| The performance of the controller not only determines whether the control system can achieve the desired goal,but also directly affects the stability and safety of the entire industrial process.At present,the computer control systems of various industrial enterprises are gradually improving,which provides conditions for storing a large amount of control system operating data.These data contain various information related to equipment characteristics,process dynamics,system operating conditions,and controller performance.How to make full use of these data to optimize the performance of the running controller has always been one of the research hotspots in the field of process control.Aiming at the most widely used PID controller in practice,this topic studies the performance optimization methods of PID control systems with different structures based on data-driven,which mainly includes the following contents:(1)A data-based PID controller parameter correction method is proposed.The method collects the closed-loop working data of the system,constructs the "data vector" of the current moment,and stores it in the database in a specific format.Then the similarity measurement algorithm is used to obtain the initial prediction parameters of PID controller.If the control loop performance is not ideal,the optimization algorithm is adopted to update the controller parameters.The optimized parameters are then added to the database to start the new iterative optimization.Finally,the proposed algorithm is applied to the p H control problem of a typical nonlinear process,and the experimental results show the effectiveness of the proposed algorithm.(2)The fictitious reference iterative tuning(FRIT)method is improved,and on this basis,two cascade system PID control parameter optimization strategies are proposed.The improved FRIT method adds the monitoring of the criterion function in the iterative process,and adjusts the iteration step length according to the change of the function,which speeds up the convergence speed of the controller parameter optimization algorithm.The first proposed cascade system controller parameter optimization strategy first deals with the secondary loop,and then takes the primary and secondary control loop as a whole,and uses the improved FRIT method to correct and obtain the optimized parameters of the primary loop.The second strategy proposed is the controller online optimization method based on the minimum variance performance index.By collecting closed-loop data of the control system within a dynamic time window,the minimum variance performance index is estimated,and then compared with the set performance index threshold.When the performance does not meet the expectations,the fictitious reference iterative tuning algorithm is used to correct the cascade controller parameters.(3)The virtual reference feedback tuning(VRFT)is introduced,and based on its application in the parameter optimization of single loop controller,the PID controller parameter optimization problem of multi-input multi-output control system is studied.The input and output data of the system are collected and the virtual input and output matrix of the controller is calculated.The principle function of multi-input multi-output system is designed,and then the controller parameters of different loops which make the principle function minimum are identified by the least square algorithm Finally,the method is applied to the distillation tower model,and the closed-loop data and open-loop data of the system are collected respectively to tune the parameters of the multi-loop PID controller. |
