Robust adaptive neural practical fixed-time tracking control for uncertain Euler-Lagrange systems under input saturations

Zhu, Guibing; Du, Jialu

doi:10.1016/j.neucom.2020.05.057

Cited by 38 publications

(28 citation statements)

References 44 publications

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“…It is worth mentioning that it's convergence time is independent of the initial states of the system. In recent years, fixed-time control has received extensive attention and many research schemes have been proposed [39][40][41][42][43]. [39] showed the problem of fuzzy logic adaptive fixed-time control for nonlinear switched system.…”

Section: Introductionmentioning

confidence: 99%

“…[39] showed the problem of fuzzy logic adaptive fixed-time control for nonlinear switched system. A fixed-time scheme is designed by combining adaptive neural network with Euler-Lagrange systems (ELSs) in [40]. In [43], Yang and Niu were devoted to the fixed-time tracking control problem for a class of uncertain strict-feedback non-linear system based on event-triggering mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Fixed-Time Bounded H Infinity Tracking Control of a Single-Joint Manipulator System with Input Saturation

chen

2021

Preprint

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Based on Lyapunov finite-time stability theory and backstepping strategy, we put forward a novel fixed-time bounded H infinity tracking control scheme for a single-joint manipulator system with input saturation. The main control objective is to maintain that the system output variable tracks the desired signal at fixed time. The advantages of this paper are the settling time of the tracking error converging to the origin is independent of the initial conditions, and its convergence speed is more faster. Meanwhile, bounded H infinity control is adopted to suppress the influence of the external disturbances on the controlled system. At the same time, the problem of input saturation control is considered, which effectively reduce the input energy consumption. Theoretical analysis shows that the tracking error of the closed-loop system converges to a small neighborhood of the origin within fixed time. In the end, a simulation example is presented to demonstrate the effectiveness of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fixed-Time Bounded H Infinity Tracking Control of a Single-Joint Manipulator System with Input Saturation

chen

2021

Preprint

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“…In recent decades, Euler-Lagrange systems (ELSs) have attracted more and more attention from researchers since they can model the dynamics of a large class of physical systems, like surface vehicles [1]- [2], robotic manipulators [3]- [4], aircrafts [5], underwater vehicles [6]- [7], etc. Due to its wide applications in engineering, a rich collection of control schemes can be captured to implement tracking control on the ELSs in existing literature (see, e.g, [8]- [10] and references therein).…”

Section: Introductionmentioning

confidence: 99%

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Wang¹,

Kuang²

2021

IEEE Access

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This paper investigates the control issue of Euler-Lagrange systems (ELSs) subject to dynamic uncertainties and external disturbances under input and output constraints, and develops two neuroadaptive control schemes, i.e., direct neuroadaptive approximation method and indirect neuroadaptive approximation method. In the control design, a smooth saturation function called Gaussian error function is used to replace the saturation model, which is applied to solve the input saturation issue. Moreover, a new function is used to guarantee that the output does not violate the restricted boundary. The uncertain dynamic of the ELSs is reconstructed by the direct or indirect neuroadaptive method, and then a virtual-parameter learning method is proposed to reduce the computational load of control schemes. With the aid of Lyapunov stability theory, it is proven that all signals in the closed-loop control system are bounded and the tracking error of ELSs converges to zero under the proposed neuroadaptive control control schemes. The simulations on a robotic manipulator illuminate the effectiveness and preponderance of the developed neuroadaptive control schemes.INDEX TERMS Euler-Lagrange system, dynamic uncertainty, input and output constraints, neuroadaptive control.

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“…An adaptive multilayer NN is developed to estimate the uncertainty and a novel saturated prescribed performance controller for EL dynamic systems in [33]. In [34], to reduce the calculation burden, the adaptive NNs with the epsilon-modification updating laws are developed to approximate the compounded uncertain vector for EL systems. In [35], a selfstructuring NN is designed to estimate the uncertain dynamics of each node of multiagents.…”

Section: Introductionmentioning

confidence: 99%

Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self‐Structuring Neural Networks

Wang

2021

Computational Intelligence and Neuroscience

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This article studies the robust tracking control problems of Euler–Lagrange (EL) systems with uncertainties. To enhance the robustness of the control systems, an asymmetric tan-type barrier Lyapunov function (ATBLF) is used to dynamic constraint position tracking errors. To deal with the problems of the system uncertainties, the self-structuring neural network (SSNN) is developed to estimate the unknown dynamics model and avoid the calculation burden. The robust compensator is designed to estimate and compensate neural network (NN) approximation errors and unknown disturbances. In addition, a relative threshold event-triggered strategy is introduced, which greatly saves communication resources. Under the proposed robust control scheme, tracking behavior can be implemented with disturbance and unknown dynamics of the EL systems. All signals in the closed-loop system are proved to be bounded by stability analysis, and the tracking error can converge to the neighborhood near the origin. The numerical simulation results show the effectiveness and the validity of the proposed robust control scheme.

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Robust adaptive neural practical fixed-time tracking control for uncertain Euler-Lagrange systems under input saturations

Cited by 38 publications

References 44 publications

Fixed-Time Bounded H Infinity Tracking Control of a Single-Joint Manipulator System with Input Saturation

Fixed-Time Bounded H Infinity Tracking Control of a Single-Joint Manipulator System with Input Saturation

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self‐Structuring Neural Networks

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