基于性能诊断的预测控制器参数整定策略

Predictive controller parameter tuning strategy based on performance diagnostics

针对多变量模型预测控制系统在长期运行中出现设定值变化、模型失配、扰动特性变化后控制器参数不再匹配的问题,提出一种在得到性能诊断结果后基于改进粒子群算法的控制器参数整定方法。通过分析最优控制律与三项系统性能的关系,构造出对应的目标优化函数,对粒子群算法迭代过程中粒子的位置和惯性因子做出改进,弥补该算法易于陷入局部最优以及无法优化控制器整型参数变量的不足。用Wood-Berry模型对其进行仿真仿真,结果验证了该方法的有效性。

To address the problem that the controller parameters no longer match after the setpoint change,model mismatch and perturbation characteristics change in multivariate predictive control system in long-term operation,a controller parameter tuning method based on the improved particle swarm optimization(PSO)algorithm was proposed after the performance diagnosis results were obtained.By analyzing the relationship between the optimal control law and the performance of the three systems,the corresponding objective optimization function was constructed and the particle position and inertia factor were improved during the iteration of the swarm algorithm,which made up for the shortcomings that the algorithm is easy to fall into the local optimum and cannot optimize the controller integer parameter variables.The Wood-Berry model was used to validate the method,and the results verified the effectiveness of the method.