Nonlinear predictive command governors for constrained tracking

Abstract

A method based on conceptual tools of predictive control is described for solving tracking problems wherein pointwise-in-time input and/or state inequality constraints are present. It consists of adding to a primal compensated system a nonlinear device called command governor (CG) whose action is based on the current state, set-point and prescribed constraints. The CG selects at any time a virtual sequence amongst a family of linearly parameterized command sequences by solving a convex constrained quadratic optimization problem, and feeds the primal system according to a receding horizon control philosophy. The overall system is proved to fulfill the constraints, be asymptotically stable, and exhibit an offset-free tracking behaviour, provided that an admissibility condition on the initial state is satisfied. Though the CG can be tailored for the application at hand by appropriately choosing the available design knobs, the required on-line computational load for the usual case of affine constraints is well tempered by the related relatively simple convex quadratic programming problem.