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Adaptive sliding mode control for robots based on fuzzy support vector machines | IEEE Conference Publication | IEEE Xplore

Adaptive sliding mode control for robots based on fuzzy support vector machines


Abstract:

To improve the control precision of robots, the control method of adaptive sliding mode for robots was presented based on fuzzy support vector machines. The sliding mode ...Show More

Abstract:

To improve the control precision of robots, the control method of adaptive sliding mode for robots was presented based on fuzzy support vector machines. The sliding mode control has complete adaptability to system disturbance and siring in sliding mode, which was used to automatically track the uncertainty of system parameters and external disturbance. Fuzzy support vector machines have strong treatment of nonlinear signal and generalization ability, which was used to reduce the chattering in sliding mode control. The FSVM controller parameters were optimized by hybrid learning algorithm, which combines least square algorithm with improved genetic algorithm, to get the optimal control performance with the controlled object. The simulation results of a two-link robotic manipulator demonstrated that the control method designed gets tracking effect with high precision and speed, as well as reduces chattering of control under the condition of existing model error and external disturbance.
Date of Conference: 09-12 August 2009
Date Added to IEEE Xplore: 18 September 2009
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ISSN Information:

Conference Location: Changchun

I. Introduction

With the development of robots and control technology, robots are applied extensively in industrial and agricultural operation. Robots are a complicated nonlinear multivariable with and strong coupling. Because of the complexity of operation condition, and random distribution of operated object, it is also time-varying system. By the general model-based control method, robots are not controlled accurately, so their tracks are not kept better. Sliding mode control (SMC) has complete adaptability for system disturbance and stirring, which is extensively applied in robots[1]. Fuzzy control does not need precise mathematical model and can decouple joints, but fuzzy control system is easily influenced by non-linear, time-varying and random disturbance [2]. Neural network control has many advantages, such as self-learning, selforganizing, self-adaptive capacity, fault-tolerance, nonlinear and parallel distributed processing, noise treatment, inadequate data treatment, and so on. However, it also has the congenital defects, such as it falls into local minimum easily, and it is weakly normalized for few samples[1]. These defects make it difficult to meet the requirements of precise control for robot.

References

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