MPC-Based Approximation of Dual Control by Information Maximization

This paper proposes multiple-step active control algorithms based on MPC approach that approximate persistent system excitation in terms of the increase of the lowest eigenvalue of the parameter estimate information matrix. It is shown how the persistent excitation condition is connected with a proposed concept of stability of a system with uncertain parameters. Unlike similar methods, the proposed algorithms predict the information matrix for more than one step of control. The problem is formulated as an MPC problem with an additional constraint on the information matrix. This constraint makes the problem non-convex, thus only locally optimal solutions are guaranteed.