Fuzzy Neural Sliding Mode Control for Robot Manipulator

Hoang, Duy-Tang; Kang, Hee-Jun

doi:10.1007/978-3-319-42297-8_50

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The adaptation mechanism is chosen by finding the neural network that minimize the error function cost (12), that satisfies the requirement (9) determined from the Lyapunov stability conditions, (12) Due the simplicity of the selected NN structure, the on-line learning procedure can easily be calculated using the simple back propagation gradient descent algorithm (13) and (14). The existence and the stability of the global minima, and the stability of the sliding manifold when error function minima are reached; were proven in [11]:…”

Section: Controller Designmentioning

confidence: 99%

“…In recent years, soft-computing methods such as artificial neural networks (NN) and fuzzy logic systems (FLS) have been successfully applied to overcome the practical problems met in the implementation of sliding mode controllers [1,2,[6][7][8][9][10][11][12][13][14][15][16][17]. In the application of NN-based controllers to improve conventional SMC drawbacks, few main ideas were considered.…”

Section: Introductionmentioning

confidence: 99%

“…The first one attempts to exploit NN learning capacities for online estimating the equivalent control or modeling errors [1,2,6,11,12], the neural net role's is then to compensate model nonlinear terms and disturbances effects; if this compensation term is sufficiently precise, the switched control, responsible of chattering phenomenon, goes to zero. The second idea tries to online determining the adequate switching control gain, just needed to overcome disturbances effects, for reducing the chattering phenomenon amplitude [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Neuro-Sliding Mode Control of PUMA 560 Robot Manipulator

Medjebouri

Mehennaoui

2016

JAMRIS

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The classical sliding mode control (SMC) is a robust control scheme widely used for dealing with nonlinear systems uncertainties and disturbances. However, the conventional SMC major drawback in real applications is the chattering phenomenon problem, which involves extremely high control activity due to the switched control input. To overcome this handicap, a pratical design method that combines an adaptive neural network and sliding mode control principles is proposed in this paper. The controller design is divided into two phases. First, the chattering phenomenon is removed by replacing the sign function included in the switched control by a continuous smooth function; basing on Lyapounov stability theorem. Then, an adaptive linear neural network, that has the role of online estimate the equivalent control in the neighborhood of the sliding manifold, is developed when the controlled plant is poorly modeled. Simulation results show clearly the satisfactory chattering free tracking performance of proposed controller when it is applied for the joints angular positions control of a 6-DOF PUMA 560 robot arm.

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