algebraic method for pricing temporary life annuities
A linear algebraic method for pricing temporary life annuities P. Date (joint work with R. Mamon, L. Jalen and I.C. Wang) Department of Mathematical Sciences, Brunel University, London Outline Introduction
More informationSpacecraft Dynamics and Control. An Introduction
Brochure More information from http://www.researchandmarkets.com/reports/2328050/ Spacecraft Dynamics and Control. An Introduction Description: Provides the basics of spacecraft orbital dynamics plus attitude
More informationChapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network
Chapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network Qian Wu, Yahui Wang, Long Zhang and Li Shen Abstract Building electrical system fault diagnosis is the
More informationResearch Article Engineering Change Orders Design Using Multiple Variables Linear Programming for VLSI Design
VLSI Design, rticle ID 698041, 5 pages http://dx.doi.org/10.1155/2014/698041 Research rticle Engineering Change Orders Design Using Multiple Variables Linear Programming for VLSI Design YuCheng Fan, ChihKang
More informationOptimal PID Controller Design for AVR System
Tamkang Journal of Science and Engineering, Vol. 2, No. 3, pp. 259 270 (2009) 259 Optimal PID Controller Design for AVR System ChingChang Wong*, ShihAn Li and HouYi Wang Department of Electrical Engineering,
More informationOnline Tuning of Artificial Neural Networks for Induction Motor Control
Online Tuning of Artificial Neural Networks for Induction Motor Control A THESIS Submitted by RAMA KRISHNA MAYIRI (M060156EE) In partial fulfillment of the requirements for the award of the Degree of MASTER
More informationCONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVI  Fault Accomodation Using Model Predictive Methods  Jovan D. Bošković and Raman K.
FAULT ACCOMMODATION USING MODEL PREDICTIVE METHODS Scientific Systems Company, Inc., Woburn, Massachusetts, USA. Keywords: Fault accommodation, Model Predictive Control (MPC), Failure Detection, Identification
More informationIntroduction to Engineering System Dynamics
CHAPTER 0 Introduction to Engineering System Dynamics 0.1 INTRODUCTION The objective of an engineering analysis of a dynamic system is prediction of its behaviour or performance. Real dynamic systems are
More informationRobot TaskLevel Programming Language and Simulation
Robot TaskLevel Programming Language and Simulation M. Samaka Abstract This paper presents the development of a software application for Offline robot task programming and simulation. Such application
More informationControl Systems with Actuator Saturation
Control Systems with Actuator Saturation Analysis and Design Tingshu Hu Zongli Lin With 67 Figures Birkhauser Boston Basel Berlin Preface xiii 1 Introduction 1 1.1 Linear Systems with Actuator Saturation
More informationAvailable online at www.sciencedirect.com Available online at www.sciencedirect.com
Available online at www.sciencedirect.com Available online at www.sciencedirect.com Procedia Procedia Engineering Engineering () 9 () 6 Procedia Engineering www.elsevier.com/locate/procedia International
More informationEDUMECH Mechatronic Instructional Systems. Ball on Beam System
EDUMECH Mechatronic Instructional Systems Ball on Beam System Product of Shandor Motion Systems Written by Robert Hirsch Ph.D. 9989 All Rights Reserved. 999 Shandor Motion Systems, Ball on Beam Instructional
More informationAMIT K. SANYAL. 20012004 Ph.D. in Aerospace Engineering, University of Michigan, Ann Arbor, MI. Date of completion:
AMIT K. SANYAL Office Home 305 Holmes Hall 3029 Lowrey Avenue Mechanical Engineering Apartment # N2211 University of Hawaii at Manoa Honolulu, HI 96822 Honolulu, HI 96822 4806038938 8089562142 aksanyal@hawaii.edu
More informationACTUATOR DESIGN FOR ARC WELDING ROBOT
ACTUATOR DESIGN FOR ARC WELDING ROBOT 1 Anurag Verma, 2 M. M. Gor* 1 G.H Patel College of Engineering & Technology, V.V.Nagar388120, Gujarat, India 2 Parul Institute of Engineering & Technology, Limda391760,
