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Neural networks, fuzzy logic, and optimal control for vehicle active systems with four-wheel steering and active suspension

By B. Cai

Abstract

It is described that in recent years, two types of neural networks (NN) have received considerable attentions in the area of artificial neural networks (ANN) theory. They are multilayer neural network (MNN) and recurrent neural network (RNN). The MNN has shown great potential in pattern recognition problems while RNN is used widely in associative memories. In this work, new methods based on the inherent abilities of NN, such as learning, memorizing, nonlinear mapping and adapting, are developed to solve the design problem of the output feedback. It is shown that for linear time-invariant systems, two kinds of optimal output feedback controller using MNN and HNN can be designed by tracking optimal state feedback, which is obtained by solving the matrix Riccati equation. The aim of using of the 4WS system is to improve the transient response of the vehicle to steering input and to lateral disturbances. There are two realizations available for the control strategy on how the rear wheel being steered. (orig./RHM)Available from TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Topics: 06D - Bionics, 13F - Ground transport systems, NEURAL NETS, ARTIFICIAL INTELLIGENCE, CONTROL SYSTEMS DESIGN, CONTROL THEORY, MATHEMATICAL MODELS, STEERING, VEHICLES, VEHICLE WHEELS, OPTIMIZATION, FUZZY SYSTEMS
Year: 1993
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Provided by: OpenGrey Repository
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