Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation

Li, Yulin; Niu, Ben; Zong, Guangdeng; Zhao, Jinfeng; Zhao, Xudong

doi:10.1080/00207721.2021.1943562

Cited by 135 publications

(45 citation statements)

References 70 publications

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“…where g is a nonzero constant, and b 0 is a positive constant, then ∫ t 0 (gN(𝜍) + 1) ̇𝜍d𝜍, N(⋅) and V(⋅) are bounded on [0, t f ). Lemma 2 (21). Considering the following command filter…”

Section: Preliminariesmentioning

confidence: 99%

“…To solve this problem, some modified backstepping techniques, such as dynamic surface control (DSC) [17][18][19] and command filtering control (CFC), [20][21][22][23] have been proposed. In Reference 18, an adaptive neural DSC scheme was proposed for nonlinear systems, where first-order filters were used to approximate derivatives of virtual control signals.…”

Section: Introductionmentioning

confidence: 99%

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Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Chen

Niu

Zhang

et al. 2022

Intl J Robust & Nonlinear

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In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Chen

Niu

Zhang

et al. 2022

Intl J Robust & Nonlinear

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“…Optimal control, 1 which refers to design an optimal control law to optimize a performance index function (also called cost function) consisting of the state and input, has been extensively studied in recent years [2][3][4][5][6][7] due to its wide applications in vehicles, industrial processes and robotics. 8 To obtain an optimal solution, the Hamilton-Jaccobi-Bellman (HJB) equation is required to be solved.…”

Section: Introductionmentioning

confidence: 99%

Quantized optimal output feedback control and optimal triggering signal co‐design for unknown discrete‐time nonlinear systems

Han

Hao

et al. 2022

Optim Control Appl Methods

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A problem of optimizing the cost function incorporated into the transmission cost is proposed for completely unknown discrete-time systems in this article. At first, a switched system containing two modes is formulated based on whether the triggering is executed or not, and the corresponding cost function and output feedback control law are also derived. By giving the formulated switched system, the triggering signal turns into the switching signal and the proposed problem is converted into an optimal control problem for the formulated discrete-time switched system. Secondly, a model network is presented to approximate the system dynamic since the dynamic model is completely unknown. Thirdly, in view of ADP-based optimal control methods of switched systems, critic and actor networks are applied respectively to approximate the optimal cost function and the optimal output feedback control law, and a PI-based online learning method is proposed. When weights of critic and actor networks are obtained and implemented through the proposed online learning method, the optimal triggering signal is derived. Fourthly, the convergent property is given to guarantee the obtained results are optimal. At last, the validity of the proposed method is shown by simulation results of two examples.

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“…Unfortunately, the strategies mentioned in [ 6 , 7 ] are only appropriate to dynamic systems with a single input. Several recent control methods, such as fuzzy logic controllers [ 8 , 9 ], fault-tolerant controller [ 10 ], TSMCs [ 11 , 12 ] and FnTC [ 13 , 14 , 15 , 16 ] have been introduced. The outstanding features of such controllers include the ability to reject disturbances, robustness, finite-time, and fast convergence.…”

Section: Introductionmentioning

confidence: 99%

A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators

Truong

2022

Sensors

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Through this article, we present an advanced prescribed performance-tracking control system with finite-time convergence stability for uncertain robotic manipulators. It is therefore necessary to define a suitable performance function and error transformation to guarantee a prescribed performance within a finite time. Following the definitions mentioned, a modified integral nonlinear sliding-mode hyperplane is constructed from the transformed errors. By using the designed nonlinear sliding-mode surface and the super-twisting reaching control law, an advanced approach to the prescribed performance control was formed for the trajectory tracking control of uncertain robotic manipulators. The proposed controller exhibits improved properties, including estimated convergence speed and a predefined upper and lower limit for maximum overshoot during transient responses. Furthermore, the maximum allowable size of the control errors at the steady-state can be predefined and these errors will inevitably converge to zero within a finite time, while the proposed controller can provide a smooth control torque without the loss of its robustness. It is shown that the proposed control system is globally stable and convergent over a finite time. A comprehensive analysis of the effectiveness of the proposed control algorithm was already conducted via the simulation of an industrial robot manipulator.

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Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation

Cited by 135 publications

References 70 publications

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Quantized optimal output feedback control and optimal triggering signal co‐design for unknown discrete‐time nonlinear systems

A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators

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