Abstract
The manipulator, in most cases, works in unstructured and changeable conditions. With large external variations, the demand for stability and robustness must be ensured. This paper proposes a neural network sliding mode control (NNSMC) to cope with uncertainties and improve the behavior of the robotic manipulator in the presence of an external disturbance. The proposed method is applied to the three degrees of freedom (DOF) manipulator. Some comparisons between the proposed and the conventional algorithms are given in both simulation and experiments to prove that the designed control can achieve higher accuracy in tracking motion.
Highlights
Robot manipulators have been rapidly developing in many applications that require high accuracy in tracking performance
In order to deal with these problems, many approaches such as using a proportional integral derivative (PID) control [2], feedback linearization [3], robust control [4,5], adaptive control [6,7,8], backstepping (BSP) control [9,10], hybrid proportional derivative sliding mode control (PDSMC) [11], sliding mode control (SMC) [12,13,14,15] and even intelligent control [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31] have been studied
Sliding mode control (SMC) is known as one of the most feasible solutions with fast response and strong robustness to resolve perturbation such as external disturbances, or unmodeled blocks in nonlinear systems compared with traditional methods [14,15]
Summary
Robot manipulators have been rapidly developing in many applications that require high accuracy in tracking performance. Vu et al proposed a robust adaptive control method based on a dynamic structure fuzzy wavelet neural network (FWNNs) system for trajectory tracking control of industrial robot manipulators to compensate for structured and unstructured uncertainties and to model complex processes [28]. These results indicated that the tracking performance was improved compared with the conventional technique. Motivated by previous works and in an effort to improve and achieve high tracking performance in the action of external disturbance, a neural network sliding mode control (NNSMC), where the NN is used to directly adjust the controller gains of the sliding mode control (SMC), is proposed to overcome the problem of payload variation for a 3-DOF manipulator.
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