基于PSO与LMI优化的非线性模型预测控制

Nonlinear Model Predictive Control Based on PSO and LMI Optimizing Algorithm

将微粒群算法(PSO)与线性矩阵不等式(LMI)用于输入受限非线性预测控制器的设计,提出了基于PSO与LMI联合优化的非线性预测控制算法。算法采用双模控制策略,利用LMI离线优化确定终端不变区域,以扩大非线性优化的求解范围,降低算法的保守性。利用PSO在线优化求解非线性预测控制输入,以避免求解非线性规划问题,同时对算法的稳定性进行了分析。仿真结果表明了该算法是有效的、可行的。

Particle swarm optimization （PSO） and linear matrix inequality （LMI） were used for the design of inputconstrained nonlinear predictive controller. A nonlinear predictive control algorithm based on PSO and LMI was proposed. The dual-mode control strategy was adopted in the algorithm. LMI was used for off- line optimization to determine a terminal invariant region to expend the solution extent of nonlinear optimization and decrease conservation degree of the algorithm; PSO was used for on-line solving nonlinear predictive control input to avoid solving nonlinear programming problems. The stability of the algorithm was analyzed. Simulation results show that the algorithm is efficient and feasible.