We extend a recently developed design for indirect adaptive model predictive control (IAMPC) and presents additional results on its stability properties. The IAMPC guarantees constraints satisfaction including during the learning transient, is input-to-state stable (ISS) with respect to the parameter estimation error, and has computational burden comparable to that of non-adaptive MPC. In this paper we extend the IAMPC to the case of uncertain input-to-state matrix, we provide a new method to design robust constraints, and we show additional stability results, in particular that asymptotic stability does not require the parameter estimation error to be zero, which also allow us to derive a tighter ISS Lyapunov function.
['Donglei Fan', 'Stefano Di Cairano']
Further results and properties of indirect adaptive model predictive control for linear systems with polytopic uncertainty