Persistently Exciting Data-Driven Model Predictive Control

Abstract: Persistence of excitation (PE) of the system input is a fundamental requirement for the successful operation of data-driven model predictive control, as it ensures that the input--output data contains sufficient information about the underlying system dynamics. Nonetheless, this property is usually assumed rather than guaranteed. This paper introduces a novel data-driven predictive control formulation that maintains persistence of excitation. The technical development that permits this is the characterization of the nonexciting input set i.e. the set of inputs that lead to loss of PE, and the consequent derivation of a pair of disjoint, linear inequality constraints on the input that, if satisfied, maintain PE. When used in the predictive control formulation, these constraints lead to a mixed-integer optimal control problem with a single binary variable or, equivalently, a pair of disjoint quadratic programming problems that can be efficiently and reliably solved in parallel. Numerical examples show how these constraints are able to maintain persistence of excitation on the input during the controller's operation.