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1 Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology 1 1 c

2 Chapter 12 Modal Decomposition of State-Space Models 12.1 Introduction The solutions obtained in previous chapters, whether in time domain or transform domain, can be further decomposed to give a geometric understanding of the solution. The modal decomposition expresses the state equation as a linear combination of the various modes of the system and shows precisely how the initial conditions as well as the inputs impact these modes The Transfer Function Matrix It is evident from (10.20) that the transfer function matrix for the system, which relates the input transform to the output transform when the initial condition is zero, is given by H(z) C(zI ; A) ;1 B + D: (12.1) For a multi-input, multi-output (MIMO) system with m inputs and p outputs, this results in a pm matrix of rational functions of z. In order to get an idea of the nature of these rational functions, we express the matrix inverse as the adjoint matrix divided by the determinant, as follows: 1 H(z) C [adj(zi ; A)] B + D: det(zi ; A) n The determinant det(zi ; A) in the denominator is an n th -order monic (i.e. coecient of z is 1) polynomial in z, known as the characteristic polynomial of A and denoted by a(z). The

3 entries of the adjoint matrix (the cofactors) are computed from minors of (zi ; A), which are polynomials of degree less than n. Hence the entries of the matrices (zi ; A) ;1 1 det(zi ; A) adj(zi ; A) and 1 H(z) ; D Cadj(zI ; A)B det(zi ; A) are strictly proper, i.e. have numerator degree strictly less than their denominator degree. With the D term added in, H(z) b ecomes proper that is all entries have numerator degree less than or equal to the degree of the denominator. For jzj % 1, H(z)! D. The polynomial a(z) forms the denominators of all the entries of (zi ; A) ;1 and H(z), except that in some, or even all, of the entries there may be cancellations of common factors that occur between a(z) and the respective numerators. We shall have a lot more to say later about these cancellations and their relation to the concepts of reachability (or controllability) and observability. To compute the inverse transform of (zi ; A) ;1 (which is the sequence A k;1 ) and the inverse transform of H(z) (which is a matrix sequence whose components are the zero-state unit sample responses from each input to each output), we need to nd the inverse transform of rationals whose denominator is a(z) (apart from any cancellations). The roots of a(z) also termed the characteristic roots or natural frequencies of the system, thus play a critical role in determining the nature of the solution. A fuller picture will emerge as we proceed. Multivariable Poles and Zeros You are familiar with the denitions of poles, zeros, and their multiplicities for the scalar transfer functions associated with single-input, single-output (SISO) LTI systems. For the case of the p m transfer function matrix H(z) that describes the zero-state input/output behavior of an m-input, p-output LTI system, the denitions of poles and zeros are more subtle. We include some preliminary discussion here, but will leave further elaboration for later in the course. It is clear what we would want our eventual denitions of MIMO poles and zeros to specialize to in the case where H(z) is nonzero only in its diagonal positions, because this corresponds to completely decoupled scalar transfer functions. For this diagonal case, we would evidently like to say that the poles of H(z) are the poles of the individual diagonal entries of H(z), and similarly for the zeros. For example, given z + 2 z H(z) diagonal (z + 0:5) 2 (z + 2)(z + 0:5) we would say that H(z) has poles of multiplicity 2 and 1 at z ;0:5, and a pole of multiplicity 1 at z ;2 and that it has zeros of multiplicity 1 at ;2, at z 0, and at z 1. Note that

