Composite backstepping control with finite-time convergence

Yi, Sheng; Wang, Jiang; Li, Bin

doi:10.1016/j.ijleo.2017.05.070

Cited by 17 publications

(5 citation statements)

References 27 publications

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“…In order to make a comparison effect, linear tracking differentiator (LTD) and finite-time convergent differentiator (FTCD) [30] were selected in the simulation and compared with the tracking differentiator (30) proposed in this paper. Figure 12 shows the comparison of differential signal tracking effects and errors of different differentiators.…”

Section: Simulation Studymentioning

confidence: 99%

Output Feedback Recursive Dynamic Surface Control with Antiwindup Compensation

et al. 2021

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Actuator saturation phenomenon often exists in the actual control system, which could destroy the closed-loop performance of the system and even lead to unstable behavior. Our main contribution is to provide an antiwindup recursive dynamic surface control (RDSC) for a discrete-time system with an unknown state and actuator saturation. The fuzzy compensator is added to perform as an active disturbance rejection term in the feedforward path to avoid windup caused by input saturation. To construct output feedback control, the system is transformed into the form of pure-feedback and an improved HOSM observer is designed to carry out future output prediction. Based on which RDSC is synthesized, only one fuzzy logic system (FLS) is used and the controller singularity is completely avoided. In addition, the simulation and numeral examples using a continuous stirred tank reactors (CSTRs) system with actuator saturation are provided and the results show that the strategy owns good robustness and effectively compensates for the disturbance caused by actuator saturation in the presence of a discrete-time system with an unmeasurable state.

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Section: Simulation Studymentioning

confidence: 99%

Output Feedback Recursive Dynamic Surface Control with Antiwindup Compensation

et al. 2021

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“…Command-filtered backstepping was studied by using a high-order low-pass filter (Dong et al, 2012). Meanwhile, sliding-mode differentiator provides another effective way to deal with this problem (Yi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

Yan

Zhou

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper investigates the issue of finite-time tracking control for multiple-input–multiple-output nonlinear systems subject to uncertainties and full state constraints. To deal with full state constraints directly, integral barrier Lyapunov functionals (iBLF) are introduced. By using finite-time stability theory, an iBLF-based adaptive finite-time neural control scheme is presented. To solve the problem of “explosion of complexity” in the design of traditional backstepping control, a new finite-time convergent differentiator is presented. Through stability analysis, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded, the finite time convergence can be guaranteed, and the state constraints are never violated. Finally, the attitude tracking simulations for an autonomous airship are conducted to verify the effectiveness of the proposed scheme.

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“…Recently, the backstepping control scheme for uncertain nonlinear systems has attracted much attention. To reduce the computational complexity in traditional backstepping control design, many fruitful control methods have been studied, such as dynamic surface control [1], command filter backstepping [2,3], and finite-time convergent differentiator (FTCD)-based backstepping [4,5]. Moreover, to compensate for the uncertainties of nonlinear systems, fuzzy logic systems (FLSs)/neural networks (NNs) were usually introduced in backstepping design [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered adaptive output‐feedback control for nonlinear state‐constrained systems using tangent‐type nonlinear mapping

Yan

Zhou

Xie

et al. 2021

Asian Journal of Control

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This paper investigates the problem of event‐triggered adaptive output‐feedback control for multi‐input and multi‐output (MIMO) uncertain nonlinear systems with time‐varying full state constraints. A tangent‐type nonlinear mapping function is proposed to transform the state‐constrained system into a new one free of constraints, such that the time‐varying full state constraints are never violated. Radial basis function neural networks are introduced to compensate for the unknown functions. A state observer is established to estimate the unmeasured states. A suitable event‐triggering rule is presented to determine when to transmit control laws. Through Lyapunov analyses, all closed‐loop signals are proved to be semiglobally uniformly ultimately bounded, and time‐varying full state constraints are never violated. Finally, simulations are presented to evaluate the efficacy of the proposed approach.

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Composite backstepping control with finite-time convergence

Cited by 17 publications

References 27 publications

Output Feedback Recursive Dynamic Surface Control with Antiwindup Compensation

Output Feedback Recursive Dynamic Surface Control with Antiwindup Compensation

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

Event‐triggered adaptive output‐feedback control for nonlinear state‐constrained systems using tangent‐type nonlinear mapping

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