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The behavior of nonlinearity and time-varying cause the pneumatic actuator systems are difficult to be controlled. This paper proposes a Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems. The Fourier series-based functional approximation technique can approximate an unknown function, thus bypassing the model-based prerequisite. The learning laws for the coefficients...
In this study, the synchronization of chaotic neural networks with time-varying delay is developed based on parameter identification and sliding model control. Under the framework of master/slave chaotic neural networks, recurrent neural network, is developed to accommodate the on-line synchronization, which the weights of the neural network are iteratively and adaptively updated through the error...
Since the twin-roll strip casting process has the properties of nonlinear uncertainty and time-varying characteristics, it is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive radial basis function sliding-mode controller is developed to overcome this problem. The proposed control strategy is based...
According to the nonlinear and parameter time-varying characteristics of vehicle stability control, a sliding control algorithm is proposed based on radial base function (RBF) neural network. The algorithm not only can reduce the chattering caused by the conventional sliding mode, but also improve the robust of the adaptive neural network control. The simulation results show the algorithm ensures...
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...
According to the fact that the unsatisfied control effects caused by nonlinear and time-varying factors, this paper proposed a novel sliding mode variable structure speed regulator based on immune RBF neural network. In order to weaken the chattering phenomenon of sliding mode control, we replace sliding mode switching control with RBF neural network. A novel online training algorithm based on immune...
This paper presents neural networks iterative learning control for a class of nonlinear time-varying systems. A finite time boundary layer is introduced and the inherent property of terminal sliding modes is exploited to realize finite time convergence, in the presence of initial repositioning errors. The neural networks employed in the controls have time-varying weights. Both indirect and direct...
This study presents a class of first-order sliding mode stabilizing control laws, which preserve the advantages of rapid response and robustness from the conventional sliding mode control (SMC) schemes. The presented SMC laws are continuous everywhere so that the chattering characteristic of the sign-type SMC laws is greatly alleviated. This scheme is also shown to be able to mend the saturation-type...
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