More informationKinematics and Dynamics of Mechatronic Systems. Wojciech Lisowski. 1 An Introduction
Katedra Robotyki i Mechatroniki Akademia GórniczoHutnicza w Krakowie Kinematics and Dynamics of Mechatronic Systems Wojciech Lisowski 1 An Introduction KADOMS KRIM, WIMIR, AGH Kraków 1 The course contents:
More informationStability Analysis of Small Satellite Formation Flying and Reconfiguration Missions in Deep Space
Stability Analysis of Small Satellite Formation Flying and Reconfiguration Missions in Deep Space Saptarshi Bandyopadhyay, Chakravarthini M. Saaj, and Bijnan Bandyopadhyay, Member, IEEE Abstract Closeproximity
More informationConstraint satisfaction and global optimization in robotics
Constraint satisfaction and global optimization in robotics Arnold Neumaier Universität Wien and JeanPierre Merlet INRIA Sophia Antipolis 1 The design, validation, and use of robots poses a number of
More informationOn Motion of Robot EndEffector using the Curvature Theory of Timelike Ruled Surfaces with Timelike Directrix
Malaysian Journal of Mathematical Sciences 8(2): 89204 (204) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Journal homepage: http://einspem.upm.edu.my/journal On Motion of Robot EndEffector using the Curvature
More informationMixedÀ¾ Ð½Optimization Problem via Lagrange Multiplier Theory
MixedÀ¾ Ð½Optimization Problem via Lagrange Multiplier Theory Jun WuÝ, Sheng ChenÞand Jian ChuÝ ÝNational Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University,
More informationThe dynamic equation for the angular motion of the wheel is R w F t R w F w ]/ J w
Chapter 4 Vehicle Dynamics 4.. Introduction In order to design a controller, a good representative model of the system is needed. A vehicle mathematical model, which is appropriate for both acceleration
More informationINPUTTOSTATE STABILITY FOR DISCRETETIME NONLINEAR SYSTEMS
INPUTTOSTATE STABILITY FOR DISCRETETIME NONLINEAR SYSTEMS ZhongPing Jiang Eduardo Sontag,1 Yuan Wang,2 Department of Electrical Engineering, Polytechnic University, Six Metrotech Center, Brooklyn,
More informationRealTime Systems Versus CyberPhysical Systems: Where is the Difference?
RealTime Systems Versus CyberPhysical Systems: Where is the Difference? Samarjit Chakraborty www.rcs.ei.tum.de TU Munich, Germany Joint work with Dip Goswami*, Reinhard Schneider #, Alejandro Masrur
More informationINSTRUCTOR WORKBOOK Quanser Robotics Package for Education for MATLAB /Simulink Users
INSTRUCTOR WORKBOOK for MATLAB /Simulink Users Developed by: Amir Haddadi, Ph.D., Quanser Peter Martin, M.A.SC., Quanser Quanser educational solutions are powered by: CAPTIVATE. MOTIVATE. GRADUATE. PREFACE
More informationCONTROLLABILITY. Chapter 2. 2.1 Reachable Set and Controllability. Suppose we have a linear system described by the state equation
Chapter 2 CONTROLLABILITY 2 Reachable Set and Controllability Suppose we have a linear system described by the state equation ẋ Ax + Bu (2) x() x Consider the following problem For a given vector x in
More informationSimulation of Trajectories and Comparison of Joint Variables for Robotic Manipulator Using Multibody Dynamics (MBD)
Simulation of Trajectories and Comparison of Joint Variables for Robotic Manipulator Using Multibody Dynamics (MBD) Jatin Dave Assistant Professor Nirma University Mechanical Engineering Department, Institute
More informationA New Natureinspired Algorithm for Load Balancing
A New Natureinspired Algorithm for Load Balancing Xiang Feng East China University of Science and Technology Shanghai, China 200237 Email: xfeng{@ecusteducn, @cshkuhk} Francis CM Lau The University of
More informationSYSTEMS, CONTROL AND MECHATRONICS
2015 Master s programme SYSTEMS, CONTROL AND MECHATRONICS INTRODUCTION Technical, be they small consumer or medical devices or large production processes, increasingly employ electronics and computers
More informationDURING automatic vehicle following, the control objective
