HONN-Based Adaptive ILC for Pure-Feedback Nonaffine Discrete-Time Systems With Unknown Control Directions

Xu, Qing‐Yuan; Li, Xiaodong

doi:10.1109/tnnls.2019.2900278

Cited by 36 publications

(21 citation statements)

References 44 publications

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“…On the other hand, similar discrete adaptive ILC approaches have also been reported in [3], [19], [20]. Nevertheless, it should be noted that the number of tuning parameters and the complexity of controller structure will increase by using Nussbaum-type functions in the adaptive ILC algorithms [3], [13]- [20]. Moreover, one can notice that the transient performance is hard to ensure because of the oscillating gain produced by the Nussbaum-type function based-adaptive ILC control [14].…”

Section: Introductionsupporting

confidence: 60%

“…Using few system model knowledge, ILC schemes can iteratively learn and compensate the iterationdependent/independent uncertainties and gain ultimately precise trajectory tracking. Therefore, great interest has been imposed on ILC in the control design of industrial process such as robotic manipulators, chemical plants, lower limb exoskeleton and so on [2], [3], [29]. Then, as we expected, a tremendous amount of ILC algorithms are constantly emerging due to application demand.…”

Section: Introductionmentioning

confidence: 99%

“…Then, as we expected, a tremendous amount of ILC algorithms are constantly emerging due to application demand. As pointed out in [3], the limitations of contracting mapping-based ILC, that gradually appeared as the controlled systems encountered non-Lipschitz nonlinearities and random uncertainties in initial value, desired reference and disturbance, give rise to adaptive ILC. Generally, adaptive ILC can well tackle with these uncertainties and non-Lipschitz nonlinearities with the help of the so-called composite energy function to do the The associate editor coordinating the review of this manuscript and approving it for publication was Wojciech Paszke.…”

Section: Introductionmentioning

confidence: 99%

“…Then, with the successful application of Nussbaum-type function, several works discussed the adaptive ILC design for uncertain continuous systems were achieved in [14]- [18]. On the other hand, similar discrete adaptive ILC approaches have also been reported in [3], [19], [20]. Nevertheless, it should be noted that the number of tuning parameters and the complexity of controller structure will increase by using Nussbaum-type functions in the adaptive ILC algorithms [3], [13]- [20].…”

Section: Introductionmentioning

confidence: 99%

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Design of Adaptive ILC for 2-D FMM Systems With Unknown Control Directions

Wei

Wan

2019

IEEE Access

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Among the existing adaptive iterative learning control (ILC) work concerning unknown control direction problem, no result is available for the two-dimensional (2-D) dynamical systems. In this paper, an adaptive ILC is developed for a class of 2-D dynamical systems described by the Fornasini-Marchesini model (FMM). Notably, the 2-D FMM system under investigation possesses not only random uncertainties in boundary condition and reference trajectory, but also unknown control direction. Even so, the constructed adaptive ILC combining with a modification mechanism can still guarantee the precise tracking of iteration-variant reference trajectory and the boundedness of all the system signals, as iteration number k tends to infinity. Theoretical analysis and simulation study are given to demonstrate the effectiveness of the developed adaptive ILC. INDEX TERMS Two-dimensional (2-D) dynamical systems, unknown control direction, adaptive iterative learning control (ILC), the Fornasini-Marchesini model (FMM).

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Section: Introductionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Adaptive ILC for 2-D FMM Systems With Unknown Control Directions

Wei

Wan

2019

IEEE Access

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“…The weightage gain W can be estimated such thatŴ → W * by various adaptive laws in the control literature [43]- [45]. In this paper, we intend to exploit a high-order neural network as in [46] and augment the adaptation law with a covariance resetting feature. Based on the selected approximator, the basis vector…”

Section: Adaptive Backstepping-based Control Designmentioning

confidence: 99%

A Neural Network-Based Adaptive Backstepping Control Law With Covariance Resetting for Asymptotic Output Tracking of a CSTR Plant

2020

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A robust nonlinear adaptive controller merging a backstepping approach with neural networks is proposed for a nonlinear non-affine model. The work presented here is evaluated on a complex uncertain model of a continuous stirred tank reactor plant including an unknown varying parameter that enters the complexity model. By exploiting NN and adaptive backstepping approximation methods, an equivalent adaptive NN controller is constructed to achieve robust asymptotic output tracking control. The robustness to uncertainties as well as the lack of informative process data is the main enhancement of this work. This is attained through the implementation of the covariance resetting algorithm in the least square estimation of the NN weight tuning algorithm. The proposed novel control algorithm has been analyzed using Lyapunov analysis. In addition to excellent output trajectory tracking performance, the proposed approach has a profound benefit in terms of substantially lower control effort in comparison to the established work in the literature. In terms of applications in the petrochemical industry, lower control effort can translate to a more energy-efficient actuator, leading to lower costs over a long-run operation. The proposed method's feasibility for chemical process control was shown via numerical simulation.

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Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations

Sun

Zhan

et al. 2022

Adaptive Control & Signal

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Summary Initial condition problem is crucial to a conventional iterative learning control (ILC) scheme, which steers the tracking error from arbitrary initial value to zero in the time steps equaling to the relative degree of the system undertaken. The implementation may be difficult, due to the practical limitation for the control amplitude. This paper presents an error‐tracking approach to discrete‐time adaptive ILC designs for tracking nonidentical tasks in the presence of initial repositioning errors, which may be quite large. A prototype iterative learning algorithm is derived for estimating the time‐varying unknowns, and the saturation is introduced for assuring the estimates to keep away from zero. A key technical lemma, tailored for the analysis purpose in the iteration domain, is presented and applied to analysis of the ILC scheme. The tracking performance of the closed‐loop system is evaluated and explored in detail. It is shown that the perfect tracking for the error between the tracking error and the desired error is achieved at every time instant, while the input and output signals remain bounded. By the simulation example, the proposed scheme is verified to be applicable to tracking tasks without restriction on initial repositioning.

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HONN-Based Adaptive ILC for Pure-Feedback Nonaffine Discrete-Time Systems With Unknown Control Directions

Cited by 36 publications

References 44 publications

Design of Adaptive ILC for 2-D FMM Systems With Unknown Control Directions

Design of Adaptive ILC for 2-D FMM Systems With Unknown Control Directions

A Neural Network-Based Adaptive Backstepping Control Law With Covariance Resetting for Asymptotic Output Tracking of a CSTR Plant

Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations

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