Several Issues On The Iterative Learning Control For The Batch Process

Iterative learning control is proposed in order to achieve bounded error in tracking the expectations orbit in the tracking systems, and it is widely used in the controlled system with repetitive motion. The research is significance to the systems which has the requirements on the location for high repeatability and high precision tracking, and also has a good application in the batch process with nonlinear, time-varying and time-delay. This research work done by the following:1. As the P-type iterative learning control algorithm is sensitivity of the output error disturbance and the initial error, the iterative learning control method with forgetting factor used in time-varying nonlinear system is investigated. In the case of the disturbance, by using the memory of the desired trajectory, control and tracking error expectations in the process of iterative learning, the learning controller can be designed through the bounded of learning gain and the batches of time-varying factors, then the necessary and sufficient conditions for the existence and convergence analysis of the control algorithm is given in terms of the operator theory, and the robustness and dynamic performance of the system is improved.2. As the P-type iterative learning control algorithm is sensitivity of the output error disturbance and the initial error, and the PD-type iterative learning control algorithm is easily to amplify the noise and reduce the robustness of the system, a PD-type iterative learning tracking control algorithm for repetitive nonlinear time-varying systems with any desired output and bounded disturbances is investigated. By using the memory of the desired trajectory, control and tracking error expectations in the process of iterative learning, the learning controller is designed with the variable batches of forgetting factors. Based on theλnorm theory and the Bellman-Gronwall inequality, the necessary and sufficient conditions for the existence of the learning gain is discussed, and the convergence of the control algorithm is analyzed to ensure the batch error of the closed-loop tracking system is bounded. The robustness and dynamic performance of the system is improved.3. For the nonlinear systems with state and control time delay in the actual industrial processes, which with the initial deviation and disturbances of output error, a PID-type iterative learning control algorithm for repetitive nonlinear time-varying systems with any desired output and bounded disturbances is investigated. By using the memory of the desired trajectory, control and tracking error expectations in the process of iterative learning, the learning controller is designed with the variable batches of forgetting factors. The necessary and sufficient conditions for the existence of the learning gain is discussed, and the convergence of the control algorithm is analyzed to ensure the batch error of the closed-loop tracking system is bounded.4. For a class of nonlinear time-varying repetitive systems with time delay,initial deviation,output error and condition disturbances, in arbitrary initial condition, the PD type closed-loop iterative learning tracking control problem which satisfies the global Lipschitz conditions is investigated. By defining the bounded desired trajectory,expected control and the tracking error of the iterative learning process, the necessary and sufficient conditions for the existence of the gain of the learning controller is given, which can guarantee the tracking error of the closed-loop system to be bounded, and the uniform convergence criteria of the control algorithm is also proved. Simulation of the speed control of the injection molding machine demonstrate the proposed method of this paper not only can improve the performance of the system significantly, but also has certain inhibition effect on the bounded disturbance and arbitrary initial problem.