This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The ...This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.展开更多
ADAPTIVE control is a proven method for learning feedback controllers for systems with unknown dynamic models,exogenous disturbances,nonzero setpoints,and unmodeled nonlinearities.Adaptive control has been applied for...ADAPTIVE control is a proven method for learning feedback controllers for systems with unknown dynamic models,exogenous disturbances,nonzero setpoints,and unmodeled nonlinearities.Adaptive control has been applied for years in process control,industry,aerospace systems。展开更多
Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial con...Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.展开更多
基金Supported by National Natural science Foundation-of P.R.Chlna （60474038, 60774022）, Specialized Research Fund for the Doctoral Program of Higher Educatlon（20060004002）
基金Supported by the National Natural Science Foundation of China （60974040, 61120106009）, the Research Award Foundation for the Excellent Youth Scientists of Shandong Province of China （BS2011DX010）, and the High School Science ＆ Technol- ogy Fund Planning Project of Shandong Province of China （J 10LG32）.
文摘This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.
文摘ADAPTIVE control is a proven method for learning feedback controllers for systems with unknown dynamic models,exogenous disturbances,nonzero setpoints,and unmodeled nonlinearities.Adaptive control has been applied for years in process control,industry,aerospace systems。
基金supported by National Natural Science Foundation of China(Nos.61374102,61433002 and 61120106009)High Education Science&Technology Fund Planning Project of Shandong Province of China(No.J14LN30)
文摘Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.
