Model predictive control: Breaking through constraints

Because it naturally and explicitly handles constraints, particularly control input saturation, model predictive control (MPC) is a potentially powerful approach for nonlinear control design. However, nonconvexity of the nonlinear programs (NLP) involved in the MPC optimization makes the solution problematic. Extending the concept of solving the Hamilton-Jacobi-Bellman equation backwards (the so-called ''converse HJB approach'') to the constrained case provides a method to generate various classes of challenging nonlinear benchmark examples, where the true constrained optimal controller is known. Properties of MPC-based constrained techniques are then evaluated and implementation issues are explored by applying both nonlinear MPC and MPC with feedback linearization.