Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop & Go

Transcription

1 Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop & Go Chien-Tzu Chen 1, Bo-Ruei Chen 2,Ching-Chih Tsai 3 1,2 Automotive Research & Testing Center (ARTC), Changhwa, R.O.C. 3 Department of Electrical Engineering, National Chung-Hsing University, Taichung, R.O.C 1 Tel: EXT2403, 1 Fax: , 1 ook@artc.org.tw ABSTRACT This paper presents a fuzzy longitudinal control system with car-following speed ranging from 0 to 120 km/h. This controller achieves the main functions of both adaptive cruise control (ACC) and Stop & Go control. For being suitable for aftermarket, we use vacuum boosters to control the throttle and the braking pedal, thus circumventing the technical difficulties of using engine management system and anti-brake system. Several car following experiments are conducted to show that the proposed fuzzy longitudinal controller is capable of achieving the requirements of comfort and safety, and giving a satisfactory performance at high and low speed conditions. KEYWORDS adaptive cruise control (ACC), Stop &Go, fuzzy, spacing control, driving assistance systems, longitudinal control INTRODUCTION Recently, adaptive cruise control (ACC) [1], [2], [3], [4] and Stop&Go [5], [6], [7] have been two of the most popular automatic speed control research topics in both the automotive industry and academia, especially in the intelligent transportation systems field. ACC and Stop&Go systems are designed to reduce driver load and fatigue in heavy traffic, thus improving driver safety and comfort. To date, ACC systems have been in market as an optional device for luxury vehicles, and such systems have been developed for highway driving assistance only at speeds above 40 km/h, whereas Stop&Go systems are employed to handle urban traffic situations at low speeds under 40 km/h. In particular, Stop&Go systems must deal with the following two difficulties encountered in ACC systems. One is sensing difficulty in complex urban; the front vehicle situation of cut-in and lane change are more frequency, and the recognition pedestrian must be completely considered. The other is the frequent switching between acceleration and deceleration, and the smooth control for the driving stability and comfort at low speeds. The commercial systems [8] achieve vehicle longitudinal dynamics control via integration engine management system (EMS) and anti-lock braking system (ABS). Considering the requirement of aftermarket, we design a new type of by-pass actuator to control the vehicle longitudinal dynamics of acceleration and deceleration. Acceleration control is not special technique by electronic throttle control. Deceleration Control for braking is typically performed using a smart booster [9], [10], [11] besides using ABS modulator. The aim of the paper is to develop a fuzzy longitudinal controller [12], [13] and investigate its feasibility and efficacy through several experiments. The contributions of the paper are fourfold. First, this vehicular longitudinal control design is accomplished by using vacuum boosters to control the opening of the throttle and the position of the braking pedal. Worthy 1

2 of mention is that this design does not involve with the complicated and sophisticated knowledge of the engine management system and the anti-brake system. Second, a single fuzzy longitudinal controller is designed to carry out three control functions, including conventional cruise control, ACC and Stop&Go driving. Third, the parameters tuning of the fuzzy controller are done by employing virtual car-following simulation. This kind of virtual experiment is safe and effective in tuning the controller s parameters in an off-line manner. Finally, high-speed ACC driving and low-speed Stop&Go control are easily accomplished by the proposed fuzzy control method. In addition, through experimental results, the proposed fuzzy control method together with the developed control system has been shown effective and useful in not only achieving high-speed ACC control and low-speed Stop&Go control, but also fulfilling conventional cruise control. SYSTEM ARCHITECTURE AND DESCRIPTION Description of the Overall System Structure This subsection is devoted to describing the overall system structure of the proposed fuzzy longitudinal control system. Figure 1 shows the overall system architecture of the proposed control system. Four sensors, including the Lidar, the ABS speed sensor, the throttle position sensor, and brake position sensor, are used to provide all the necessary signals for the control system. The data recorder is responsible for collecting the signals from the four sensors. The data recorder is connected via Ethernet to the first computer, called Computer A, which monitors all the signals in real time. For the purpose of real-time feedback control, the two signals obtained from the Lidar and the ABS speed sensor are directly inputted to the high-level controller, MicroAutoBox 1507 made by dspace, thus generating the desired PWM commands to drive the two vacuum boosters. The loop refresh rate (sampling) is performed once Figure 1 - Overall system structure every 1/100 of a second. The second computer, named Computer B, is adopted to write and edit the control codes of the proposed control laws for the dspace high-level controller, tune the controller s parameters, and acquire and display the real-time command information via the optical fiber network. The main actuators are made by the two vacuum boosters, offering the driving and braking forces for the experimental platform. 2

