Tracking control of robot manipulators using second order neuro sliding mode

Abstract

Few works on neural networks-based robot controllers address the issue of how many units of neurons, hidden layers and inputs are necessary to approximate any functions up to a bounded approximation error. Thus, most proposals are conservative in a sense that they depend on high dimensional hidden layer to guarantee a given bounded tracking error, at a computationally expensive cost, besides that an independent input is required to stabilize the system. In this paper, a low dimensional neural network with online adaptation of the weights is proposed with an stabilizer input which depends on the same variable that tunes the neural network. The size of the neural network is defined by degree of freedom of the robot, without hidden layer. The neuro-control strategy is driven by a second order sliding surface which produce a chattering-free control output to guarantee tracking error convergence. To speed the response up even more, a time base generator shapes a feedback gain to induce finite time convergence of tracking errors for any initial condition. Experimental results validate our proposed neuro-control scheme.