Indirect model reference adaptive control of piecewise affine systems with concurrent learning

In this paper, we propose a concurrent learning-based indirect model reference adaptive control approach for multivariable piecewise affine systems as an enhancement of our previous work. The main advantage of the concurrent learning-based approach is that the linear independence condition of the recorded data suffices for the convergence of the estimated system parameters. The classical persistent excitation assumption of the input signal is not required. Moreover, it is proved that the closed-loop system is stable even when the system enters the sliding mode. The numerical example shows that the concurrent learning-based approach exhibits better tracking performance and achieves parameter convergence when compared with our previously proposed approach.