基于改进粒子群算法的隐式广义预测控制

针对大多数工业系统的控制输入输出都存在约束的情况,提出一种基于改进粒子群算法的隐式广义预测控制算法(IGPC)。粒子群算法(PSO)是一种基于群体的智能优化算法,解决受约束的优化问题具有精度高、收敛速度快等优点;为了避免粒子群算法陷入早熟,提高精度,引入细菌觅食算法中的自适应迁徙机制。在隐式广义预测控制的滚动优化环节引入改进粒子群算法,弥补了传统GPC在处理受约束控制问题上的缺陷。仿真结果表明了该方法的有效性和良好的控制性能。

Implicit generalized predictive control based on improved particle swarm optimization

An implicit generalized predictive control(IGPC)algorithm based on improved particle swarm optimization(IPSO)is proposed for most industrial systems with control input and output constraints.Particle swarm optimization(PSO)is a swarm-based intelligent optimization algorithm,which has the advantages of high accuracy and fast convergence speed to solve constrained optimization problems.In order to avoid the premature and improve the accuracy of PSO,the adaptive migration mechanism in bacterial foraging algorithm is introduced.An improved particle swarm optimization algorithm is introduced to the rolling optimization of implicit generalized predictive control(IGPC),which makes up for the shortcomings of traditional GPC in dealing with constrained control problems.The simulation results show that the method is effective and has good control performance.