Neural network-based sliding mode adaptive control for robot manipulators

Sun, Tairen; Pei, Hailong; Pan, Yongping; Zhou, Hongyu; Zhang, Caihong

doi:10.1016/j.neucom.2011.03.015

Cited by 226 publications

(115 citation statements)

References 16 publications

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“…Conventional feedback control methods do not obtain robustness and high performance when facing with the nonlinearities, uncertainties and external disturbances [7][8][9][10]. Sliding mode control (SMC) as an effective robust control technique that has been successfully applied to control or track certain linear and nonlinear systems such as robotic manipulators [11], nonholonomic systems [12], aircraft [13], underwater vehicles [14], spacecraft [15], flexible space structures [16], chaotic systems [17], electrical motors [18] and power systems [19]. The significant features of SMC are the fast response, robustness against uncertainties, insensitivity to the bounded disturbances, good transient performance and computational easiness with respect to other robust control methods [20][21][22].…”

Section: Background and Motivationsmentioning

confidence: 99%

An adaptive fast terminal sliding mode control combined with global sliding mode scheme for tracking control of uncertain nonlinear third-order systems

Mobayen

2015

Nonlinear Dyn

108

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In this paper, an adaptive fast terminal sliding mode control technique combined with a global sliding mode control scheme is investigated for the tracking problem of uncertain nonlinear third-order systems. The proposed robust tracking controller is formulated based on the Lyapunov stability theory and guarantees the existence of the sliding mode around the sliding surface in a finite time. Under the uncertainty and nonlinearity effects, the reaching phase is removed and the chattering phenomenon is eliminated. This scheme guarantees robustness against nonlinear functions, parameter uncertainties and external disturbances. The derivative of the state variable is replaced by a delay term in the form of an Euler approximation of the derivative function. Furthermore, the knowledge of upper bounds of the system uncertainties is not required, which is more flexible in the real implementations. Simulation results are presented to show the effectiveness of the suggested method.

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Section: Background and Motivationsmentioning

confidence: 99%

An adaptive fast terminal sliding mode control combined with global sliding mode scheme for tracking control of uncertain nonlinear third-order systems

Mobayen

2015

Nonlinear Dyn

108

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“…In the literature of adaptive control, neural network is widely used to approximate the unknown nonlinearities due to its inherent approximation capabilities [2,[10][11][12][13][14]. In this paper, the neural network is used for online approximating uncertainties in the DP system.…”

Section: Neural Networkmentioning

confidence: 99%

Design of neural network observer for ship dynamic positioning system

Lin

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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A neural network observer is developed for ship dynamic positioning system with uncertain dynamics and disturbances in this paper. For ship dynamic positioning system, in most cases the position measurements are available but the velocity measurements are not. In addition, the measured position and heading signals are often corrupted by noises, which may reduce the performance of the dynamic positioning system. The designed observer is presented to estimate all the state of the dynamic positioning system, including position and velocity signals from the corrupted position signals. Based on Lyapunov theory, it is proved that the observer estimation errors are uniformly ultimately bounded. It is also shown that the designed neural network observer is capable of producing noise-free estimates of velocity and position from corrupted position measurement. An example is given to show the performance and effectiveness of the proposed neural network observer.

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“…Recently, several intelligent SMC systems using the fuzzy system and neural network approaches have been developed to control unknown nonlinear systems [6][7][8][9][10]. The parameter learning methods of the intelligent SMC systems are designed based on gradient descent method, Lyapunov stability theorem and bio-inspired population algorithms.…”

Section: Introductionmentioning

confidence: 99%