改进粒子群算法优化的非线性模型预测控制

Nonlinear model predictive control optimization with improved particle swarm algorithm

针对带有有界随机扰动和概率约束的非线性模型预测控制的优化控律求解问题.采用引入粒子滤波重采样步骤改进的粒子群算法,并与粒子的变异操作相结合来求解非线性模型预测控制优化控制律的方法,提高了算法的收敛速度和控制效果.对概率约束的处理,采用对不满足约束的粒子进行有效替代的方法,进而得到满足概率约束条件的优化控制律.仿真结果表明了提出的改进粒子群算法用于优化求解非线性模型预测控制的优化控制律的可行性和有效性.

In order to optimize nonlinear model predictive control（NMPC） system with bounded random disturbances and probabilistic constraint,the resampling step of particle filter is introduced into the PSO algorithm for the NMPC online optimization, combining with the existing method of particle variation. The revised PSO algorithm speeds up the convergence rate and leads to better optimization results. In handling probabilistic constraint, the particles that do not meet the constraint are replaced such that the feasible optimal control law can be obtained. The simulation results demonstrate the effectiveness of the improved PSO algorithm to optimize NMPC system.