The Study on Fuzzy Control of Automotive Clutch

2011 ◽  
Vol 230-232 ◽  
pp. 334-338
Author(s):  
Lei Zhang ◽  
Xiao Ming Zhang ◽  
Xiu Ming Yang ◽  
Yan Liu
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Variable Parameter ◽  
Continuously Variable Transmission ◽  
Scale Model ◽  
Electronic Control ◽  
Single Chip ◽  
Driving Experience ◽  
Variable Transmission ◽  
Clutch Control

Due to the complexity of the car running condition, the differences of the subjective intention of the driver, and Continuously Variable Transmission control system itself existed nonlinear, time delay, interference, variable parameter factors, the traditional control arithmetic based on scale model is hard to meet the clutch control requirements. This article through to design the drive mechanism of the dry friction clutch on a new, without hydraulic pump, pure electronic control car continuously variable transmission, and analyze its mechanical properties and control target, the clutch control model was established, according to the fuzzy control theory, driving experience and developers’ technical experience, the corresponding fuzzy language rules were formed, and intelligent control of the clutch on the single-chip control system was realized. At present, the pure electronic control continuously variable transmission has been tested on the experimental bench and car operation, and has passed the inspection by the national vehicle quality supervision and inspection center (Chongqing) and the identification by China Machinery Industry Federation .

Design of Control System for Automotive CVT Based on ATmega164P MCU

2012 ◽  
Vol 479-481 ◽  
pp. 1897-1900
Author(s):  
Yue Cheng
Keyword(s):  
Control System ◽  
Force Control ◽  
Hydraulic System ◽  
Continuously Variable Transmission ◽  
Clamping Force ◽  
Single Chip ◽  
Rotating Speed ◽  
Position Signal ◽  
Single Chip Computer ◽  
Variable Transmission

Control system and some functional circuits of automotive CVT (Continuously Variable Transmission) based on ATmega164P single chip computer were introduced in this paper. Hydraulic system of the CVT was controlled according to the throttle position signal, oil pressure, rotating speed of the engine and transmission output speed etc. This system has achieved the clamping force control of the metal belt.

The Design of Based on AT89C52 Automotive Air Conditioning Control System

2014 ◽  
Vol 1079-1080 ◽  
pp. 1014-1017
Author(s):  
Chun Lin Wang
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Air Conditioning ◽  
Modular Design ◽  
Design Method ◽  
Single Chip ◽  
Automotive Air Conditioning ◽  
Temperature Detection ◽  
Programming Process ◽  
Automobile Air Conditioning

This topic through to the automobile air conditioning working principle and the structure analysis of air conditioning assembly, with AT89C52 as the core of single-chip microcomputer control system is designed, and the controller hardware and part of the circuit design; To realize fuzzy control algorithm of the control system is established. Expounds how to realize the automation of automobile air conditioning system of fuzzy control; Temperature detection with high precision integrated temperature sensor MF51 implementation; Programming process, adopts the modular design method of each module respectively for programming, debugging, then connect them in accordance with the requirements of control, the debugging, analysis.

scholarly journals Hybrid Recurrent Laguerre-Orthogonal-Polynomial NN Control System Applied in V-Belt Continuously Variable Transmission System Using Particle Swarm Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-17
Author(s):  
Chih-Hong Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Orthogonal Polynomial ◽  
Particle Swarm ◽  
Continuously Variable Transmission ◽  
Learning Ability ◽  
Time Varying ◽  
Swarm Optimization ◽  
Adaptive Law ◽  
Variable Transmission

Because the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the hybrid recurrent Laguerre-orthogonal-polynomial neural network (NN) control system which has online learning ability to respond to the system’s nonlinear and time-varying behaviors is proposed to control PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Laguerre-orthogonal-polynomial NN control system consists of an inspector control, a recurrent Laguerre-orthogonal-polynomial NN control with adaptive law, and a recouped control with estimated law. Moreover, the adaptive law of online parameters in the recurrent Laguerre-orthogonal-polynomial NN is derived using the Lyapunov stability theorem. Furthermore, the optimal learning rate of the parameters by means of modified particle swarm optimization (PSO) is proposed to achieve fast convergence. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

An intelligent dynamic control of continuously variable transmission system using modified particle swarm optimization

2015 ◽  
Vol 230 (13) ◽  
pp. 2181-2207 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Orthogonal Polynomials ◽  
Dynamic Control ◽  
Continuously Variable Transmission ◽  
Transmission System ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Variable Transmission

In order to capture nonlinear and dynamic behaviors of the V-belt continuously variable transmission system with lots of unknown nonlinear and time-varying characteristics, an intelligent dynamic control system using modified particle swarm optimization is proposed for controlling a permanent magnet synchronous motor servo-drive V-belt continuously variable transmission system to raise robustness. The intelligent dynamic control system comprised an inspector control system, a recurrent Laguerre-orthogonal-polynomials neural network controller with adaptive law and a recouped controller with estimation law. The adaptive law of parameters in the recurrent Laguerre-orthogonal-polynomials neural network is derived according to Lyapunov stability theorem. To achieve better learning performance and faster convergence, the modified particle swarm optimization is employed to regulate two varied learning rates of the parameters in the recurrent Laguerre-orthogonal-polynomials neural network. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.

Comparative Dynamic Control of SynRM Servodrive Continuously Variable Transmission System Using Blend Amend Recurrent Gegenbauer-Functional-Expansions Neural Network Control and Altered Artificial Bee Colony Optimization

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Artificial Bee Colony ◽  
Continuously Variable Transmission ◽  
Control Performance ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Functional Expansions ◽  
Variable Transmission ◽  
Bee Colony Optimization

In comparison control performance with more complex and nonlinear control methods, the classical linear controller is poor because of the nonlinear uncertainty action that the continuously variable transmission (CVT) system is operated by the synchronous reluctance motor (SynRM). Owing to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma, and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization (ABCO) yields two varied learning rates for two parameters to find two optimal values, which helped improve convergence. Finally, the experimental results with various comparisons are demonstrated to confirm that the proposed control system can result in better control performance.

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