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A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

Guo, L.-X. and Dao, D.-N. (2020) A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system. JVC/Journal of Vibration and Control, 26 (13-14). pp. 1187-1198. ISSN 10775463

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Abstract

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers. © The Author(s) 2019.

Item Type: Article
Divisions: Faculties > Faculty of Control Engineering
Identification Number: 10.1177/1077546319890188
Uncontrolled Keywords: Adaptive control systems; Computer circuits; Deep learning; Deep neural networks; Delay control systems; Engines; Fuzzy control; Fuzzy logic; Genetic algorithms; Learning algorithms; Parameter estimation; Particle swarm optimization (PSO); Time delay; Timing circuits; Traffic signals; Vibrations (mechanical); Active mounts; Derivative control systems; Derivative controllers; Non- dominated sorting genetic algorithms; Optimal fuzzy logic controllers; Particle swarm optimization algorithm; Time and frequency domains; Time delay estimation; Controllers
Additional Information: Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8990

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