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

Wang, Lei; Li, Mu; Yang, Huizhong

doi:10.3390/math8061004

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…For a given α and β, the LMI (Equations ( 32) and ( 33)) can be solved with the LMI toolbox [26]. If both α and β are regarded as decisive variables, Equation (32) and the inequality in Equation ( 33) are nonlinear matrix inequalities, α and β are regarded as scalars between 0 and 1, and the optimization solution is carried out.…”

Section: Proof See Appendix Bmentioning

confidence: 99%

Iterative Learning Control for Actuator Fault Uncertain Systems

Wang¹,

Dong

Chen

et al. 2022

Symmetry

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An iterative learning fault-tolerant control method is designed for an actuator fault intermittent process with simultaneous uncertainties for the system parameters. First, an intermittent fault tolerance controller is designed using 2D system theory, and the iterative learning control (ILC) intermittent process is transformed into a 2D Roesser model. Secondly, sufficient conditions for the controller’s existence are analyzed using the linear matrix inequality (LMI) technique, and the control gain matrices are obtained by convex optimization with LMI constraints. Under these conditions for all additive uncertainties for the system parameters and admissible failures, the controller can ensure closed-loop fault-tolerant performance in both the time and batch directions, and it can also meet the H∞ robust performance level against outside disturbances. Eventually, the algorithm’s computational complexity is analyzed, and the effectiveness of the algorithm is verified by simulation with respect to an injection molding machine model. Compared with traditional ILC laws, which do not consider actuator faults, the proposed algorithm has a better convergence speed and stability when the time-invariant and time-variant actuator faults occur during implementation.

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Section: Proof See Appendix Bmentioning

confidence: 99%

Iterative Learning Control for Actuator Fault Uncertain Systems

Wang¹,

Dong

Chen

et al. 2022

Symmetry

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PD-Type Iterative Learning Control with Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator

Dai

Zhao

et al. 2021

Electronics

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To realize the high-performance load torque tracking of an electric dynamic load simulator system with random measurement noises and strong position disturbances, a PD-type iterative learning control (ILC) algorithm with adaptive learning gains is proposed in this paper. With the principle of system analyzing, a nonlinear discrete state-space model is established. The adaptive learning gains is used to suppress the effects of periodic disturbances and random measurement noises on the load torque tracking performance. A traditional PD feedback controller in parallel with the proposed ILC is designed to stabilize the system and render the ILC converge quickly. The convergence analysis of the proposed control method ensures the stability of the system. Compared with the fixed learning gains, the experiment results show that the proposed control method has better load torque tracking performance and can effectively suppress the adverse effects of periodic and aperiodic disturbances on tracking accuracy.

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

Cited by 2 publications

References 18 publications

Iterative Learning Control for Actuator Fault Uncertain Systems

Iterative Learning Control for Actuator Fault Uncertain Systems

PD-Type Iterative Learning Control with Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator

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