Particle swarm optimization based model predictive control for constrained nonlinear systems

Particle Swarm Optimization (PSO) is an effective optimization technique that can efficiently solve nonlinear and non-convex optimization problems, however, system constraints like output and states constraints cannot be considered. On the other hand, Model Predictive Control (MPC) is an efficient optimization technique that can offer the optimal solution while respecting the given constraints; however, for constrained nonlinear/non-convex optimization this turned out to be a complex problem and in many cases, it became inapplicable in real-time due to the computation burden. This paper presents a Particle Swarm Optimization based Model Predictive Control for constrained nonlinear systems. It is a simple control algorithm that offers a sub-optimal solution, in reasonable time, for nonlinear systems while respecting the given constraints. The proposed technique can consider both hard and soft constraints. The new developed technique is not considered as a computation burden, and on-line application is possible. An application of the proposed technique to a speed control of linear induction control is presented.