A Data-Driven Constrained Norm-Optimal Iterative Learning Control Framework for LTI Systems
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The estimation of the system's impulse response using input/output measurements from previous iterations is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints.Abstract:
This brief presents a data-driven constrained norm-optimal iterative learning control framework for linear time-invariant systems that applies to both tracking and point-to-point motion problems. The key contribution of this brief is the estimation of the system's impulse response using input/output measurements from previous iterations, hereby eliminating time-consuming identification experiments. The estimated impulse response is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints. Experimental validation on a linear motor positioning system shows the ability of the proposed data-driven framework to: 1) achieve tracking accuracy up to the repeatability of the test setup; 2) minimize the rms value of the tracking error while respecting the actuator input constraints; 3) learn energy-optimal system inputs for point-to-point motions.read more
Citations
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References
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TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
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Iterative learning control and repetitive control for engineering practice
TL;DR: In this article, the authors discuss linear iterative learning and repetitive control, presenting general purpose control laws with only a few parameters to tune The method of tuning them is straightforward, maki
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Iterative learning control for discrete-time systems with exponential rate of convergence
TL;DR: An algorithm for iterative learning control is proposed based on an optimisation principle used by other authors to derive gradient-type algorithms and has potential benefits which include realisation in terms of Riccati feedback and feedforward components.
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Iterative Learning Control: An Optimization Paradigm
TL;DR: This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts and demonstrates that there are new algorithms that are capable of incorporating input and output constraints.
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Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems
TL;DR: In this paper, the authors present a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iterationdomain stochastic uncertainty.