【摘要】 将改进粒子群优化算法(MPSO)融合到神经网络预测控制中,提出了基于MPSO-RBF混合优化策略的模型预测器,以及基于MPSO算法的非线性优化控制器.针对过热汽温的控制。构造了基于神经网络预测控制的串级控制系统,并就该系统在实现时所涉及到的预测模型、滚动优化算法、反馈校正、仿真参数设置问题等进行了分析,给出了MPSO算法的粒子编码、操作设计和混合优化算法步骤.对某超临界600 MW直流锅炉高温过热器的过热汽温控制,进行了仿真试验,结果表明该方法具有良好的性能指标和应用前景.更多还原

【Abstract】 Combining modified particle swarm optimization (MPSO) with neural network predictive control (NNPC), we propose a model-prediction controller,based-on modified particle swarm optimization (MPSO) and radial basis function (RBF) hybrid optimization strategy (MPSO-RBF),and a nonlinear optimization controller,based-on MPSO.For the super- heated steam temperature control,we construct a cascade control system based on the neural network predictive control, and analyze all related problems,including the predictive model,the rolling optimizing algorithm,the feedback adjusting and the simulation-parameter setting.We also present the particle encoded format of MPSO,operating design method,and steps in hybrid optimization algorithm.Simulation experiments of the superheated steam temperature control were done in a super-critical-600 MW direct-current boiler,demonstrating the validity,the superior performance and the application prospects.更多还原