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In this paper, robust Pareto multi-objective optimum design of vehicle vibration model having parameters with probabilistic uncertainties is considered. In order to achieve optimum robust design against probabilistic uncertainties existing in reality, a multi-objective uniform-diversity genetic algorithm (MUGA) in conjunction with Monte Carlo simulation is used for Pareto optimum robust design of...
In this paper, multi-objective evolutionary Pareto optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling of nonlinear systems using input-output data sets with probabilistic uncertainties. In this way, A Monte Carlo Simulation (MCS) is first performed to generate input-output data set using some probabilistic distributions. Multi-objective uniform-diversity genetic...
In this paper, An optimal fuzzy system (OFS), instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a two-mass-spring system having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the probabilities of failure of settling time (PTs), of control...
In this paper, a new method is proposed to design a sliding mode controller with variable boundary layer for a nonlinear system. In this method, model predictive control (MPC) is used to predict the future boundary layer thickness. In order to predict the behavior of the nonlinear system, a neural network model is used as an internal model. The simulation results show the supremacy of this method...
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