粒子群优化辨识的自适应预估控制及应用

Adaptive Predictive Control and Application of Particle Swarm Optimization Identification

针对Smith预估器对预测模型精度依赖程度较高的问题,提出了一种基于粒子群优化(PSO)辨识算法的自适应预估控制方法。该控制方法利用PSO辨识方法在线调整Smith预估器参数,利用单神经元的非线性逼近特性及自学习、自组织能力,对控制器参数进行在线修正。将该控制方法应用于矿井通风系统风量控制仿真分析中,在系统参数时变情况下,进行跟踪响应分析。结果表明,该控制方法对大滞后时变系统具有很强的适应性和鲁棒性,具有较强的抗干扰能力和良好的跟踪性能。

In order to solve the problem that the accuracy of Smith predictor was highly dependent on the prediction model, an adaptive predictive control algorithm based on particle swarm optimization （PSO） algorithm was proposed. The control method uses the PSO identification method to adjust the parameters of the Smith predictor online, and makes use of the nonlinear approximation property of the single neuron and the self-learning and self-organizing ability to adjust the controller parameters online. Finally, the control method is applied to the simulation analysis of air volume control in mine ventilation system. In the case of time-varying system parameters, tracking response analysis. The results show that the control method has strong adaptability and robustness to the large time delay time-varying system, and has strong anti-interference ability and good tracking performance.