4 in the MIMO case we can have poles and zeros at the same frequency (e.g. those at ;2 in the above example), without any cancellation! Also note that a pole or zero is not necessarily characterized by a single multiplicity we may instead have a set of multiplicity indices (e.g. as needed to describe the pole at ;0:5 in the above example). The diagonal case makes clear that we do not want to dene a pole or zero location of H(z) in the general case to be a frequency where all entries of H(z) respectively have poles or zeros. For a variety of reasons, the appropriate denition of a pole location is as follows: Pole Location: H(z) has a pole at a frequency p 0 if some entry of H(z) has a pole at z p 0. The full denition (which we will present later in the course) also shows us how to determine the set of multiplicities associated with each pole frequency. Similarly, it turns out that the appropriate denition of a zero location is as follows: Zero Location: H(z) has a zero at a frequency 0 if the rank of H(z) drops at z 0. Again, the full denition also permits us to determine the set of multiplicities associated with each zero frequency. The determination of whether or not the rank of H(z) drops at some value of z is complicated by the fact that H(z) may also have a pole at that value of z however, all of this can be sorted out very nicely Similarity Transformations Suppose we have characterized a given dynamic system via a particular state-space representation, say with state variables x 1 x 2 x n. The evolution of the system then corresponds to a trajectory of points in the state space, described by the succession of values taken by the state variables. In other words, the state variables may be seen as constituting the coordinates in terms of which we have chosen to describe the motion in the state space. We are free, of course, to choose alternative coordinate bases i.e., alternative state variables to describe the evolution of the system. This evolution is not changed by the choice of coordinates only the description of the evolution changes its form. For instance, in the LTI circuit example in the previous chapter, we could have used i L ;v C and i L +v C instead of i L and v C. The information in one set is identical with that in the other, and the existence of a state-space description with one set implies the existence of a state-space description with the other, as we now show more concretely and more generally. The exibility to choose an appropriate coordinate system can be very valuable, and we will nd ourselves invoking such coordinate changes very often. Given that we have a state vector x, suppose we dene a constant invertible linear mapping from x to r, as follows: ;1 r T x x T r: (12.2) Since T is invertible, this maps each trajectory x(k) to a unique trajectory r(k), and vice versa. We refer to such a transformation as a similarity transformation. The matrix T embodies

5 the details of the transformation from x coordinates to r coordinates it is easy to see from (12.2) that the columns of T are the representations of the standard unit vectors of r in the coordinate system of x, which is all that is needed to completely dene the new coordinate system. Substituting for x(k) in the standard (LTI version of the) state-space model (10.1), we have or T r(k + 1) A T r(k) + Bu(k) (12.3) y(k) C T r(k) + Du(k): (12.4) r(k + 1) (T ;1 AT ) r(k) + (T ;1 B) u(k) (12.5) Ab r(k) + Bb u(k) (12.6) y(k) (CT ) r(k) + D u(k) (12.7) Cb r(k) + D u(k) (12.8) We now have a new representation of the system dynamics it is said to be similar to the original representation. It is critical to understand, however, that the dynamic properties of the model are not at all aected by this coordinate change in the state space. In particular, the mapping from u(k) to y(k), i.e. the input/output map, is unchanged by a similarity transformation Solution in Modal Coordinates The proper choice of a similarity transformation may yield a new system model that will be more suitable for analytical purposes. One such transformation brings the system to what are known as modal coordinates. We shall describe this transformation now for the case where the matrix A in the state-space model can be diagonalized, in a sense to be dened below we leave the general case for later. Modal coordinates are built around the eigenvectors of A. To get a sense for why the eigenvectors may be involved in obtaining a simple choice of coordinates for studying the dynamics of the system, let us examine the possibility of nding a solution of the form for the undriven LTI system x(k) k v v 6 0 (12.9) x(k + 1) Ax(k) (12.10) Substituting (12.9) in (12.10), we nd the requisite condition to be that (I ; A) v 0 (12.11)

