Parallel algorithms for adaptive control: Robust stability

Abstract A class of parallel algorithms for adaptive control of siso linear systems is described. The systems considered are assumed to belong to one among a finite number of classes of admissible process models, and each class is robustly etabilizable by some linear time-invariant controller. The control used is chosen in real time — from among the outputs of a finite set of linear time-invariant candidate controllers — by a tuner or supervisor, according to observations of suitably defined "identification errors." The method preserves the robustness properties of the linear control design in an adaptive context. We expect that parallel algorithms of the type discussed here will be useful tools to exploit the compromise between performance of an adaptive control system and the computational power of the hardware in which it is implemented. Another application is to fault-tolerant control.