Explicit Approaches to Constrained Model Predictive Control: A Survey

This paper presents a review of the explicit approaches to constrained model predictive control. The main motivation behind the explicit solution is that it avoids the need for real-time optimization, and thus allows implementation at high sampling frequencies in real-time systems with high reliability and low software complexity. The paper is organized as follows. Section 1 includes formulation of the constrained linear quadratic regulation (LQR) problem, summary of the implicit approaches, and the basics of the model predictive control (MPC). Sections 2 and 3 consider respectively the exact and the approximate approaches to explicit solution of constrained MPC problems, together with several examples.