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Neural sliding mode control (NSMC) may decrease chattering of the sliding mode control (SMC) and improve control accuracy of the neural control (NC). There are some problems with the common parallel structure, such as the chattering is big at start stage. In order to overcome the above problem, we propose a new serial structure for NSMC, it is called two-stage neural sliding control. A dead-zone NC...
A neural network training method for identification in bounded time of nonlinear systems is presented in this paper. A sliding mode surface drives the adalines, perceptrons and multilayer perceptrons so as to a new second order sliding mode is enforced for all time. This neural network-based sliding mode enforces an invariant differential manifold, with a time-varying feedback gain to give rise to...
This paper proposes a control strategy based on artificial neural networks (ANNs) for a positioning system with a flexible transmission element, taking into account Coulomb friction for both motor and load, and using a variable learning rate for adaptation to parameter changes and accelerate convergence. A control structure consists of a feedforward ANN that approximates the manipulator's inverse...
A simplified primal-dual neural network based on linear variational inequalities (LVI) is presented in this paper, which is used to solve the joint angle drift problem of PUMA560 robot arm. To do this, a drift-free criterion is exploited in the form of a quadratic function. In addition, the physical constraints such as joint limits and joint velocity limits are incorporated into the problem formulation...
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