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This paper presents a neural-adaptive sliding mode control for the tracking control of 4-SPS(PS) type parallel manipulator. The neural-adaptive controller is introduced to modify the coefficients of sliding manifold in sliding control strategy, which solve the problem that the equivalent control can not be obtained accurately because of the uncertain and fixed coefficients of sliding manifold and...
This paper addresses the issue of trajectory tracking control based on a neural network controller for industrial manipulators. A new control scheme is proposed based on neural network technology and linear feedback approach for tracking a planned trajectory. The control system is established with two parallel subsystems designed separately. One is a linear controller based on state feedback with...
This paper proposes a novel dynamic structure neural-fuzzy network (DSNFN) via a robust adaptive sliding-mode approach to address trajectory-tracking control of an n-link robot manipulator. In the DSNFN, a five-layer neural-fuzzy network (NFN) is used to model complex processes and compensate for structured and unstructured uncertainties. However, it is difficult to find a suitable-sized NFN to achieve...
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