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In this paper, a new control scheme for speed control is proposed that utilizes sliding mode control (SMC) and radial basis function neural network (RBFNN) to achieve the robustness. First, the design of conventional sliding mode control scheme is investigated. However, the bounds of uncertainties in the induction motor are needed to preserve the robust property. The proposed neural network sliding...
The paper is proposed the design method of nonlinear unknown input observer for Lipschitz nonlinear uncertain. Combined with the stability theory of nonlinear observers and LMI, LMI existence condition and some verification are given for the the nonlinear unknown input observers which can not only estimate the system's state approximately, but enhance the robustness for the system unknown disturbance...
In the competition paradigm of the electric power markets, both power producers and consumers need some price prediction tools in order to plan their bidding strategies. This paper studies the problem of modeling market clearing price forecasting in deregulated markets. And electricity price forecasting with support vector machines based on artificial fish swarm algorithm is provided. Except considering...
A robust nonlinear observer for the Lispschitz nonlinear system with unknown input is addressed and designed. The sufficient existence conditions for asymptotic estimation convergence, which requires solving the nonlinear matrix inequalities, are derived and proved. Using by LMI approach, the existence conditions are then reformulated as the new sufficient existence conditions in the terms of an LMI...
According to the designing method of robust sliding mode observers based on LMI in the references [8], the problems of the multiplicative fault detection and reconstruction in linear dynamic systems are studied. By designing robust sliding mode observers with LMI and applying the equivalence output error concept, the fault information is obtained so that multiplicative faults are detected and reconstructed...
In order to reduce the time of neural network self-learning, it proposes an algorithm which combines genetic algorithm (GA) and neural network prediction together. Genetic algorithm is used to search the optimal solution globally, and the data generated by GA during evolutionary process are used to train a predictive network. The predictive network establishes a mapping between parameters of operant...
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