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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.
Aiming at solving the optimal control law of nonlinear model predictive control with bounded random perturbations and probability constraints, the improved Particle Swarm Optimization (PSO) with particle swipe resampling is introduced and the nonlinear model predictions Control and optimize the control law to improve the convergence speed and control effect of the algorithm.For the processing of the probability constraint, the method of effective replacement for particles that do not satisfy the constraints is adopted, then an optimal control law satisfying the probability constraints is obtained.The simulation results show The feasibility and effectiveness of the improved particle swarm optimization algorithm for optimizing the optimal control law for nonlinear model predictive control are presented.