Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

Jin, Xu

doi:10.1002/rnc.3899

Cited by 51 publications

(35 citation statements)

References 42 publications

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“…The former makes use of two or more modules that are operated in parallel. For example,

\dot{x} = f (x) + \sum_{j = 1}^{q + 1} g_{j} (x) u_{j},

where the q +1 actuators are active during operation as studied in References , and . Such a way, on the one hand, results in long‐term wear‐and‐tear of standby components.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…If actuator faults are not well handled, they would result in constraint violation. Motivated by this, a pair of solutions was provided in References , and . To enhance transient performance of output tracking vs failures, the prescribed performance control (PPC) technology was newly introduced into the FTC design in Reference .…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the existing results, the established one is of the following superiorities. Only one monitoring function is constructed to implement failure detection, contrary to the multiple monitoring functions designed in References and . There is no need to compute analytic derivatives of virtual control signals, in contrast to References , , and . No adaptive techniques or approximating structures (eg, neuro/fuzzy systems) are involved in the control design, despite model uncertainties. …”

Section: Introductionmentioning

confidence: 99%

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Supervisory switching‐based prescribed performance control of unknown nonlinear systems against actuator failures

Zhang

Yang

2020

Intl J Robust & Nonlinear

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Summary This article studies the fault‐tolerant control problem for unknown nonlinear strict‐feedback systems subject to actuator failures yet with dynamic redundancies. The prescribed performance control methodology is newly combined with a modification‐based supervisory switching strategy to solve the problem. To implement failure detection, the performance function is properly modified to synthesize a monitoring function to supervise the behavior of an error variable. Once a failure is detected, the current actuator is shut down and the backup actuator is switched in to execute the reconfigured control command. Compared with the existing results, (1) the postfailure and postswitching tracking performance is improved, other than uniform ultimate boundedness and (2) the dependence on extra robust control schemes (eg, adaptive or approximating structures) to deal with model uncertainties or the need to compute analytic derivatives of virtual control signals in the backstepping design is eliminated.

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“…The former makes use of two or more modules that are operated in parallel. For example,

\dot{x} = f (x) + \sum_{j = 1}^{q + 1} g_{j} (x) u_{j},

where the q +1 actuators are active during operation as studied in References , and . Such a way, on the one hand, results in long‐term wear‐and‐tear of standby components.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervisory switching‐based prescribed performance control of unknown nonlinear systems against actuator failures

Zhang

Yang

2020

Intl J Robust & Nonlinear

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“…In practice, nonlinear systems are usually subjected to output constraints for reasons of performance specifications and/or safety, in addition to the stabilization task . During operation, any violation of the constraint may lead to performance degradation, system failure, or even unexpected hazards.…”

Section: Introductionmentioning

confidence: 99%

“…It should be pointed out that most of the existing results with either the logarithm ‐type BLF or the tangent ‐type BLF are mainly based on the backstepping design and focused only on systems in the strict‐feedback form, which is a special case of system with p i =1 for all i =1,…, n . Therefore, the schemes in other works are inapplicable to system on account of the inherent nonlinearities resulting from both p i 's and ϕ i ( t , x , u )'s. For example, consider the planar system

{\overset{x}{true}}_{1} = x_{2}^{p_{1}} + ϕ_{1} false(t, x, u false)

{\overset{x}{true}}_{2} = u

, y = x 1 with p 1 =5 and

ϕ_{1} false(t, x, u false) = x_{1}^{3}

.…”

Section: Introductionmentioning

confidence: 99%

A new approach to stabilization of high‐order nonlinear systems with an asymmetric output constraint

Chen

2019

Intl J Robust & Nonlinear

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Summary This paper is concerned with the problem of state feedback stabilization for a class of high‐order nonlinear systems with an asymmetric output constraint. A novel asymmetric barrier Lyapunov function (BLF) is first proposed by deliberating the characteristics of the system nonlinearities. Then, the presented BLF, together with a skillful manipulation of sign functions, is utilized to delicately revamp the technique of adding a power integrator, thereby developing a systematic approach that guides us in constructing a continuous state feedback stabilizer and preventing the violation of a pre‐specified asymmetric output constraint during operation. The novelty of this paper is attributed to the development of a unified method that is able to simultaneously tackle the problem of stabilization for high‐order nonlinear systems with and without output constraints in a constructive fashion, without changing the controller structure. An illustrative example is presented to demonstrate the superiority of the proposed approach.

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Disturbance observer‐based adaptive boundary iterative learning control for a rigid‐flexible manipulator with input backlash and endpoint constraint

Zhou

Wang

Tian

et al. 2020

Adaptive Control & Signal

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Summary In this article, an observer‐based adaptive boundary iterative learning control law is developed for a class of two‐link rigid‐flexible manipulator with input backlash, the unknown external disturbance, and the endpoint constraint. To tackle the backlash nonlinearities and ensure the vibration suppression, the disturbance observers based upon the iterative learning conception are considered in the adaptive boundary control design. A barrier Lyapunov function is incorporated with boundary control law to restrict the endpoint state. Based on the defined barrier composite energy function, the tracking angle error convergence of the rigid part is guaranteed, and the vibrations of the flexible part are suppressed through the rigorous analysis. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed control.

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Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

Cited by 51 publications

References 42 publications

Supervisory switching‐based prescribed performance control of unknown nonlinear systems against actuator failures

Supervisory switching‐based prescribed performance control of unknown nonlinear systems against actuator failures

A new approach to stabilization of high‐order nonlinear systems with an asymmetric output constraint

Disturbance observer‐based adaptive boundary iterative learning control for a rigid‐flexible manipulator with input backlash and endpoint constraint

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