Research On Updating Trajectory Point-to-Point Integrated Two-dimensional Model Predictive Iterative Learning Control

There are many point-to-point(P2P)tracking problems in industrial processes,such as robot manipulator,fine chemical reactor,and so on.In these processes,some specific tracking points of system outputs must be reached in each batch when they carry out repetitive tasks.For the point-to-point tracking problems,iterative learning control(ILC)is a kind of most useful methods.The main research works focus on utilizing the freedom of none-critical tracking points in order to accelerate convergence of control method in batch direction.However,the disturbances and initial state errors of these process have been seldom discussed by now.This dissertation builds the two-dimensional(2D)model of the point-to-point tracking problem based on the trajectory updating strategy.The 2D model is then used to analyze the convergence in the batch domain and robustness in time domain of ILC method,because the model can exploit advantages of the method both in time domain and batch domain.Different control algorithms have been proposed based on this model,aiming at addressing the problems with disturbances and initial state errors.The main contributions of the dissertation are as follows:1.A two-dimensional point-to-point(called as 2DP2P)model is proposed by describing the point-to-point tracking problem based on the trajectory updating strategy.The characteristics of time-varying state transition matrix of the model is analyzed,and the system response of this model has been derived theoretically.Then,a 2D point-topoint integrated predictive iterative learning control(2DP2P-IPILC)method is proposed by combining model predictive control in the time domain.The algorithm improves the convergence speed in batch direction by trajectory updating,and it also enhances the robustness in time direction by integrating model predictive control.Convergence principle of the proposed method is analyzed based on 2D theory,and the boundary of output tracking errors is computed in detail.Simulations on a numerical model and a robot manipulator model are demonstrated.2.By adding the state variables to the feedback control law in the 2DP2P-IPILC,a new 2D point-to-point tracking control method is proposed.Convergence principle and tracking error boundary are analyzed with 2D theory.Because the method introduces all information of two states in different domains into the batch feedback and combines with the IPILC in time direction,the proposed method contributes to better robustness when both initial state errors and process disturbances exist.Simulations on the robot manipulator model are also illustrated.3.To address the problem of the constrained input and output in the point-to-point tracking problem,a feasible trajectory updating strategyof optimization in input constrained space is proposed.In the case of an arbitrary initial trajectory,system output can automatically converge to a specific trajectory even under the constrained conditions by using the proposed scheme.By combining with 2DP2P-IPILC,the tracking error can reach the optimal solution under the constrained conditions,and robustness and stability of the algorithm is also guaranteed at the same time...