Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

Chang, Wanmin; Tong, Shaocheng; Li, Yongming

doi:10.1007/s00521-016-2425-2

Cited by 35 publications

(24 citation statements)

References 43 publications

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“…By using small-gain approaches, Zhang et al [20] and Su et al [21] designed adaptive fuzzy backstepping schemes for uncertain nonlinear systems. Moreover, many other neural and fuzzy backstepping controllers have been recently employed in a variety of applications, ranging from robotic manipulators [22][23][24] to spacecrafts [25][26][27]. However, although promising alternatives have already been proposed [28,29], the explosion of complexity, which is an inherent issue in backstepping, still poses some difficulties for the implementation of control schemes based on this method.…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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“…Therefore, as long as the BLF is bounded, the related states will not violate the constraints. References [25][26][27] describe how BLFs have been used to deal with the output constraints. References [25,27] describe how a BLF can be used to solve the output constraint problem of a robot manipulator system.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy State Observer‐Based Adaptive Dynamic Surface Control of Nonlinear Systems with Time‐Varying Output Constraints

Wan

Liu

Zheng³

et al. 2019

Mathematical Problems in Engineering

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In this paper, a new fuzzy dynamic surface control approach based on a state observer is proposed for uncertain nonlinear systems with time-varying output constraints and external disturbances. An adaptive fuzzy state observer is used to estimate the states that cannot be measured in the systems. In our method, a time-varying Barrier Lyapunov Function (BLF) is used to ensure that the output does not violate time-varying constraints. In addition, dynamic surface control (DSC) technology is applied to overcome the problem of “explosion of complexity” in a backstepping control. Finally, the stability and signal boundedness of the system are confirmed by the Lyapunov method. The simulation results show the effectiveness and correctness of the proposed method.

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“…Thus, the adaptive impedance control for n-link robot manipulator with input saturation is developed through neural networks. An adaptive fuzzy logic is designed to estimate sliding mode control parameters in order to prevent chattering effect [3]. Real-virtual control inputs are inferred by solving a set of dynamic equations.…”

Section: Introductionmentioning

confidence: 99%

One DOF Robot Manipulator Control Through Type-2 Fuzzy Robust Adaptive Controller

Naderolasli

Chatraei

2019

JAMRIS

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In this article, the one DOF robot manipulator control is assessed through second type robust fuzzy-adaptive controller. The objective is to obtain a tracking path with appropriate accuracy. The stability of the closed loop system is verified through Lyapunov stability theory and the efficiency of tracking is analyzed subject to the constraints and uncertainty. In order to design the fuzzy controller a set of if-then fuzzy rules are considered which describe the system input-output behavior. Simulation and the results of the experiments on the one DOF robots indicate the effectiveness of the proposed methods.

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Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

Cited by 35 publications

References 43 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Fuzzy State Observer‐Based Adaptive Dynamic Surface Control of Nonlinear Systems with Time‐Varying Output Constraints

One DOF Robot Manipulator Control Through Type-2 Fuzzy Robust Adaptive Controller

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