一种基于PSO算法的非线性模型预测控制方法

A nonlinear model predictive control based on PSO algorithm

将微粒群优化（PSO）算法用于输入受限非线性系统，提出了一种基于PSO的非线性模型预测控制算法。该算法采用双模控制策略，将保证预测控制稳定性的终端等式约束转化为终端不等式约束，推导出使系统稳定的不变可行集。在不变集外，利用PSO算法优化求解预测控制律，使系统状态进入不变集；在不变集内，利用线性状态反馈使系统状态渐近稳定。同时对算法的稳定性进行了分析。仿真结果证明了该算法的可行性和有效性。

A nonlinear model predictive control algorithm based on particle swarm optimization （PSO） is proposed in this paper. PSO is used for design of the input constrained nonlinear model predictive controller. The dual-mode control strategy is used in the algorithm, and then the terminative equality constrain which guarantees the stability of predictive control is transferred into the terminative inequality constrain. The invariant set of the system is presented. Outside the invariant set PSO algorithm is used to optimally solve prediction control input; inside the invariant set the linear state feedback method is used to make the state of the system gradually stable. The stability of the proposed algorithm is analyzed. The simulation results show that the proposed algorithm is feasible and valid.