Adaptive rejection of multi-periodic disturbances for a class of linear systems

Chen, P.; Sun, Mingxuan; Yan, Qingdong

doi:10.1080/00207179.2016.1153152

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…Both iterative learning control (ILC) and repetitive control are intelligent control techniques pursuing high-precision tracking performance according to learning strategy. The former is suitable for dealing with repetitive tasks over a finite time interval [1]- [3], while the latter is often used to solve the problems of periodic reference tracking or periodic disturbance rejection [4]- [7]. In the past decades, the trajectorytracking control of robot manipulators has attracted a lot of attention for their wide applications in numerous industrial situations such as assembly lines, rehabilitation processes and so on.…”

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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“…For most periodic systems, external periodic disturbances are unavoidable. Assumption 3 is the matching condition, often appearing in other literatures, such as [32,[41][42][43].…”

Section: Problem Statementmentioning

confidence: 99%

“…For most periodic systems, external periodic disturbances are unavoidable. Assumption 3 is the matching condition, often appearing in other literatures, such as [32, 41–43]. The matching condition means that the various disturbances and uncertainties enter the system via the same channels as the control inputs, which is necessary for the disturbances and uncertainties to be rejected by the control inputs.…”

Section: Problem Statementmentioning

confidence: 99%

Estimation and rejection of periodic disturbance in a preview repetitive control system

Lan,

Liu

2023

Asian Journal of Control

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This paper deals with the problem of two‐dimensional (2D) system‐based preview repetitive control (PRC) with external periodic disturbance for a class of continuous‐time linear systems. First, a repetitive learning‐based estimator (RLE) is incorporated into the controller block to compensate for the periodic disturbance. Next, the PRC law with repetitive learning compensation disturbance is obtained by combining the observer and RLE. Then, we combine the 2D system theory with the linear matrix inequality (LMI) method to derive sufficient conditions to ensure the robustness and stability of the closed‐loop system. Finally, numerical simulations verify the superiority of the proposed method.

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“…Control derived by the pseudospectral method will be numerically revealed using MATLAB via a general optimization program [27]. By comparison, recall classical compensators and those designed to control flexible systems [28][29][30][31][32][33][34] ubiquitously command maneuvers in the direction of the desired end state. To validate this claim, reviews of classical compensators [35] and frequency domain shaping filters [36] were performed by Wie and Agrawal, respectively.…”

Section: Of 18mentioning

confidence: 99%

Optimization Provenance of Whiplash Compensation for Flexible Space Robotics

Sands

2019

Aerospace

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Automatic controls refer to the application of control theory to regulate systems or processes without human intervention, and the notion is often usefully applied to space applications. A key part of controlling flexible space robotics is the control-structures interaction of a light, flexible structure whose first resonant modes lie within the bandwidth of the controller. In this instance, the designed-control excites the problematic resonances of the highly flexible structure. This manuscript reveals a novel compensator capable of minimum-time performance of an in-plane maneuver with zero residual vibration (ZV) and zero residual vibration-derivative (ZVD) at the end of the maneuver. The novel compensator has a whiplash nature of first commanding maneuver states in the opposite direction of the desired end state. For a flexible spacecraft simulator (FSS) free-floating planar robotic arm, this paper will first derive the model of the flexible system in detail from first principles. Hamilton's principle is augmented with the adjoint equation to produce the Euler-Lagrange equation which is manipulated to prove equivalence with Newton's law. Extensive efforts are expended modeling the free-free vibration equations of the flexible system, and this extensive modeling yields an unexpected control profile-a whiplash compensator. Equations of motion are derived using both the Euler-Lagrange method and Newton's law as validation. Variables are then scaled for efficient computation. Next, general purposed pseudospectral optimization software is used to seek an optimal control, proceeding afterwards to validate optimality via six theoretical optimization necessary conditions: (1) Hamiltonian minimization condition; (2) adjoint equations; (3) terminal transversality condition; (4) Hamiltonian final value condition; (5) Hamiltonian evolution equation; and lastly (6) Bellman's principle. The results are novel and unique in that they initially command full control in the opposite direction from the desired end state, while no such results are seen using classical control methods including classical methods augmented with structural filters typically employed for controlling highly flexible multi-body systems. The manuscript also opens an interesting question of what to declare when the six optimality necessary conditions are not necessarily in agreement (we choose here not to declare finding the optimal control, instead calling it suboptimal).Aerospace 2019, 6, 93 2 of 18 highly flexible structure. In order to optimize the control of such challenging systems, a brief review of mechanics and ubiquitous control techniques is warranted.Mechanical motion of mass in six degrees of freedom is completely described by separate treatment of three degrees of freedom of translation plus three additional degrees of freedom of rotation as articulated in the year 1830 [1] and expanded and modernized over the following two centuries [2][3][4][5][6][7][8][9][10][11][12][13]. Translational degrees of freedom are governed by Newton's law, whil...

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Adaptive rejection of multi-periodic disturbances for a class of linear systems

Cited by 14 publications

References 16 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

Estimation and rejection of periodic disturbance in a preview repetitive control system

Optimization Provenance of Whiplash Compensation for Flexible Space Robotics

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