Iterative learning control for non‐repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults

Jin, Xu

doi:10.1002/acs.2734

Cited by 54 publications

(21 citation statements)

References 41 publications

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“…Researchers worked hard to solve the initial problem of ILC for long [1], and only a few results were reported [2], [16]- [19], including time-varying boundary layer, initial rectifying action, error-tracking strategy, and so on. As far as the initial problem of robot ILC is concerned, the related results are very few [20], [21]. Jin used the initial rectifying action to handle the initial problem of robotic systems [20].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Adaptive Learning Control for Robot Manipulators With Arbitrary Initial Errors

Yan

Cai

2019

IEEE Access

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In this paper, a neural network-based adaptive iterative learning control scheme is developed to solve the trajectory tracking problem for rigid robot manipulators with arbitrary initial errors. Time-varying boundary layers are used to relax the zero initial error condition which must be observed in traditional iterative learning control design, and adaptive learning neural networks are constructed to approximate uncertainties in robotic systems, whose optimal weights are estimated by using partial saturation difference learning method. For arbitrary bounded initial state errors, the tracking error of robot manipulators will asymptotically converge to a tunable residual set as the iteration number increases. An illustrative example and the comparisons are provided to demonstrate the effectiveness of the proposed neural network-based adaptive iterative learning control scheme.INDEX TERMS Iterative learning control, neural networks, robot manipulators, adaptive learning control.

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Section: Introductionmentioning

confidence: 99%

Neural Network-Based Adaptive Learning Control for Robot Manipulators With Arbitrary Initial Errors

Yan

Cai

2019

IEEE Access

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“…Note also that the desired trajectories x d, k ( t ) and y d, k ( t ) are iteration dependent. This extends the applications of most traditional ILC algorithms, which require identical reference trajectories over the iteration domain . At the start of each operation, ie, t = 0 at the k th iteration, x k (0) and y k (0) can be any values, which means they do not need to satisfy the common assumptions regarding the initial conditions in the ILC literature such as i.i.c.…”

Section: Discussion On Output Constraints Nonrepetitive Trajectoriesmentioning

confidence: 93%

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Jin

2019

Intl J Robust & Nonlinear

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Summary In this paper, we present a novel parametric iterative learning control (ILC) algorithm to deal with trajectory tracking problems for a class of nonlinear autonomous agents that are subject to actuator faults. Unlike most of the ILC literature, the desired trajectories in this work can be iteration dependent, and the initial position of the agent in each iteration can be random. Both parametric and nonparametric system unknowns and uncertainties, in particular the control input gain functions that are not fully known, are considered. A new type of universal barrier functions is proposed to guarantee the satisfaction of asymmetric constraint requirements, feasibility of the controller, and prescribed tracking performance. We show that under the proposed algorithm, the distance and angle tracking errors can uniformly converge to an arbitrarily small positive number and zero, respectively, over the iteration domain, beyond a small user‐prescribed initial time interval in each iteration. A numerical simulation is presented in the end to demonstrate the efficacy of the proposed algorithm.

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“…The situation where the tracking tasks slowly vary with respect to iteration was tackled earlier in [2], and the robustness has been characterized for D-type, PD-type and PID-type learning algorithms. The non-equal task problem was addressed in the framework of AILC [14]- [16]. It is found that for the parameter learning, the reference signals are allowed to be vary with iteration, and the controller design can be carried out with ease.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Iterative Learning Control for Tracking Trajectories With Non-Equal Trail Lengths and Initial Errors

et al. 2021

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Both non-equal trail lengths and non-zero initial errors are practical challenges to learning control of robotic and mechatronic systems. Iterative learning to update input is still desired, because of the repetitive motion nature of the controlled objects. This paper concerns with the adaptive iterative learning control method for performing non-identical tasks. The time scaling technique is applied to normalize nonequal trail lengths, while the error-tracking approach is adopted for coping with initial errors. Theoretical results for performance analysis are presented in detail. The uniform convergence of the tracking error is examined, while boundedness of the variables in the closed-loop is characterized. It is shown that the fully-saturated learning algorithm plays an important role in assuring uniform boundedness of the control input. The proposed control design method does not require the magnitude transformation, and removes the assumption of identical initial conditions. The time scaling technique is verified to be effective in assuring the expected performance, for tracking tasks with non-equal trail lengths and initial errors.INDEX TERMS Uniform convergence, initial condition problem, non-equal trial lengths, learning algorithms.

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Iterative learning control for non‐repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults

Cited by 54 publications

References 41 publications

Neural Network-Based Adaptive Learning Control for Robot Manipulators With Arbitrary Initial Errors

Neural Network-Based Adaptive Learning Control for Robot Manipulators With Arbitrary Initial Errors

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Adaptive Iterative Learning Control for Tracking Trajectories With Non-Equal Trail Lengths and Initial Errors

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