Iterative learning control of nonlinear system with state and control time delay

作者：Ding Bao; Tao Hong Feng^*; Yang Hui Zhong

来源：Journal of Nanjing University of Science and Technology(Natural Science), 2011, 35(SUPPL. 1): 191-195.

• Bounded disturbances
• Closed-loop tracking systems
• Control time
• Dynamic performance
• Forgetting factors
• Industrial processs
• Iterative learning
• Iterative learning control
• Iterative learning control algorithm
• Learning controllers
• Learning gain
• Nonlinear time varying systems
• Output errors
• Robot arms
• Sufficient conditions
• Tracking control
• Tracking errors

For nonlinear systems with state and control time delays in actual industrial processes, which have the initial deviation and disturbances of output errors, a PID-type iterative learning control algorithm for repetitive nonlinear time-varying systems with any desired outputs and bounded disturbances is investigated. By using the memories of the desired trajectories, control and tracking error expectations in the processes of iterative learning, the learning controller is designed with the variable batches of forgetting factors. Based on the λ norm theory and the Bellman-Gronwall inequality, the necessary and sufficient conditions for the existence of the learning gain are discussed, and the convergence of the control algorithm is analyzed to ensure that the batch error of the closed-loop tracking system is bounded. The robustness and dynamic performances of the system are improved. The simulation of the motion control of the single-joint robot arm illustrates the effectiveness of the proposed algorithm.

• 出版日期2011
• 单位江南大学