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A fuzzy neural network disturbance observer (FNNDO) is developed and a backstepping adaptive control approach combined with FNNDO is presented for a general class of strict-feedback nonlinear systems with a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions. FNNDO is used to approximate the unknown uncertainties online, and...
A novel robust adaptive neural control approach is presented for a general class of strict-feedback nonlinear systems with both nonlinear uncertainties and virtual control gain nonlinearities that may not be linearly parameterized. A unified and systematic procedure is employed to derive a robust adaptive tracking controller by using of the backstepping technique and radial basis function neural networks...
This paper proposes a multi-model switching integrated control for a class of nonlinear systems based on terminal sliding mode control and T-S fuzzy control. This class of nonlinear systems has two control loops: the outer loop and inner loop. The resulting controller utilizes two-loop controller and provides robust tracking of the required tracks. First, the required virtual control input is determined...
Predictive control algorithm had been developed rapidly and successfully used in the industrial production practice from the 20th century 70's until now, and the generalized predictive control (GPC) algorithm had been gotten well control effect to the linear or weak nonlinear systems. But there were still difficulties to construct many steps predictive models and its control rules for the strong nonlinear...
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