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In this paper, spacecraft attitude control is investigated by fuzzy control and quantization. The spacecraft attitude control model is transformed into a T-S fuzzy system with appropriate fuzzy rules. Moreover, a “zoom” strategy is applied to deal with the influence of quantization. A fuzzy controller is designed to guarantee the stability of the fuzzy system with quantization. A sufficient condition...
This paper introduces a new maximum power point tracking (MPPT) based on a new generalized dynamic fuzzy neural network (GD-FNN) for photovoltaic (PV) systems. Fuzzy control rules can be generated or deleted automatically according to their significance to the control system, and no predefined fuzzy rules are required. The basic principle and the implementation procedures of GD-FNN are elaborated...
In view of the relative backward status of the energy-saving control technology for hydraulic excavators, a control method with fuzzy neural network (shorted as FNN) is proposed. A standard model's fuzzy neural network is used as the controller, in which the back propagation algorithm (BP algorithm) is applied. Simulation results show that the control system has good rapidity and stability, can make...
It is difficult to realize dynamic control for some complex nonlinear processes which are operated in different environments and when operation conditions are changed frequently. In this paper we propose an identifier-based control method in dynamic tracking neuro-fuzzy control system. The dynamic tracking neuro-fuzzy control (DTNFC) system is comprised of two neural networks and a system identification...
This paper presents the dynamic neuro-fuzzy control system for controlling a gas-fired water heater. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic neural networks in the forward path. A dynamic identification network (DIN) is used to identify the output of the manipulator system, and a dynamic learning network (DLN)...
This paper presents a design method of adaptive fuzzy controllers using dual-fuzzy neural networks (DFNNs). For the convenience of adaptive control, the structure of the two-fuzzy neural-network controller is divided into two parts. Each part is a fuzzy neural-network (FNN). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The fuzzy control rule...
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