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In this study, a robust path tracking control scheme is constructed for a nonholonomic mobile robot via a dynamic Petri recurrent-fuzzy-neural-network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic...
PID control is common control method in industrial production, but strong non-linear control system based on ordinary control algorithm isn't able to meet stability and self-adaptive requirements, control precision can't be ensured. Adaptive adjust intelligent model of control parameter based on improved fuzzy radial basis function neural network (F-RBFNN) is proposed in this paper, new algorithm...
A control structure that makes possible the integration of a kinematic controller and a neuro-fuzzy network (NFN) dynamic controller for mobile robots is presented. A combined kinematic/dynamic control law is developed using backstepping and stability is guaranteed by Lyapunov theory. The NFN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled...
A robust fault diagnosis (FD) scheme using Takagi-Sugeno (T-S) neural-fuzzy model and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A neural-fuzzy observer and neural-fuzzy sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of the two observers. Stability of...
In this paper, a hybrid control strategy, variable universe adaptive fuzzy sliding mode control, is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as...
Based on the reference trajectory setting (RST) and noise adding (NA) technologies, a new intelligent controller design method was proposed. According to the method, there are four steps in controller design process. Firstly, a certain precision model of the plant and the disturbances should be acquired. Secondly, RST was used to plan the control objective. Thirdly, the Fuzzy-Neural Network (FNN)...
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