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 48, NO. 1, JANUARY 1999 319 Autonomous Intelligent Cruise Control Using Front and Back Information for Tight Vehicle Following Maneuvers Y. Zhang, Elias
More informationBilateral Teleoperation over the Internet: the Time Varying Delay Problem 1
Bilateral Teleoperation over the Internet: the Time Varying Delay Problem 1 Nikhil Chopra and Mark W. Spong Coordinated Science Laboratory University of Illinois at UrbanaChampaign 14 S. Mathews Avenue
More informationMetrics on SO(3) and Inverse Kinematics
Mathematical Foundations of Computer Graphics and Vision Metrics on SO(3) and Inverse Kinematics Luca Ballan Institute of Visual Computing Optimization on Manifolds Descent approach d is a ascent direction
More informationQuasistatic evolution and congested transport
Quasistatic evolution and congested transport Inwon Kim Joint with Damon Alexander, Katy Craig and Yao Yao UCLA, UW Madison Hard congestion in crowd motion The following crowd motion model is proposed
More informationPrincipal Rotation Representations of Proper NxN Orthogonal Matrices
Principal Rotation Representations of Proper NxN Orthogonal Matrices Hanspeter Schaub Panagiotis siotras John L. Junkins Abstract hree and four parameter representations of x orthogonal matrices are extended
More informationSampleddata iterative learning control for nonlinear systems with arbitrary relative degree
Automatica 37 (2001) 283}289 Brief Paper Sampleddata iterative learning control for nonlinear systems with arbitrary relative degree Mingxuan Sun, Danwei Wang* School of Electrical and Electronic Engineering,
More informationOptimal Design of αβ(γ) Filters
Optimal Design of (γ) Filters Dirk Tenne Tarunraj Singh, Center for Multisource Information Fusion State University of New York at Buffalo Buffalo, NY 426 Abstract Optimal sets of the smoothing parameter
More informationModel based Inversion Technique of GMR signals using ElementFree. Galerkin Method
Model based Inversion echnique of GMR signals using ElementFree Galerin Method Xin Liu 1, Yiming Deng 1, Zhiwei Zeng 1, Lalita Udpa 1, and Jeremy S. Knopp 2 1 Department of Electrical and Computer Engineering
More information1 Norms and Vector Spaces
008.10.07.01 1 Norms and Vector Spaces Suppose we have a complex vector space V. A norm is a function f : V R which satisfies (i) f(x) 0 for all x V (ii) f(x + y) f(x) + f(y) for all x,y V (iii) f(λx)
More informationSensorymotor control scheme based on Kohonen Maps and AVITE model
Sensorymotor control scheme based on Kohonen Maps and AVITE model Juan L. PedreñoMolina, Antonio GuerreroGonzález, Oscar A. FlorezGiraldo, J. MolinaVilaplana Technical University of Cartagena Department
More informationNonlinear Model Predictive Control of Hammerstein and Wiener Models Using Genetic Algorithms
Nonlinear Model Predictive Control of Hammerstein and Wiener Models Using Genetic Algorithms AlDuwaish H. and Naeem, Wasif Electrical Engineering Department/King Fahd University of Petroleum and Minerals
More informationThe Research on Demand Forecasting of Supply Chain Based on ICCELMAN
505 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 46, 2015 Guest Editors: Peiyu Ren, Yancang Li, Huiping Song Copyright 2015, AIDIC Servizi S.r.l., ISBN 9788895608372; ISSN 22839216 The
More informationPID, LQR and LQRPID on a Quadcopter Platform
PID, LQR and LQRPID on a Quadcopter Platform Lucas M. Argentim unielargentim@fei.edu.br Willian C. Rezende uniewrezende@fei.edu.br Paulo E. Santos psantos@fei.edu.br Renato A. Aguiar preaguiar@fei.edu.br
More informationAnalysis of Mobile Phone Reliability Based on Active Disassembly Using Smart Materials *
Journal of Surface Engineered Materials and Advanced Technology, 2011, 1, 8087 doi:10.4236/jsemat.2011.12012 Published Online July 2011 (http://www.scirp.org/journal/jsemat) Analysis of Mobile Phone Reliability
More informationTD(0) Leads to Better Policies than Approximate Value Iteration
