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In this paper, deadzone compensator with additive gain is proposed for robust tracking problem. After modeling a two-link manipulator via TS fuzzy model, the neural network is trained on-line to reduce the tracking error caused by deadzone. Based on the linear matrix inequality (LMI) design approach, a class of a state feedback controller with additive gain such as neurocontroller is established....
This paper investigates an application of neural networks (NNs) to the decentralized guaranteed H∞ performance for a class of large-scale uncertain nonlinear systems. In order to guarantee the adequate H∞ performance level for the nonlinear systems, nonlinear linear matrix inequality (NLMI) condition is derived. The linear matrix inequality (LMI) approach instead of the NLMI is used to construct the...
This paper considers the robust tracking control problem that is based on the guaranteed cost control approach with additive gain perturbation. Based on the linear matrix inequality (LMI) design approach, a class of a state feedback controller is established and some sufficient conditions for the existence of guaranteed cost controller are derived. A novel contribution is that the guaranteed cost...
This paper investigates the application of neural networks to the guaranteed cost control problem of discrete time uncertain system. Based on the linear matrix inequality (LMI) design approach, a class of a state feedback controller is established, and the sufficient conditions for the existence of guaranteed cost controller are derived by making use of the LMI. The novel contribution is that the...
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