Neural-based adaptive control for nonlinear systems with quantized input and the output constraint

Wu, Jian; Sun, Wei; Su, Shun‐Feng; Xia, Jianwei

doi:10.1016/j.amc.2021.126637

Cited by 26 publications

(12 citation statements)

References 28 publications

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“…Chen et al [43] presented an adaptive fuzzy command filtered backstepping controller to improve tracking performance for uncertain pure-feedback systems. Sun et al [44] proposed a quantized tracking control scheme based on neural networks to ensure the tracking error converge to a small neighbourhood near zero for non-linear systems with quantized input and the output constraint.…”

Section: Lemma 1 [28]: For Any Positive Constants K CImentioning

confidence: 99%

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Full state constrained command filtered output feedback control for the electro‐hydraulic ammunition ramming system

Yin

Nie

Qian

et al. 2022

IET Control Theory & Appl

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A barrier Lyapunov functions-based command filtered output feedback control scheme is presented for the electro-hydraulic ammunition ramming system (EHARS) with position and velocity constraints in this paper. Due to the limitation of compact installation space in ramming mechanism, there remain some immeasurable signals such as the velocity of projectile and pressures in hydro-cylinder; thus, a state observer is designed to estimate the unmeasured states of EHARS. The barrier Lyapunov functions theory is adopted to guarantee position and velocity constraints of projectile are not violated. In order to suppress the complexity of differential explosion in backstepping iteration, command filtered technology and corresponding compensating technique of filtering errors are applied. By the stability theory of Lyapunov, the stability of the closed-loop system and the boundedness of the whole state in EHARS are theoretically proved. Simulation results show prescribed position-tracking accuracy and desired velocity of projectile under different firing angles, which indicates the effectiveness of the proposed method.

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Section: Lemma 1 [28]: For Any Positive Constants K CImentioning

confidence: 99%

“…Sun et al. [44] proposed a quantized tracking control scheme based on neural networks to ensure the tracking error converge to a small neighbourhood near zero for non‐linear systems with quantized input and the output constraint.…”

Section: Controller Designmentioning

confidence: 99%

Full state constrained command filtered output feedback control for the electro‐hydraulic ammunition ramming system

Yin

Nie

Qian

et al. 2022

IET Control Theory & Appl

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“…As a result, in an effort to accomplish this goal, quantizers have been introduced by the research community. [40][41][42][43] In view of these aspects, it is quite realistic and acceptable to quantize the output of PPTVSs in order to make them amenable to practical circumstances. Motivated by the aforementioned discussions, in this work, we pay our attention to both FT boundedness and IO-FT stability issues for the PPTVSs with the external disturbances, immeasurable states and random gain fluctuations.…”

Section: Introductionmentioning

confidence: 99%

Design of proportional integral observer‐based resilient control for periodic piecewise time‐varying systems: The finite‐time case

Satheesh,

Sakthivel,

Aravinth

et al. 2023

Intl J Robust & Nonlinear

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The prime intent of this study is to scrutinize the state estimation and finite‐time (FT) stabilization problems for a class of periodic piecewise time‐varying systems (PPTVSs) in the presence of external disturbances, immeasurable states and gain fluctuations by dint of resilient control. Moreover, the proportional integral observer system is built in line with the output of the PPTVSs to reconstruct the states of the PPTVSs, since the state dynamics of the system are often not measurable. Meanwhile, the output signals are quantized with the use of a logarithmic quantizer prior to being fed into the constructed observer systems. Further, the fluctuations in the controller and observer gain matrices are considered to occur in a random manner and they pursue the pattern of a Bernoulli distributed white sequence. Subsequently, by means of Lyapunov stability theory and the matrix polynomial lemma, the adequate requirements for ascertaining the FT boundedness and IO‐FT stabilization of the addressed system are procured in the context of linear matrix inequalities. Thereupon, on the premise of the established criteria, the gain matrices of the controller and observer are obtained. Ultimately, the simulation results have been showcased including the mass spring damper system to exemplify the efficacy and usefulness of the theoretical conclusions drawn.

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“…How to handle this problem has aroused the research interests of scholars today. For examples, Zhang et al [7] adopted an adaptive controller for the stochastic nonlinear systems with parametric uncertainties to constraint all the states in the bounded compact set; by utilizing the adding a power integrator combined with barrier Lyapunov function and fuzzy control, the control method of high-order uncertain nonlinear systems with full-state constraints is effective in Sun et al [8]; Li et al [9] discussed the adaptive fuzzy finite-time tracking control for a class of switched nonlinear systems; neural network and adaptive laws were adapted to approximate the uncertainty and estimate the weight of neural networks [10][11][12][13][14]. Compared with the research on nonlinear deterministic uncertainty, actual control systems, such as ships, robot arms, and satellites, are difficult to avoid the influence of external noise, which may affect the performance of the systems and even personal safety.…”

Section: Introductionmentioning

confidence: 99%

Adaptive event‐triggered tracking control for a class of stochastic nonlinear systems with full‐state constraints

Jiang

Luo

2022

Asian Journal of Control

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This paper proposes an adaptive event trigger‐based sample‐and‐hold tracking control scheme for a class of strict‐feedback nonlinear stochastic systems with full‐state constraints. By introducing a tan‐type stochastic Barrier Lyapunov function (SBLF) combined with radial basis function neural networks (RBFNNs), which is used to approximate the nonlinear functions in backstepping procedures, an adaptive event‐triggered controller is designed. It is shown with stochastic stability theory that all the states cannot violate their constraints, and Zeno behavior is excluded almost surely. Meanwhile, all the signals of the closed‐loop systems are bounded almost surely and the tracking error converges to an arbitrary small compact set in the fourth‐moment sense. A simulation example is given to show the effectiveness of the control scheme.

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Neural-based adaptive control for nonlinear systems with quantized input and the output constraint

Cited by 26 publications

References 28 publications

Full state constrained command filtered output feedback control for the electro‐hydraulic ammunition ramming system

Full state constrained command filtered output feedback control for the electro‐hydraulic ammunition ramming system

Design of proportional integral observer‐based resilient control for periodic piecewise time‐varying systems: The finite‐time case

Adaptive event‐triggered tracking control for a class of stochastic nonlinear systems with full‐state constraints

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