TD(0) Leads to Better Policies than Approximate Value Iteration Benjamin Van Roy Management Science and Engineering and Electrical Engineering Stanford University Stanford, CA 94305 bvr@stanford.edu Abstract
More informationIntelligent Submersible ManipulatorRobot, Design, Modeling, Simulation and Motion Optimization for Maritime Robotic Research
20th International Congress on Modelling and Simulation, Adelaide, Australia, 1 6 December 2013 www.mssanz.org.au/modsim2013 Intelligent Submersible ManipulatorRobot, Design, Modeling, Simulation and
More information(57) (58) (59) (60) (61)
Module 4 : Solving Linear Algebraic Equations Section 5 : Iterative Solution Techniques 5 Iterative Solution Techniques By this approach, we start with some initial guess solution, say for solution and
More informationα = u v. In other words, Orthogonal Projection
Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v
More informationA Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks
A Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks H. T. Kung Dario Vlah {htk, dario}@eecs.harvard.edu Harvard School of Engineering and Applied Sciences
More informationA QUICK GUIDE TO THE FORMULAS OF MULTIVARIABLE CALCULUS
A QUIK GUIDE TO THE FOMULAS OF MULTIVAIABLE ALULUS ontents 1. Analytic Geometry 2 1.1. Definition of a Vector 2 1.2. Scalar Product 2 1.3. Properties of the Scalar Product 2 1.4. Length and Unit Vectors
More informationNEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS
NEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS TEST DESIGN AND FRAMEWORK September 2014 Authorized for Distribution by the New York State Education Department This test design and framework document
More informationA Novel Adaptive SuperTwisting Sliding Mode Controller with a Single InputSingle Output Fuzzy Logic Control based Moving Sliding Surface
A Novel Adaptive SuperTwisting Sliding Mode Controller with a Single InputSingle Output Fuzzy Logic Control based Moving Sliding Surface Abdul Kareem and Mohammad Fazle Azeem 1 Research Scholar, E&C,
More informationRobotics and Automation Blueprint
Robotics and Automation Blueprint This Blueprint contains the subject matter content of this Skill Connect Assessment. This Blueprint does NOT contain the information one would need to fully prepare for
More informationOn using numerical algebraic geometry to find Lyapunov functions of polynomial dynamical systems
Dynamics at the Horsetooth Volume 2, 2010. On using numerical algebraic geometry to find Lyapunov functions of polynomial dynamical systems Eric Hanson Department of Mathematics Colorado State University
More informationEnhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm
1 Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm Hani Mehrpouyan, Student Member, IEEE, Department of Electrical and Computer Engineering Queen s University, Kingston, Ontario,
More informationDynamics. Basilio Bona. DAUINPolitecnico di Torino. Basilio Bona (DAUINPolitecnico di Torino) Dynamics 2009 1 / 30
Dynamics Basilio Bona DAUINPolitecnico di Torino 2009 Basilio Bona (DAUINPolitecnico di Torino) Dynamics 2009 1 / 30 Dynamics  Introduction In order to determine the dynamics of a manipulator, it is
More informationTHERE is a significant amount of current research activity
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 14, NO. 5, SEPTEMBER 2006 789 Stable Task Load Balancing Strategies for Cooperative Control of Networked Autonomous Air Vehicles Jorge Finke, Member,
More informationLecture 13 Linear quadratic Lyapunov theory
EE363 Winter 289 Lecture 13 Linear quadratic Lyapunov theory the Lyapunov equation Lyapunov stability conditions the Lyapunov operator and integral evaluating quadratic integrals analysis of ARE discretetime
More informationA Reliability Point and Kalman Filterbased Vehicle Tracking Technique
A Reliability Point and Kalman Filterbased Vehicle Tracing Technique Soo Siang Teoh and Thomas Bräunl Abstract This paper introduces a technique for tracing the movement of vehicles in consecutive video
