Journal Article10.1109/TSMCB.2002.1018773
Multi-input square iterative learning control with input rate limits and bounds
Brian J. Driessen,Nader Sadegh +1 more
- 01 Aug 2002
- Vol. 32, Iss: 4, pp 545-550
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TL;DR: A new proof that the modified controller produces monotonically decreasing input error norms, with a norm that covers the entire time interval of a learning trial.
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Abstract: We present a simple modification of the iterative learning control algorithm of Arimoto et al. (1984) for the case where the inputs are bounded and time-rate-limited. The Jacobian error condition for monotonicity of input-error, rather than output-error, norms, is specified, the latter being insufficient to assure convergence, as proved herein. To the best of our knowledge, these facts have not been previously pointed out in the iterative learning control literature. We present a new proof that the modified controller produces monotonically decreasing input error norms, with a norm that covers the entire time interval of a learning trial.
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Citations
Iterative learning control design based on composite energy function with input saturation
TL;DR: In this work, an iterative learning control scheme is designed for a class of nonlinear uncertain systems with input saturation based on composite energy function, which consists of both input and state information along the time and iteration axes.
162
Optimization-Based Constrained Iterative Learning Control
TL;DR: This paper implements an interior-point-type method to reduce the number of iterations in the constrained ILC problem, and demonstrates the technique on a prototype wafer stage system with actuator saturation constraints and l2 norm of the tracking error as the objective function.
122
Constrained data-driven optimal iterative learning control
TL;DR: A novel constrained data-driven optimal ILC is developed by minimizing a predesigned objective function and the derived linearized data model is equivalent to the original nonlinear system and reflects the real-time dynamics of the controlled plant, rather than a static approximate model.
91
Unified iterative learning control schemes for nonlinear dynamic systems with nonlinear input uncertainties
TL;DR: This note presents a unified design framework to deal with very general nonlinear input uncertainties in ILC algorithms, and introduces the concept of a dual-loop ILC, which can be designed independently and connected by a proper time-scale separation.
51
Generalized Iterative Learning Control Using Successive Projection: Algorithm, Convergence, and Experimental Verification
TL;DR: A generalized ILC paradigm is proposed, which extends and unifies the scope of existing design frameworks by amalgamating previous task descriptions and embedding system constraints on the input and output.
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Kevin L. Moore,Mary Ann Johnson,Michael J. Grimble +2 more
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Learning Control of Robot Manipulators
TL;DR: The use of function identification and adaptive control algorithms in learning controllers for robot manipulators and the similarities and differences between betterment learning schemes, repetitive controllers and adaptive learning schemes based on integral transforms are discussed.
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Iterative learning control based on quasi-Newton methods
Konstantin Avrachenkov
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TL;DR: In this article, an iterative learning control scheme based on the quasi-Newton method is proposed to improve the performance of the systems working cyclically in a continuous differentiable operator acting in Banach spaces.
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Learning control of process systems with hard input constraints
Chyi-Tsong Chen,Shih-Tien Peng +1 more
TL;DR: Due to significant features of simple structure, efficient algorithm and good performance, the proposed learning control strategy appears to be a promising and practical approach to the intelligent control of process systems subject to hard input constraints.
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