System identification and learning control

This chapter describes how system identification interacts naturally with the learning control problem. The linkage between the two fields is described within the framework of discrete-time modern control and system identification theories. The role of the Markov parameters in learning control is described, and straight forward identification of these parameters can be used in the design of high performance learning controllers. For time-varying models the basis functions become important in making both the identification and learning control problems practical. Although perfect identification cannot be expected in practice, it is often sufficiently accurate in guiding the learning process to achieve a level of tracking accuracy beyond that obtained by inverting the identified model alone. Numerical and experimental results are used to illustrate key concepts described in this chapter.

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