MPC for a class of nonlinear systems with guaranteed identifiability

This paper addresses the problem of model predictive control for a class of nonlinear systems which satisfies persistent excitation condition. The conditions under which a nonlinear system description can be handled are specified and two algorithms (one optimizing the first input sample and the other considering optimization of an M-sample subsequence of the input profile) solving the persistent excitation condition within a predictive controller for nonlinear systems are developed, both maximizing the smallest eigenvalue of the information matrix increase. The numerical experiments performed on a test-bed system demonstrate that the algorithms are able to successfully improve identifiability of a nonlinear system description while keeping the original controller performance degradation lower than arbitrarily chosen level.