A nonlinear proportion integration differentiation(PID)controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control proc...A nonlinear proportion integration differentiation(PID)controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multi-step predictive cost energy to train the weights of the decou-pling controller.Simulation studies have shown the efficiency of these strategies.展开更多
基金supported in part by the Opening Project Foundation of National Laboratory of Industrial Control Technology(No.0708008)the National Natural Science Foundation of China(Grant No.60374037 and 60574036)+1 种基金the Program for New Century Excellent Talents in University of China(NCET)the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20050055013).
文摘A nonlinear proportion integration differentiation(PID)controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multi-step predictive cost energy to train the weights of the decou-pling controller.Simulation studies have shown the efficiency of these strategies.