3 Throttle and Braking Actuators The two vacuum boosters drive the braking pedal and gas pedal. Figure 2 depicts the cross-sectional view of it. The pressure difference between the two sides of the piston produces a force to pull the gas or braking pedal through the steel rope. The pressure at the upper side of the piston is almost 1 atm because this air room is directly connected to the air. The pressure at the lower side of the piston can be regulated by exciting the three coils in the three electromagnetic valves. The electromagnetic valve B is used to adjust the opening of Figure 2 - Cross-sectional view of the vacuum booster the valve by its PWM commands, thus reducing the pressure of the chamber inside the piston. Both two electromagnetic valves A and C are utilized to increase the pressure of the chamber inside the piston. To simply the control of the vacuum booster, we let the valve A be always closed and control the valve C by using PWM commands. For the sake of simple control, the two valves C and B are controlled by using the same PWM commands. FUZZY LONGITUDINAL CONTROLLER DESIGN Controller System This subsection presents the proposed longitudinal control method. Figure 3 describes the block diagram of the fuzzy longitudinal control system. In Figure 3 the outputs of the longitudinal controller are the distance error and the relative speed between the host and preceding cars, which are the inputs of the fuzzy longitudinal controller. The relative speed V is calculated from the speed difference of the preceding and the host cars, i.e., V = V p V h (1) where V p and V h respectively denote the speed of the preceding car and the speed of the host car. The distance error d is defined by subtracting the relative distance from the safety distance, i.e., d = D s D r (2) where D s and D r denote the safety distance and the relative distance, respectively. It is worthwhile to note that the relative distance is measured by the lidar and the safety distance is obtained from the product of the desired time gap and the speed of the preceding car plus the safety factor, that is, D = V * t f (3) s p g + where g t and f denote the time gap and the safety factor, respectively. The safety factor means the offset distance to avoid car collision if the speed of the preceding vehicle becomes 3

4 zero. In doing so, we set the sufficiently safe distance of 2 meters in this experiment. After executing the fuzzy control rules specified by the designer, the fuzzy longitudinal controller will generate the actual control signal u. Note that the control signal u is in the interval [-100%, 100%]. If the control signal u is positive, then the control u is transformed into a corresponding PWM command and the gas pedal actuator will be activated according the PWM command. Conversely, if the control signal u is negative, then the braking pedal actuator will provide the braking action, based on the braking PWM signal which is proportional to the absolute magnitude of the control u. Based on the PWM command, the gas or braking pedal actuator will produce a pull force to control the opening of the throttle and the position of the braking pedal, hereby accelerating or decelerating the host car and eventually obtaining the desired speed. In case of no preceding cars, the fuzzy longitudinal control will work in the cruise control mode. In the cruise control, the fuzzy longitudinal controller is used to achieve precise constant speed tracking and speed profile tracking. However, the physical quantities, including the safety distance D S, the relative speed V and the distance error d, must be modified as follows. D = V t f (4) S t g + V = V t V h (5) t d = Ds V dτ (6) 0 where V t is the target speed specified by the user. Figure 3 - Block diagram of the fuzzy longitudinal control system Fuzzy Controller Design The membership functions of the linguistic variables for the two inputs and the one output can be adjusted so as to achieve satisfactory accelerations and decelerations of the host car. The appropriate selections of the membership functions of the two inputs will provide better drivers comfort. For example, as Figure 4 shows, the positive real numbers a, b and c are key parameters of the two input variables and the one output variable, respectively. For fixed c, if the parameter a of the relative speed becomes small, then the host car will generate large accelerations and decelerations, thereby causing the driver to feel discomfort. Conversely, for fixed c if the parameter a turns out large, then the host car will have gentle or small accelerations and decelerations and the driver will feel no discomfort. The situations also hold for the distance error, but the change of the parameter a of the relative speed gives more effective tuning. In addition, for the two fixed parameters a and b, if the parameter c turns out large, then the host car will provide large accelerations and decelerations; if the parameter c becomes small, then small accelerations and decelerations are generated. However, the 4