More informationIntegration of a fin experiment into the undergraduate heat transfer laboratory
Integration of a fin experiment into the undergraduate heat transfer laboratory H. I. AbuMulaweh Mechanical Engineering Department, Purdue University at Fort Wayne, Fort Wayne, IN 46805, USA Email: mulaweh@engr.ipfw.edu
More informationVisualization of General Defined Space Data
International Journal of Computer Graphics & Animation (IJCGA) Vol.3, No.4, October 013 Visualization of General Defined Space Data John R Rankin La Trobe University, Australia Abstract A new algorithm
More informationA Direct Numerical Method for Observability Analysis
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 15, NO 2, MAY 2000 625 A Direct Numerical Method for Observability Analysis Bei Gou and Ali Abur, Senior Member, IEEE Abstract This paper presents an algebraic method
More informationFuzzy Differential Systems and the New Concept of Stability
Nonlinear Dynamics and Systems Theory, 1(2) (2001) 111 119 Fuzzy Differential Systems and the New Concept of Stability V. Lakshmikantham 1 and S. Leela 2 1 Department of Mathematical Sciences, Florida
More informationAn AgentBased Concept for Problem Management Systems to Enhance Reliability
An AgentBased Concept for Problem Management Systems to Enhance Reliability H. Wang, N. Jazdi, P. Goehner A defective component in an industrial automation system affects only a limited number of sub
More informationResearch Article Adaptive Human Behavior in a TwoWorm Interaction Model
Discrete Dynamics in ature and Society Volume 22, Article ID 828246, 3 pages doi:.55/22/828246 Research Article Adaptive Human Behavior in a TwoWorm Interaction Model LiPeng Song,, 2 Xie Han,, 2 DongMing
More informationWireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm
, pp. 99108 http://dx.doi.org/10.1457/ijfgcn.015.8.1.11 Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm Wang DaWei and Wang Changliang Zhejiang Industry Polytechnic College
More informationA MONTE CARLO DISPERSION ANALYSIS OF A ROCKET FLIGHT SIMULATION SOFTWARE
A MONTE CARLO DISPERSION ANALYSIS OF A ROCKET FLIGHT SIMULATION SOFTWARE F. SAGHAFI, M. KHALILIDELSHAD Department of Aerospace Engineering Sharif University of Technology Email: saghafi@sharif.edu Tel/Fax:
More informationSOLID MECHANICS TUTORIAL MECHANISMS KINEMATICS  VELOCITY AND ACCELERATION DIAGRAMS
SOLID MECHANICS TUTORIAL MECHANISMS KINEMATICS  VELOCITY AND ACCELERATION DIAGRAMS This work covers elements of the syllabus for the Engineering Council exams C105 Mechanical and Structural Engineering
More informationSolving Nonlinear Equations Using Recurrent Neural Networks
Solving Nonlinear Equations Using Recurrent Neural Networks Karl Mathia and Richard Saeks, Ph.D. Accurate Automation Corporation 71 Shallowford Road Chattanooga, Tennessee 37421 Abstract A class of recurrent
More informationDYNAMIC INVERSION (DI) is a similar concept as
1390 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 18, NO. 6, NOVEMBER 2010 Global Adaptive Output Feedback Tracking Control of an Unmanned Aerial Vehicle W. MacKunis, Z. D. Wilcox, M. K. Kaiser,
More informationComputing divisors and common multiples of quasilinear ordinary differential equations
Computing divisors and common multiples of quasilinear ordinary differential equations Dima Grigoriev CNRS, Mathématiques, Université de Lille Villeneuve d Ascq, 59655, France Dmitry.Grigoryev@math.univlille1.fr
More informationFault Monitoring of the Electric Machine in a Hybrid Vehicle
Fault Monitoring of the Electric Machine in a Hybrid Vehicle Christofer Sundström, Erik Frisk, and Lars Nielsen Vehicular Systems, Dept. of Electrical Engineering, Linköping University, SE581 83 Linköping,
More informationStabilizing a Gimbal Platform using SelfTuning Fuzzy PID Controller
Stabilizing a Gimbal Platform using SelfTuning Fuzzy PID Controller Nourallah Ghaeminezhad Collage Of Automation Engineering Nuaa Nanjing China Wang Daobo Collage Of Automation Engineering Nuaa Nanjing
