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In this paper, we propose a controller for a nonlinear system with an input constraint depending on the state. The proposed controller is based on Kidane's controllers. Then, if system has one input, we can tune optimality and robustness easily. We apply the proposed controller to single-link robot arm in order to verify the effectiveness of the proposed controller
PID control schemes still continue to be widely used for most industrial control systems. This is mainly because PID controllers have simple control structures, and are simple to maintain and tune. However, it is difficult to find a set of suitable control parameters in the case of time-varying and/or nonlinear systems. For such a problem, the robust controller has been proposed. Although it is important...
For linear systems with structured uncertainties, the unknown input fault detection observer (UIFDO) provides a robust diagnostic approach by decoupling the fault signals from the unknown input term. However, in real applications, detection performance of the UIFDO degrades seriously as nonlinearities become significant in a wide dynamic operating range. In this paper, a novel UIFDO design and the...
This paper is about a regularization method applied to a chemical process considered as a nonlinear system. The Volterra model is used to represent such a nonlinear system. Volterra kernels are expanded on generalized orthonormal function bases in order to provide a quite flexible and accurate model. Tikhonov's regularization method, optimized on a sliding window, is then applied to the Volterra model...
This paper describes a design for adaptive control of transverse flux permanent magnet machines as nonlinear systems with unknown nonlinearities by utilizing Takagi-Sugeno-Kang type neuro-fuzzy networks. The technique of feedback linearization and Hinfin control are used to design the adaptive control law for compensating the unknown nonlinear parts, such the effect of cogging torque, as a disturbance...
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