A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm

Shi, Jia; Zhou, Hua; Cao, Zhenggang; Jiang, Qingyin

doi:10.1016/j.jprocont.2014.07.004

Cited by 42 publications

(18 citation statements)

References 11 publications

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“…The closed loop controller constitutes the inner loop, and the iterative learning controller is in the outer loop. The output of ILC is used to change the given value of the closed-loop controller [11]- [15]. This control structure is first proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

2021

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MIT control strategy is a kind of model reference adaptive control method with the simplest structure. The reason why the structure is simple is that only the gain of the controlled object is adaptively adjusted. Because only the gain is adjusted, the ability of MIT control strategy to change the characteristics of the controlled object is limited. This also limits its application. In this paper, the simple idea of iterative learning control is introduced into MIT controller to increase the controller's ability to adjust the characteristics of the controlled object, and make it suitable for complex control objects. In the proposed control method, the output of the iterative learning controller is used to adjust the adaptive law of the MIT controller. The proposed control method is applied to the speed control system of ultrasonic motor. Experiments show that although only the gain of MIT controller can be adjusted, the learning process based on memory increases the degree of control freedom. Therefore, the dynamic characteristics of the system can be greatly changed, and the control performance can be significantly improved. Moreover, the proposed method only needs to add a simple P-type iterative learning controller to the MIT controller, and the increase of online calculation amount is small. INDEX TERMS Ultrasonic motor, iterative learning control (ILC), MIT control.

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Section: Introductionmentioning

confidence: 99%

Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

2021

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“…The self-learning mechanism in linear repetition of iterative learning control processes and linear repetitive processes are essentially consistent. In its run time, the system information not only propagates along the channel (along the pass), but also between the channels (pass to pass) that is, a two-dimensional system with dynamic behaviour in two independent directions [9]. In the framework of two-dimensional system theory, iterative learning control for linear repetitive processes has also attracted great interest among researchers.…”

Section: Introductionmentioning

confidence: 99%

Robust PD-Type Iterative Learning Control of Discrete Linear Repetitive Processes in the Finite Frequency Domain

Wang

Yang

2020

Mathematics

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This paper studies a robust iterative learning control design for discrete linear repetitive processes in the finite frequency domain. Firstly, the state-space model of the iterative learning process is deduced. Then the dynamic performance condition of the control system in the finite frequency domain is derived by combining it with the stability theory of discrete linear repetitive processes. The system performances in the finite frequency domain are then transformed into the corresponding solutions of the linear matrix inequality by using the generalised KYP lemma. Finally, an integrated state feedback PD-type iterative learning control strategy is proposed. The robust control problem with norm-bounded uncertainty and convex polyhedral uncertainty are also considered in this paper. The simulation of the injection velocity in injection molding verified that the proposed methods in this paper are more effective than the P-type state feedback iterative learning control algorithm.

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“…Based on the concept of two-dimensional systems, the integrated predictive iterative learning control method was discussed by Chen and Xiong [41], which had good tracking performance and highly convergence rate. In [42], Shi et al proposed an indirect iterative learning control system composed of the internal circulation model predictive control and external circulation iterative learning control based on the 2D-GPC algorithm, which achieved the parametric optimization of the feedback controller and the design of the controller of simple iterative learning was realized, and ensured the optimal control performance of the system under the 2D-GPC. In [43], considering the nonlinearity of batch processes, a two-dimensional iterative learning predictive control algorithm based on the real-time feedback was discussed and verified the feasibility and convergence of this algorithm through simulation cases.…”

Section: Introductionmentioning

confidence: 99%

2D Terminal Constrained Model Predictive Iterative Learning Control of Batch Processes With Time Delay

Zhang

Wang

2019

IEEE Access

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In this paper, a 2D terminal constrained model predictive iterative learning control method of batch processes with time delay is proposed to deal with time delay, input and output constraints, and disturbances in batch processes. Firstly, an iterative learning control law is designed for the given batch process; then the state error and output tracking error are introduced, and the original state-space model is converted to an equivalent 2D-FM model. In the meantime, an optimal performance index with terminal constraints is introduced, and an update law is designed to minimize the objective function under input and output constraints. The robust constraint set is adopted that the system state is converged to this set round the desired point. Then, based on the designed optimal performance index and Lyapunov stability theory, the MPC problem is transformed into a linear matrix inequality problem and a sufficient condition is given to ensure the robust asymptotically stability of the closed-loop system. Finally, the validity of the proposed method is proved by the simulation on a stirred tank. INDEX TERMS Batch processes, time delay, terminal constraints, 2D terminal constrained model predictive iterative learning control.

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A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm

Cited by 42 publications

References 11 publications

Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

Robust PD-Type Iterative Learning Control of Discrete Linear Repetitive Processes in the Finite Frequency Domain

2D Terminal Constrained Model Predictive Iterative Learning Control of Batch Processes With Time Delay

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