6 i.e., that be an eigenvalue of A, and v an associated eigenvector. Note from (12.11) that multiplying any eigenvector by a nonzero scalar again yields an eigenvector, so eigenvectors are only dened up to a nonzero scaling any convenient scaling or normalization can be used. In other words, (12.9) is a solution of the undriven system i is one of the n roots i of the characteristic polynomial n n;1 a(z) det(zi ; A) z + a n;1 z + + a 0 (12.12) and v is a corresponding eigenvector v i. A solution of the form x(k) i k v i is referred to as a mode of the system, in this case the ith mode. The corresponding i is the ith modal frequency or natural frequency, and v i is the corresponding modal shape. Note that we can excite just the ith mode by ensuring that the initial condition is x(0) 0 i v i v i. The ensuing motion is then conned to the direction of v i, with a scaling by i at each step. It can be shown fairly easily that eigenvectors associated with distinct eigenvalues are (linearly) independent, i.e. none of them can be written as a weighted linear combination of the remaining ones. Thus, if the n eigenvalues of A are distinct, then the n corresponding eigenvectors v i are independent, and can actually form a basis for the state-space. Distinct eigenvalues are not necessary, however, to ensure that there exists a selection of n independent eigenvectors. In any case, we shall restrict ourselves for now to the case where because of distinct eigenvalues or otherwise the matrix A has n independent eigenvectors. Such an A is termed diagonalizable (for a reason that will b ecome evident shortly), or non-defective. There do exist matrices that are not diagonalizable, as we shall see when we examine the Jordan form in detail later in this course. Because (12.10) is linear, a weighted linear combination of modal solutions will satisfy it too, so nx x(k) i v i k i (12.13) i1 will be a solution of (12.10) for arbitrary weights i, with initial condition nx x(0) i v i (12.14) i1 Since the n eigenvectors v i are independent under our assumption of diagonalizable A, the right side of (12.14) can be made equal to any desired x(0) by proper choice of the coecients i, and these coecients are unique. Hence specifying the initial condition of the undriven system (12.10) species the i via (12.14) and thus, via (12.13), species the response of the undriven system. We refer to the expression in (12.13) as the modal decomposition of the undriven response. Note that the contribution to the modal decomposition from a conjugate pair of eigenvalues and will be a real term of the form v k + v k. From ( 12.14), it follows that V ;1 x(0), where is a vector with components i. Let W V ;1, and w0 i be the i th row of W, then nx x(k) k 0 i v i w i x(0) (12.15) i1

7 . 4 5 It easy to see that w i is a left eigenvector corresponding to the eigenvalue i. The above modal decomposition of the undriven system is the same as obtaining the diadic form of A k. The contribution of x(0) to the i th mode is captured in the term w i 0 x(0). Before proceeding to examine the full response of a linear time-invariant model in modal terms, it is worth noting that the preceding results already allow us to obtain a precise condition for asymptotic stability of the system, at least in the case of diagonalizable A (it turns out that the condition below is the right one even for the general case). Recalling the denition in Example 10.1, we see immediately from the modal decomposition that the LTI system (12.10) is asymptotically stable i j i j 1 for all 1 i n, i.e. i all the natural frequencies of the system are within the unit circle. Since it is certainly possible to have this condition hold even when kak is arbitrarily greater than 1, we see that the sucient condition given in Example 1 is indeed rather weak, at least for the time-invariant case. Let us turn now to the LTI version of the full system in (10.1). Rather than approaching its modal solution in the same style as was done for the undriven case, we shall (for a dierent point of view) approach it via a similarity transformation to modal coordinates, i.e., to coordinates dened by the eigenvectors fv i g of the system. Consider using the similarity transformation x(k) V r(k) (12.16) where the ith column of the n n matrix V is the ith eigenvector, v i : V v 1 v 2 v n (12.17) We refer to V as the modal matrix. Under our assumption of diagonalizable A, the eigenvectors are independent, so V is guaranteed to be invertible, and (12.16) therefore does indeed constitute a similarity transformation. We refer to this similarity transformation as a modal transformation, and the variables r i (k) dened through (12.16) are termed modal variables or modal coordinates. What makes this transformation interesting and useful is the fact that the state evolution matrix A now transforms to a diagonal matrix : V ;1 0 AV diagonal f 1 n g n 3 7 (12.18) The easiest way to verify this is to establish the equivalent condition that AV V, which in turn is simply the equation (12.11), written for i 1 n and stacked up in matrix form. The reason for calling A \diagonalizable when it has a full set of independent eigenvectors is now apparent. Under this modal transformation, the undriven system is transformed into n decoupled, scalar equations: r i (k + 1) i r i (k) (12.19)

8 for i 1 2 : : : n. Each of these is trivial to solve: we have r i (k) i k r i (0). Combining this with (12.16) yields (12.13) again, but with the additional insight that i r i (0) (12.20) Applying the modal transformation (12.16) to the full system, it is easy to see that the transformed system takes the following form, which is once again decoupled into n parallel scalar subsystems: where the i and i are dened via r i (k + 1) i r i (k) + i u(k) i 1 2 : : : n (12.21) y(k) 1 r 1 (k) + + n r n (k) + Du(k) (12.22) V ;1 B h i CV 1 2 n (12.23) n The scalar equations above can be solved explicitly by elementary methods (compare also with the expression in (22.2): r i (k) k i r i (0) + k i } 0 {z where \ZIR denotes the zero-input response, and \ZSR the zero-state response. From the preceding expression, one can obtain an expression for y(k). Also, substituting (12.24) in (12.16), we can derive a corresponding modal representation for the original state vector x(k). We leave you to write out these details. Finally, the same concepts hold for CT systems. We leave the details as an exercise. Example 12.1 Consider the following system: {z ZIR k;x 1 ;`;1 ZSR i u(`) (12.24) x_ x u (12.25) x_ 2 8 ;2 x 2 1 We will consider the modal decomposition of this system for the zero input response. The eigenvalues of A are -4 and 2 and the associated eigenvectors are [ 1 ;4 ] 0 and [ 1 2 ] 0 : The modal matrix is constructed from the eigenvectors above:! V 1 1 ;4 2 } (12.26)

