Concurrent learning adaptive identification of piecewise affine systems

Abstract

In this paper, we enhance a recently proposed method for adaptive identification of piecewise affine systems by the use of concurrent learning. It is shown that the concurrent use of recorded and instantaneous data leads to exponential convergence of all subsystem parameters under verifiable conditions on the recorded data. A key advantage of the proposed method is that linear independence of the recorded data is sufficient, compared to the persistence of excitation assumed by previous adaptive parameter identifiers. Furthermore, the procedure tremendously improves the performance of adaptive identification for piecewise affine systems that previously suffered from slow convergence.