Preface
In this book, methods of adaptive signal processing are borrowed from the field of digital signal processing to solve problems in dynamic systems control. Adaptive filters, whose design and behavioral characteristics are well known in the signal processing world, can be used to control plant dynamics and to minimize the effects of plant disturbance. Plant dynamic control and plant disturbance control are treated herein as two separate problems. Optimal least squares methods are developed for these problems, methods that do not interfere with each other. Thus, dynamic control and disturbance canceling can be optimized without one process compromising the other. Better control performance is the result. This is not always the case with existing control techniques. Inverse control of plant dynamics involves feed-forward compensation, driving the plant with a filter whose transfer function is the inverse of that of the plant. Inverse compensation is well known in signal processing and communications. Every MODEM in the world uses adaptive filters for channel equalization. Similar techniques are described here for plant dynamic control. Inverse control is feed-forward control. The same precision of feedback that is obtained with existing control techniques is also obtained with adaptive feed-forward control since feedback is incorporated in the adaptive algorithm for obtaining the parameters of the feed-forward compensator. Inverse control can be used effectively with minimum phase and non-minimum phase plants. It cannot work with unstable plants, however. They must first be stabilized with conventional feedback, of any design that simply achieves stability. Then the plant and stabilizing feedback can be treated as an equivalent stable plant that can be controlled in the usual way with adaptive inverse control. Model reference control can be readily incorporated into adaptive inverse control. Adaptive noise canceling techniques are described that allow optimal reduction of plant disturbance, in the least squares sense. Adaptive noise canceling does not affect inverse control of plant dynamics. Inverse control of plant dynamics does not affect adaptive disturbance canceling. If initial feedback is needed to provide plant stabilization, the design of the stabilizer has no effect on the optimality of the adaptive disturbance canceler. The designs of the adaptive inverse controller and of the adaptive disturbance canceler are quite simple once the control engineer gains a mastery of adaptive signal processing. This book provides an introductory presentation of this subject with enough detail to do system design. The mathematics is simple and indeed the whole concept is simple and easy to implement, especially when compared with the complexity of current control methods. Adaptive inverse control is not only simple, but it affords new control capabilities that can often be superior to those of conventional systems. Many practical examples and applications are shown in the text. Another feature of adaptive inverse control is that the same methods can be applied to adaptive control of nonlinear plants. This is surprising because nonlinear plants do not have transfer functions. But approximate inverses are possible. Experimental results with nonlinear plants have shown great promise. Optimality cannot be proven yet, but excellent results have been obtained. This is a very promising subject for research. The whole area of nonlinear adaptive filtering is a fascinating research field that already shows great results and great promise. This book was originally published under the title Adaptive Inverse Control. We are grateful to IEEE Press and John Wiley, Inc. for bringing it back into print. We are also grateful to colleagues Gene Franklin, Karl Johan Astrom, Jose Cruz, Brian Anderson, Paul Werbos, and Shmuel Merhav for their early comments, suggestions, and feedback. We are grateful to former Stanford students Steve Piche, Michel Bilello, Gregory Plett, and Ming-Chang Liu who confirmed the results with experiments and who assisted with preparation of the drawings and final manuscript. Bernard Widrow Eugene Walach |
Chapter 10.2.1 - Adaptive MIMO Modeling Using Scheme B
10.2.1 Adaptive MIMO Modeling Using Scheme B The plant can be adaptively modeled, using independent dither inputs, as shown for the two-input two-output case of Fig. 10.7. Use of uncorrelated dither in the modeling process is necessary when the plant inputs alone cannot be used, that is, when they are correlated with each other or when they are not persistently exciting. The dither scheme illustrated in Fig. 10.7 is a MIMO version of scheme B, which is shown in Fig. 4.3(b) for the SISO case. A more succinct picture of the dither scheme is shown in Fig. 10.8, making use of a vector block diagram. The modeling process illustrated in Fig. 10.7 involves the adaptation of four separate filters, Figures 10.9, 10.10, 10.11, and 10.12 have been devised as an aid in addressing these issues. Figure 10.9 shows one-half of the adaptive process illustrated in Fig. 10.7. This half operates independently of the other half and can be studied all by itself. The natural driving signal, the controller output, is not used for plant identification by scheme B. The dither signal serves this purpose. In fact, the natural driving signal acts as noise to the adaptive identification process. Figure 10.10 is equivalent to Fig. 10.9, except that here the interference due to the controller output is accounted for by an additive noise component at the plant output, and it appears in addition to the plant disturbance. Assume that all of the filters of Fig. 10.10 are FIR and that they all are of length n time samples. Assume that the two dither signals of Fig. 10.10 are white and of equal power. Figure 10.11 shows how a single white dither input could be used under these circumstances instead of the two dither inputs of Fig. 10.10. Two dither signals are created from a single one by using a delay of n time samples. In this way, all of the signals at the taps of the tapped delay lines of The system of Fig. 10.11 can be redrawn as shown in Fig. 10.12. The two adaptive filters
Figure 10.7 Modeling a two-input two-output system in accord with scheme B.
Figure 10.8 Simplified block diagram for modeling a MIMO plant based on scheme B.
Figure 10.9 A part of the two-channel MIMO modeling process.
Figure 10.10 A part of the two-channel MIMO modeling process, simplified.
Using formulas (B.20) for time constant, (B.22) for stable range of µ, and (B.27) for misadjustment, the corresponding formulas for scheme B applied to a multichannel MIMO system are
Figure 10.11 Alternative dither for two-channel MIMO modeling process.
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