Constrained Iterative Learning Control for Linear Time-Varying Systems With Experimental Validation on a High-Speed Rack Feeder

Chu, Bing; Rauh, Andreas; Aschemann, Harald; Rogers, Eric; Owens, D.H.

doi:10.1109/tcst.2021.3123744

Cited by 10 publications

(3 citation statements)

References 37 publications

(52 reference statements)

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“…However, due to hardware limitations of actuators, achieving this requirement can be challenging when the initial state error is sufficiently small. In [18], a boundary layer strategy is utilized to make ILC more robust under initial state errors. However, this approach is difficult to directly apply to multi-axis trajectory tracking tasks, as multi-degree-of-freedom systems lack boundary layer function derivatives.…”

Section: Introductionmentioning

confidence: 99%

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Yu,

Ma,

Wang

et al. 2024

Preprint

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This paper aims to investigate a chattering-free adaptive iterative learning strategy for trajectory tracking tasks on linear-motor-driven-gantry-stages with initial state errors. The reduction mechanism of the proposed parameter learning law effectively addresses initial state tracking errors, unmodeled system dynamics, and external disturbances during the iterative process. Furthermore, to mitigate the oscillation issues caused by traditional iterative learning control signals, this paper adopts the approximation for the sign function. However, this approximation introduces the non-negative definite problems. Therefore, this paper introduces a novel analysis method based on contraction mapping and composite energy functions in the Lyapunov-like theory. This method rigorously proves the boundedness and convergence during the entire iteration under non-negative definite problems with initial state errors. The effectiveness of the proposed approach is validated through experiments on a linear motor platform.

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Section: Introductionmentioning

confidence: 99%

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Yu,

Ma,

Wang

et al. 2024

Preprint

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“…By employing the tracking information of preceding iterations, ILC adjusts the control input of subsequent iterations to make the tracking error reduce. One of the most conspicuous strengths of ILC is that less prior knowledge of the dynamical system is required in design process, which makes ILC popular in both theoretical and applicable fields with repetitive cases such as robot [11][12][13], high speed rack feeder [14], high speed train [15][16][17], pneumatic artificial muscle [18], and air conditioning system [19].…”

Section: Introductionmentioning

confidence: 99%

“…This would lead to the time interval varies from iteration to iteration. Thus, some ILC designs for dynamical systems with iteration dependent interval have been investigated [14,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. And the authors in the work [39] have provided a review of the model and ILC method of the iteration dependent interval systems.…”

Section: Introductionmentioning

confidence: 99%

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

Wei

Wang

et al. 2023

CAAI Trans on Intel Tech

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For linear time varying (LTV) multiple input multiple output (MIMO) systems with vector relative degree, an open‐closed‐loop iterative learning control (ILC) strategy is developed in this article, where the time interval of operation is iteration dependent. To compensate the missing tracking signal caused by iteration dependent interval, the feedback control is introduced in ILC design. As the tracking signal of many continuous iterations is lost in a certain interval, the feedback control part can employ the tracking signal of current iteration for compensation. Under the assumption that the initial state vibrates around the desired initial state uniformly in mathematical expectation sense, the expectation of ILC tracking error can converge to zero as the number of iteration tends to infinity. Under the circumstance that the initial state varies around the desired initial state with a bound, as the number of iteration tends to infinity, the expectation of ILC tracking error can be driven to a bounded range, whose upper bound is proportional to the fluctuation. It is revealed that the convergence condition is dependent on the feedforward control gains, while the feedback control can accelerate convergence speed by selecting appropriate feedback control gains. As a special case, the controlled system with integrated high relative degree is also addressed by proposing a simplified iteration dependent interval based open‐closed‐loop ILC method. Finally, the effectiveness of the developed iteration dependent interval based open‐closed‐loop ILC is illustrated by a simulation example with two cases on initial state.

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Constrained ILC Design

2023

Iterative Learning Control Algorithms and Experimental Benchmarking

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Constrained Iterative Learning Control for Linear Time-Varying Systems With Experimental Validation on a High-Speed Rack Feeder

Cited by 10 publications

References 37 publications

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

Constrained ILC Design

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