Neural network based global adaptive dynamic surface tracking control for robot manipulators

Teng, Tao; Yang, Chenguang; Xu, Bin; Li, Zhijun

doi:10.1109/icarm.2016.7606888

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Considering (24), (26) and the equality and inequality as shown above, we can get the inequality about the time derivative of Lyapunov function as shown below:…”

Section: Inequality About the Time Derivative Of Vmentioning

confidence: 99%

“…A DSC-based adaptive fuzzy method is used to design a good performance of position tracking control for induction motors used in electric vehicle drive system in [20], and the DSC and adaptive fuzzy control are used together to design a control strategy for APF to improve the system response speed in [21]. What's more, the DSC and adaptive control schemes have been used in flexible spacecraft, transport aircraft with continuous heavy cargo airdrop, unmanned aerial vehicle, permanent magnet synchronous motor, robot manipulators, MEMS gyroscope, vehicle active suspension systems and other equipments successfully [22][23][24][25][26][27][28], but the DSC and adaptive control are not used to design an LCC HVDC wide-area emergency controller with good performance in AC-DC parallel power systems.…”

Section: Introductionmentioning

confidence: 99%

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Design of LCC HVDC wide‐area emergency power support control based on adaptive dynamic surface control

Liu

Zhao

et al. 2017

IET Generation, Transmission & Distribution

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“…Considering (24), (26) and the equality and inequality as shown above, we can get the inequality about the time derivative of Lyapunov function as shown below:…”

Section: Inequality About the Time Derivative Of Vmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of LCC HVDC wide‐area emergency power support control based on adaptive dynamic surface control

Liu

Zhao

et al. 2017

IET Generation, Transmission & Distribution

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“…Nevertheless, if the input states are outside the neural active region, the approximation error convergence cannot be guaranteed. In Huang (2012), a set of multi-switching algorithms was proposed, and Wu et al (2013) and Khalil (1996) discussed switch functions, which are used for the control system design, thereby the global adaptive neural control method is proposed through introducing the robust controllers, which was used for the control system design in and Teng et al (2016), and the related works were reported by Chen et al (2013) and Jian et al (2013). The other main concern is the disturbance of hypersonic flight dynamics, which can be dealt with a disturbance observer (DOB).…”

Section: Introductionmentioning

confidence: 99%

Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation

Zhao

Yang

2021

AEAT

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Purpose This paper aims to discuss the precise altitude and velocity tracking control of a hypersonic vehicle, a global adaptive neural backstepping controller was studied based on a disturbance observer (DOB). Design/methodology/approach The DOB combined with a radial basis function (RBF) neural network (NN) was used to estimate the disturbance terms that are generated by the flexible modes of the hypersonic vehicle system. A global adaptive neural method was introduced to approximate the unknown system dynamics, with robust control terms pulling the system transient states back into the neural approximation domain externally. Findings The globally uniformly ultimately bounded for all signals of a closed-loop system can be guaranteed by the proposed control algorithm. Additionally, the command filtered backstepping methods can avoid the explosion of the complexity problem caused by the backstepping design process. In addition, the effectiveness of the proposed controller can be verified by the simulation used in this study. Research limitations/implications Normally lateral dynamics issue should be discussed in the process of control system designed, the lateral dynamics are not included in the nonlinear dynamic model of hypersonic vehicle used in this paper, merely the longitudinal flight dynamics are discussed in this paper. Originality/value The flexible states in rigid modes are considered as the disturbance of the system, which is estimated by structuring DOB with NN approximations. The compensating tracking error and prediction error are used in the update law of RBF NN weight. The differential explosions complexity derived from the backstepping procedure is dealt with by using command filters.

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“…The conventional NN-based robot manipulators control could merely guarantee the semi-global uniformly ultimately bounded (SGUUB) stability. In this sense, it is ideal to grow a global NNs controller for robot manipulators [24]. In [25], the global NNs controller was only designed for strict-feedback systems in the case of existing unknown dynamics.…”

Section: Introductionmentioning

confidence: 99%

Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence

Yang

Teng

et al. 2017

Int. J. Control Autom. Syst.

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In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.

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Neural network based global adaptive dynamic surface tracking control for robot manipulators

Cited by 5 publications

References 29 publications

Design of LCC HVDC wide‐area emergency power support control based on adaptive dynamic surface control

Design of LCC HVDC wide‐area emergency power support control based on adaptive dynamic surface control

Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation

Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence

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