5 smaller c will result in that the car can not reach the desired maximum opening of the throttle and maximum position of braking. Hence, in normal parameter tuning process, the designer is suggested to tune the parameter c first and then to tune the parameters a and b. Note that the appropriate parameters a, b and c can be experimentally determined so that the desired accelerations and decelerations are achieved. Figure 4 - Fuzzy sets and the rule matrix for the fuzzy longitudinal controller EXPERIMENTAL RESULTS AND DISCUSSION Parameter Tuning of the Fuzzy Longitudinal Controller Using Virtual Simulations This section aims at presenting the experimental method to tune the parameters of the output membership functions and the weighting factors of the input fuzzy linguistic variables. These parameters and weighting factors are determined using virtual simulations. Virtual simulations are performed in the following two steps. First, two time-series speed data were collected which simulated ACC and Stop&Go control driving conditions. These data were used to mimic the speeds of the preceding vehicle, thus calculating virtual lidar signals and virtual relative speeds which are the two inputs of the fuzzy longitudinal controller. Second, we proceeded with both ACC and Stop&Go control experiments using our own unique host car. The data concerning with the speeds of the host car, the relative distances, the positions of the braking pedal and the opening of the throttle were adopted to tune the parameters of the output membership functions and the weighting factors of the input fuzzy linguistic variables. This procedure was repeated until the satisfactory car-following results were obtained. Afterwards, the well-tuned parameters and weighting factors will be used for implementing the proposed all-speed fuzzy longitudinal controller. In our experiments, the tuned parameters a, b and c were given by a=30, b=10, and c=100. Conventional Cruise Control Experiments Figures 5 and 6 respectively depict the performance of the proposed fuzzy longitudinal controller for conventional cruise control at 80 km/h. These experimental results indicate that the proposed fuzzy longitudinal controller is shown capable of achieving conventional cruise control and speed commands profile tracking. Worthy of mention is that the result in Figure 5 (c) showed that the braking pedal was not activated for accomplishing the constant speed cruise control, but in Figure 6 (c) the throttle complemented the braking actuation for carrying out the constant command profile tracking. This fact indicates that the proposed controller behaved like a human driver. 5

6 Speed (km/h) Target Host Vehicle Speed Position of Braking (a) (b) (c) Figure 5 - The performance of speed tracking for the step input of 80 km/h. (a) The speed of the host car and the desired speed. (b) The opening of the throttle. (c) The position of the braking pedal Speed (km/h) Host Vehicle Speed Target Position of Braking pedal (a) (b) (c) Figure 6 - The performance of the proposed fuzzy longitudinal controller for conventional cruise control. (a) The speed of the host car and the desired speed. (b) The opening of the throttle. (c) The position of the braking pedal Adaptive Cruise Control Experiments Figure 7 shows the performance of the proposed longitudinal controller with the tuned parameters and weighting factors. Figure 7 (a) compares the speeds of the host car and the virtually preceding car. The result showed that both the speeds of the host and the preceding car were almost the same, indicating that the host car could follow the preceding car with allowable speed errors. The result in Figure 7 (b) reveals that the acceleration and deceleration of the host car are less than 300 mg, satisfying the safety criteria. In addition, as Figure 7 (c) shows, the host car safely followed the preceding car because the actual time gaps between the host car and the virtually preceding car were almost larger than two seconds, except at the moments when the preceding car took an abrupt deceleration. Through the results shown in Figure 7 (d) and (e), the proposed longitudinal controller has been shown useful in automatically driving the host care because the use of the opening of the throttle and the position of the braking pedal is very close to the driving strategy of human drivers. Worthy of mention is that the braking pedal was activated only during the deceleration period of the preceding car. Preceding Vehicle Speed (km/h) Acceleration (g) Time Gaps (s) Host Vehicle ( a) (b) 6 (c)

