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This paper introduces a robust ${H}_{{\infty }}$ adaptive fuzzy controller for a class of unknown nonlinear systems over network. There are two main problems in the networked control systems, the time-varying networked-induced delay and the data packet dropouts. The time-varying networked-induced delays cause degradation for the system performance that controlled over network and also the system...
This paper focuses on the problem of adaptively controlling nonlinear systems with time-varying and unknown parameters. Both state feedback and output feedback systems are considered. We seek plant stabilization and output reference tracking. The control problem is dealt with using a back-stepping adaptive controller including a switching a modification. In the case of state feedback systems, all...
Based on analysis to the performance requirements of the modulation rotary steerable drilling tool (MRST) stabilized platform controlling system, traditional PID and fuzzy adaptive PI-variable damping control schemes are studied contradistinctively to better solve the control problem. The simulation results show that the traditional PID control method only caters to one working condition of the controlling...
Based on integrating the property of sliding mode control (SMC) with the thought of variable universe in adaptive fuzzy control, a design method of variable universe adaptive fuzzy sliding mode control (FSMC) strategy for uncertain chaotic systems is proposed. There are two sets of control rule bases. The first set is utilized to approach the equivalent control of SMC. By adjusting the universes of...
In this paper, we investigate deterministic learning from adaptive neural control of general Brunovsky systems, in which the affine terms are unknown functions of system states. We firstly present an extension of a recent result on stability analysis of linear time varying (LTV) systems. We then analyze the difficulties caused by the unknown affine term in deterministic learning for general Brunovsky...
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