Particle-swarm optimization algorithm for model predictive control with constraints
基于粒子群优化的有约束模型预测控制器

We investigate the optimization algorithms for solving the constrained optimization problems in model predictive control(MPC). To deal with the disadvantage of the quadratic programming(QP) algorithm, we introduce and apply the chaotic particle-swarm optimization(CPSO) algorithm to solve the control problem with simultaneous constraints on inputs and states. A practical constrained optimization problem of the discrete-time linear system is solved by QP and PSO, respectively. By comparing the simulation results, we show the advantages of the PSO-based MPC algorithm.