Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone

Zhou, Qi; Zhao, Shiying; Li, Hongyi; Lu, Renquan; Wu, Chengwei

doi:10.1109/tnnls.2018.2869375

Cited by 304 publications

(92 citation statements)

References 57 publications

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“…Consider the stochastic system (2) and observer (14) satisfying Assumptions 1 to 3. The actual controller (13) with the intermediate controller (7), (9), and (11) and adaptive laws (8), (10), and (12) ensure that all the signals in the closed-loop system is SGUUB. Moreover, the observer errors and the output can be chosen by appropriate design parameters.…”

Section: Stability Analysismentioning

confidence: 99%

“…In the past decades, nonlinear control has been paid considerable attention, such as sliding-model control, 1-3 robust control, [4][5][6][7][8] and adaptive control. [9][10][11][12][13] However, stochastic phenomenon always exists in nonlinear systems and effects nonlinear systems' stability. To overcome this problem, backstepping control approach [14][15][16][17][18][19][20][21][22] was introduced to handle the problem of stabilization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Observer‐based adaptive control for nonlinear strict‐feedback stochastic systems with output constraints

Liang

et al. 2018

Intl J Robust & Nonlinear

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In this paper, an adaptive output-feedback control problem is investigated for nonlinear strict-feedback stochastic systems with input saturation and output constraint. A barrier Lyapunov function is used to solve the problem of output constraint. Then, fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. To overcome the difficulties in designing the control signal in the saturation, we introduce an auxiliary signal in the n + 1th step in the deduction. By combining Nussbaum technique and the adaptive backstepping technique, an adaptive output-feedback control method is developed. The proposed control method not only overcomes the problem of the compensation for the nonlinear term from the input saturation but also overcomes the problem of unavailable state measurements. It is proved that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the effectiveness of the proposed method is verified by the simulation results.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observer‐based adaptive control for nonlinear strict‐feedback stochastic systems with output constraints

Liang

et al. 2018

Intl J Robust & Nonlinear

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“…He et al [18] investigated an adaptive impedance control problem for an n-link robotic manipulator by applying NNs to identify unknown nonlinearities, in which the robot was subject to full state constraints. By employing NNs to approximate the originally designed virtual controllers with unknown nonlinear items, an adaptive state-feedback control strategy was provided in [19] for robot manipulators with dead zone input.…”

Section: Introductionmentioning

confidence: 99%

Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances

et al. 2020

Sci. China Inf. Sci.

Self Cite

228

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This paper investigates the adaptive event-triggered control problem for a class of nonlinear systems subject to periodic disturbances. To reduce the communication burden, a reliable relative threshold strategy is proposed. Fourier series expansion and radial basis function neural network are combined into a function approximator to model suitable time-varying disturbed function of known periods in strict-feedback systems. By combining the Lyapunov stability theory and the backstepping technique, the proposed adaptive control approach ensures that all the signals in the closed-loop system are bounded, and the tracking error can be regulated to a compact set around zero in finite time. Finally, simulation results are presented to verify the effectiveness of the theoretical results.

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“…During the past few decades, the nonlinear uncertain system has been examined intensively owing to its strong application in both theoretical and practical fields [1]. It should be pointed out that stability analysis and controller design problems for nonlinear systems are much more complicated than regular ones [2], because the nonlinear system requires consideration of time delays or disturbances, which will arise in the practice control system [3,4]. Hence, a great number of fundamental notions and theories for nonlinear uncertain systems such as adaptive, fuzzy, state feedback control methods, and so on, have been investigated [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic output feedback control with output quantizer for nonlinear uncertain T‐S fuzzy systems with multiple time‐varying input delays and unmatched disturbances

Zhengwei

WangHongbin

WangHongrui

et al. 2019

Asian Journal of Control

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The dynamic output feedback control problem with output quantizer is investigated for a class of nonlinear uncertain Takagi‐Sugeno (T‐S) fuzzy systems with multiple time‐varying input delays and unmatched disturbances. The T‐S fuzzy model is employed to approximate the nonlinear uncertain system, and the output space is partitioned into operating regions and interpolation regions based on the structural information in the fuzzy rules. The output quantizer is introduced for the controller design, and the dynamic output feedback controller with output quantizer is constructed based on the T‐S fuzzy model. Stability conditions in the form of linear matrix inequalities are derived by introducing the S‐procedure, such that the closed‐loop system is stable and the solutions converge to a ball. The control design conditions are relaxed and design flexibility is enhanced because of the developed controller. By introducing the output‐space partition method and S‐procedure, the unmatched regions between the system plant and the controller caused by the quantization errors can be solved in the control design. Finally, simulations are given to verify the effectiveness of the proposed method.

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Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone

Cited by 304 publications

References 57 publications

Observer‐based adaptive control for nonlinear strict‐feedback stochastic systems with output constraints

Observer‐based adaptive control for nonlinear strict‐feedback stochastic systems with output constraints

Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances

Dynamic output feedback control with output quantizer for nonlinear uncertain T‐S fuzzy systems with multiple time‐varying input delays and unmatched disturbances

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