9 ;0:0145 Its inverse is given by ;1 W V 1 2 ; It follows that: W AV 1 0 ;4 0 : Now let's dene r in modal coordinate as ;1 x(t) T r! r(t) x(t): T : Then in terms of r, the original system can be transformed into the following: r_ 1 ;4 0 r 1 r_ r 2 : (12.27) The response of the system for a given initial state and zero input can now be expressed as: x(t) V r(t) V e (t;t 0 ) W x(t 0 ) 1 1 e ;4(t;t 0 ) ;1 ;4 2 0 e 2(t;t 0 ) x(t 0 ): For instance, if the initial vector is chosen in the direction of the rst eigenvector, i.e., x(t 0 ) v 1 [ 1 ;4 ] 0 then the response is given by: x(t) Example 12.2 Inverted Pendulum 1 ;4 e ;4(t;t 0 ) : Consider the linearized model of the inverted pendulum in Example 7.6 with the parameters given by: m 1, M 10, l 1, and g 9:8. The eigenvalues of the matrix A are 0, 0, 3:1424, and ;3:1424. In this case, the eigenvalue at 0 is repeated, and hence the matrix A may not be diagonalizable. However, we can still construct the Jordan form of A by nding the generalized eigenvectors corresponding to 0, and the eigenvectors corresponding to the other eigenvalues. The Jordan form of A, T ;1 AT and the corresponding transformation T are given by: :0145 0: : ;0:0455 ;0:0455 T 0 0 ;0:1591 3: : : ;3: :

10 We can still get quite a bit of insight from this decomposition. Consider the zero input response, and let x(0) v 1 [ ] 0. This is an eigenvector corresponding to the zero eigenvalue, and corresponds to a xed distance s, zero velocity, zero angular position, and zero angular velocity. In that case, the system remains in the same position and the response is equal to x(0) for all future time. Now, let x(0) v 2 [ ] 0, which corresponds to a non-zero velocity and zero position, angle and angular velocity. This is not an eigenvector but rather a generalized eigenvector, i.e., it satises Av 2 v 1. We can easily calculate the response to be x(t) [t 1 0 0] implying that the cart will drift with constant velocity but will remain in the upright position. Notice that the response lies in the linear span of v 1 and v 2. The case where x ( 0) v 3 corresponds to the eigenvalue 3:1424. In this case, the cart is moving to the left while the pendulum is tilted to the right with clockwise angular velocity. Thus, the pendulum tilts more to the right, which corresponds to unstable behavior. The case where x(0) v 4 corresponds to the eigenvalue ;3:1424. The cart again is moving to the left with clockwise angular velocity, but the pendulum is tilted to the left. With an appropriate combination of these variables (given by the eigenvector v 4 ) the response of the system converges to the upright equilibrium position at the origin.

11 Exercises Exercise 12.1 Use the expression in (12.1) to nd the transfer functions of the DT versions of the controller canonical form and the observer canonical form dened in Chapter 8. Verify that the transfer functions are consistent with what you would compute from the input-output dierence equation on which the canonical forms are based. Exercise 12.2 Let v and w 0 be the right and left eigenvectors associated with some non-repeated eigenvalue of a matrix A, with the normalization w 0 v 1. Suppose A is perturbed innitesimally to A + da, so that is perturbed to + d, v to v + dv, and w 0 to w 0 + dw 0. Show that d w 0 (da)v.

12 MIT OpenCourseWare J / J Dynamic Systems and Control Spring 2011 For information about citing these materials or our Terms of Use, visit:

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