More informationAn Introduction to Applied Mathematics: An Iterative Process
An Introduction to Applied Mathematics: An Iterative Process Applied mathematics seeks to make predictions about some topic such as weather prediction, future value of an investment, the speed of a falling
More informationMultiRobot Tracking of a Moving Object Using Directional Sensors
MultiRobot Tracking of a Moving Object Using Directional Sensors Manuel Mazo Jr., Alberto Speranzon, Karl H. Johansson Dept. of Signals, Sensors & Systems Royal Institute of Technology SE 44 Stockholm,
More informationResearch Article Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining
Electrical and Computer Engineering Volume 25, Article ID 357378, 7 pages http://dx.doi.org/.55/25/357378 Research Article Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application
More informationReliability Guarantees in Automata Based Scheduling for Embedded Control Software
1 Reliability Guarantees in Automata Based Scheduling for Embedded Control Software Santhosh Prabhu, Aritra Hazra, Pallab Dasgupta Department of CSE, IIT Kharagpur West Bengal, India  721302. Email: {santhosh.prabhu,
More informationAN INTRODUCTION TO NUMERICAL METHODS AND ANALYSIS
AN INTRODUCTION TO NUMERICAL METHODS AND ANALYSIS Revised Edition James Epperson Mathematical Reviews BICENTENNIAL 0, 1 8 0 7 z ewiley wu 2007 r71 BICENTENNIAL WILEYINTERSCIENCE A John Wiley & Sons, Inc.,
More informationA Passivity Measure Of Systems In Cascade Based On Passivity Indices
49th IEEE Conference on Decision and Control December 57, Hilton Atlanta Hotel, Atlanta, GA, USA A Passivity Measure Of Systems In Cascade Based On Passivity Indices Han Yu and Panos J Antsaklis Abstract
More informationModern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh
Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh Peter Richtárik Week 3 Randomized Coordinate Descent With Arbitrary Sampling January 27, 2016 1 / 30 The Problem
More informationAn interval linear programming contractor
An interval linear programming contractor Introduction Milan Hladík Abstract. We consider linear programming with interval data. One of the most challenging problems in this topic is to determine or tight
More informationFlatness based Control of a Gantry Crane
9th IFAC Symposium on Nonlinear Control Systems Toulouse, France, September 46, 213 ThB2.5 Flatness based Control of a Gantry Crane Bernd Kolar Kurt Schlacher Institute of Automatic Control Control Systems
More informationDimension Theory for Ordinary Differential Equations
Vladimir A. Boichenko, Gennadij A. Leonov, Volker Reitmann Dimension Theory for Ordinary Differential Equations Teubner Contents Singular values, exterior calculus and Lozinskiinorms 15 1 Singular values
More informationA Health Degree Evaluation Algorithm for Equipment Based on Fuzzy Sets and the Improved SVM
Journal of Computational Information Systems 10: 17 (2014) 7629 7635 Available at http://www.jofcis.com A Health Degree Evaluation Algorithm for Equipment Based on Fuzzy Sets and the Improved SVM Tian
More informationTHE problem of visual servoing guiding a robot using
582 IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 13, NO. 4, AUGUST 1997 A Modular System for Robust Positioning Using Feedback from Stereo Vision Gregory D. Hager, Member, IEEE Abstract This paper
More informationMathematical Modeling and Dynamic Simulation of a Class of Drive Systems with Permanent Magnet Synchronous Motors
Applied and Computational Mechanics 3 (2009) 331 338 Mathematical Modeling and Dynamic Simulation of a Class of Drive Systems with Permanent Magnet Synchronous Motors M. Mikhov a, a Faculty of Automatics,
More informationModel Predictive Control Lecture 5
Model Predictive Control Lecture 5 Klaus Trangbæk ktr@es.aau.dk Automation & Control Aalborg University Denmark. http://www.es.aau.dk/staff/ktr/mpckursus/mpckursus.html mpc5 p. 1 Exercise from last time
More information