7 (d) Figure 7 - Performance of the fuzzy longitudinal controller for ACC car-following experiment. (a) The speed of the host car and the preceding car. (b) The acceleration and deceleration of the host car. (c) Time gaps between the host car and the preceding car. (d) The opening of the throttle. (e) The position of the braking pedal (e) Stop&Go Experiments In the following, the proposed controller was applied to steer the host car in achieving the goal of Stop&Go. The Stop & Go experiment was setup as follows. The preceding car moved in the speed of 20 km/h and it was far from the host car. The host car moved forward with a speed of 50 km/h. In such a way, the relative distances between two cars were gradually reduced and the proposed fuzzy longitudinal controller started to work. At the same time, the preceding car took actions of acceleration and deceleration, thus providing the time-varying speed profiles as shown in Figure 8(a). Figure 8(a) shows that the host car tracked the preceding car with small speed errors, and Figure 8(b) depicts time histories of the acceleration and deceleration of the host car. The result in Figure 8(b) indicates that the acceleration and deceleration of the proposed fuzzy longitudinal controllers were under 300 mg, i.e., the controller generated gentle acceleration and deceleration for safety reason. Moreover, the result in Figure 8(c) clearly indicates that the host car followed the preceding car at safe distances, and the time gaps between the two cars were greater than 3 seconds. As can be observed in Figure 8(d) and (e), the throttle worked with the braking pedal in a human driving manner, that is, the throttle complemented the braking actuation so that the proposed fuzzy longitudinal controller performed well in keeping safe inter-vehicle time gap. Host Vehicle Speed (km/h) Preceding Vehicle Acceleration (g) Time Gaps (s) (a) (b) (c) Position of Braking pedal (d) 7 (e)

8 Figure 8 - Experimental results of the proposed fuzzy longitudinal controller for Stop&Go car-following experiment. (a) Speeds of the host car and the preceding car. (b) Acceleration of the host car. (c) Actual Time Gaps between the host car and the preceding car. (d) Position of the throttle of the host car. (e) Position of the braking pedal of the host car. CONCLUSIONS This paper has presented a fuzzy longitudinal controller to achieve adaptive cruise control and Stop&Go driving. It is worthwhile to note that this new design does not change or modify the original engine management system (EMS) and anti-brake system (ABS), but inserts additional components to achieve vehicular longitudinal control. Acknowledgements This work had been supported by Department of Industrial Technology that is a department under Ministry of Economic Affairs, Taiwan, the Republic of China. REFERENCES [1] F. Sanchez, M. Seguer, A. Freixa, P. Andreas, K. Sochaski and R. Holze, From Adaptive Cruise Control to Active Safety Systems, SAE Technical Paper, no , [2] W. Prestl, T. Sauer, J. Steinle and O. Tshchernoster, The BMW Active Cruise Control ACC, SAE Technical Paper, no , [3] J. Wang and R. Rajamani, Should Adaptive Cruise-Control Systems be Designed to Maintain a Constant Time Gap Between Vehicles? IEEE, vol. 53, no. 5, pp , Sept [4] W. D. Jones, Keeping Cars, IEEE, vol. 38, no. 9, pp , Sept [5] J. C. Gerdes, J. K.and Hedrick, Vehicle speed and spacing control via coordinated throttle and brake actuation, Control Eng. Pract., vol. 5, no.11, pp , [6] M. Person, F. Botling, E. Hesslow, and R. Johnsson, Stop&Go Controller for Adaptive Cruise Control, Proc. of IEEE International Conference on Control Applications, Kohala Coast Island of Hawaii, Hawaii, USA, pp ,1999. [7] P. Venhovens, K. Naab and B. Adiprasito, Stop and go cruise control, Int. J. Automot. Technol., vol. 1, no.2, pp , [8] Adaptive Cruise Control, [9] B. Riley, G. Kuo, B. Schwartz and J. Zumberge, Development of a Controlled Braking Strategy For Vehicle Adaptive Cruise Control, SAE Technical Paper, no , [10] T. lijima, A. Higashimata, S. Tange, K. Mizoguchi, H, Kamiyama, K. Iwasaki and K. Egawa, Development of an Adaptive Cruise Control System with Brake Actuation, SAE Technical Paper, no , [11] D. littlejohn, T. Fornari, G. Kuo, B. Fulmmer, A. Mooradian, K. Shipp, J. Elliott, and K. Lee, Performance, Robustness, and Durability of an Automatic Brake System for Vehicle Adaptive Cruise Control, SAE Technical Paper, no , [12] J. E. Naranjo, C. Gonzalez, J. Reviejo, R. Garcia, and T. de Pedro, Adaptive Fuzzy Control for Inter-Vehicle Gap Keeping, IEEE Transactions on Intelligent Transportation Systems, vol. 4, no.3, pp , Sept [13] P. H. Shi, Design and Implementation of an FPGA-based Intelligent Cruise Control System, MS thesis, Department of Electrical and Control Engineering, National Chiao Tung University,