Dynamic Performance Optimization of the Dual Clutch Transmission Based on Genetic Algorithm

2018 ◽  
Author(s):  
Dongwei He ◽  
Guangqiang Wu ◽  
Yimin Shi ◽  
Wenbin Yang
Keyword(s):  
Genetic Algorithm ◽  
Performance Optimization ◽  
Control Method ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Simulation Models ◽  
Pressure Control ◽  
Control Logic ◽  
Engagement Process ◽  
Overall Performance

Gear shift law is determined, and shifting control logic, clutch pressure control strategy are developed, Simulation models of the continuous up-shift and down-shift process of DCT are established. Comparison of simulation results by the indicators of friction work, friction power and jerk, Influence on shifting performance about clutch pressure rate and circulates pressure are analyzed qualitatively. According to the fitness function of overall performance of shifting quality, the pressure optimal control objectives of DCT are proposed, and based on genetic algorithm for present control method in engagement process of DCT, the parameter optimization is studied.

Performance Optimization of Extrusion Blow Molded Parts Using Fuzzy Neural-Taguchi Method and Genetic Algorithm

2002 ◽  
Author(s):  
Jyh-Cheng Yu ◽  
Tsung-Ren Hung ◽  
Francis Thibault
Keyword(s):  
Genetic Algorithm ◽  
Performance Optimization ◽  
Fitness Function ◽  
Back Propagation ◽  
Stress Constraints ◽  
Molding Process ◽  
Finite Element Software ◽  
Blow Molding ◽  
Fuzzy Neural ◽  
Propagation Network

This paper presents a soft computing strategy to determine the optimal die gap parison programming of extrusion blow molding process. The design objective is to minimize part weight subject to stress constraints. The finite-element software, BlowSim, is used to simulate the parison extrusion and the blow molding processes. However, the simulations are time consuming, and minimizing the number of simulation becomes an important issue. The proposed strategy, Fuzzy Neural-Taguchi and Genetic Algorithm (FUNTGA), first establishes a back propagation network using Taguchi’s experimental array to predict the relationship between design variables and response. Genetic algorithm is then applied to search for the optimum design of parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The Reliability Distance is proposed and introduced to genetic algorithm using fuzzy rules to modify the fitness function and thus improve search efficiency. This study uses ANSYS to find the stress distribution of blown parts under loads. The comparison of results demonstrates the effectiveness of the proposed strategy.

Blade Arrangement Optimization for Mistuned Bladed Disk Based on Gaussian Process Regression and Genetic Algorithm

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Wangbai Pan ◽  
Meiyan Zhang ◽  
Guoan Tang
Keyword(s):  
Genetic Algorithm ◽  
Gaussian Process ◽  
High Efficiency ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Computational Cost ◽  
Combinatorial Problems ◽  
Optimal Arrangement ◽  
Bladed Disk ◽  
Gp Model

Abstract Mistuning phenomena exist in the bladed disk due to the inevitable deviations among blades' properties, e.g., stiffness, mass, geometry, etc., leading to localization and response amplification. The dynamic performance of mistuned bladed disk is sensitive to the arrangement of blades. The blade arrangement optimization aims to obtain the optimal arrangement that minimizes the influence of mistuning. In this paper, a framework of high efficiency is raised to deal with the challenge of high computational cost this optimization. It comprehensively utilizes mixed-dimensional finite element model (MDFEM), Gaussian process (GP) regression, and genetic algorithm (GA). The MDFEM can perform mistuned modal analysis efficiently and provides the training set of GP regression rapidly. The GP model, as a surrogate model, predicts the desired dynamic performance directly without calculating the numerical model and can function as fitness function in optimization. GA has the capability to deal with combinatorial problems and is a good option for problems with large search domains and several local maxima/minima. The techniques and processes of three methods are illustrated in detail. Case studies, based on a real turbine, are concretely presented in a gradually progressive manner to test and verify the effectiveness, accuracy, and efficiency of methods and entire framework step by step. The results show the satisfactory optimal arrangement for a randomly chosen set of mistuned blades, and the influence of mistuning is reduced indeed. The time cost of the optimization has been reduced several orders of magnitude. This framework can be a promising approach for the blade arrangement optimization problem.

scholarly journals Research on Optimization of Fuzzy Network Control System Based on New Smith Predictive Time Delay Compensation

2021 ◽  
Vol 2132 (1) ◽  
pp. 012019
Author(s):  
Cong Lu ◽  
Xuehui Xian ◽  
Changqing Li
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Control Method ◽  
Dynamic Performance ◽  
Network Control ◽  
Smith Predictor ◽  
Fuzzy Pid ◽  
Network Control System ◽  
Fuzzy Pid Control ◽  
Actual Model

Abstract Aiming at the problem of network delay when the network control system transmits data, this paper adopts a new type of fuzzy control method of Smith predictor to compensate for the delay. In actual application scenarios, it is difficult to accurately match the Smith prediction model with the actual model. At the same time, the quantization factor and scale factor in the fuzzy PID controller are too dependent on experience, which makes the system’s adaptability to actual working conditions very poor. In this paper, genetic algorithm is used to optimizes fuzzy PID. According to the simulation results, when the Smith predictive model does not match the actual model, the steady-state performance and dynamic performance of the system under the fuzzy PID control optimized by the genetic algorithm are improved.

Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 961-966
Author(s):  
Fei Xiang ◽  
Shan Li
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Adaptive Mutation ◽  
System Response ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Adaptive Immune ◽  
Design Scheme

For power plant boiler combustion control system has large inertia, nonlinear and other complex characteristics, a control algorithm of PID optimized by means of adaptive immune genetic algorithm is presented. A variety of improved schemes of GA were designed, include: initial population generating scheme, fitness function design scheme, immunization strategy, adaptive crossover probability and adaptive mutation probability design scheme. By taking the rise time, error integral and overshoot of system response as the performance index, and using genetic algorithm for real-coded of PID parameters, then a group of optimal values were obtained. Simulation results show that the method has a good dynamic performance, superior to the conventional PID controller.

scholarly journals Parameters optimization of two-speed powertrain of electric vehicle based on genetic algorithm

2020 ◽  
Vol 12 (1) ◽  
pp. 168781402090165
Author(s):  
Yuhang Li ◽  
Bo Zhu ◽  
Nong Zhang ◽  
Hao Peng ◽  
Yongzhong Chen
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Optimization Model ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Parameters Optimization ◽  
Optimization Process ◽  
Performance Parameters ◽  
Improved Genetic Algorithm

Aiming at the shortcomings of only optimizing the gear ratios of two-speed transmission in the optimization process of two-speed powertrain parameters of electric vehicles, the optimization of two-speed powertrain parameters of electric vehicles based on genetic algorithm is proposed. The optimization process is to optimize the main performance parameters of the drive motor and the gear ratios of two-speed transmission. That is, taking the economy and dynamic of the electric vehicle as the fitness function, the gear ratios of two-speed transmission is optimized under the main performance parameters of different drive motors, so as to find the powertrain parameter with the best fitness function value. Among them, the AMESim software is used to build the vehicle optimization model, the genetic algorithm is improved by MATLAB, and the improved genetic algorithm is used to optimize the vehicle optimization model. The results show that the optimization of the vehicle’s economic and dynamic performance has been improved, indicating that this optimization method is effective.

Adaptive Weighting Decoupling Design for Constrained Generalized Predictive Control Using Improved Genetic Algorithm

2013 ◽  
Vol 441 ◽  
pp. 829-832
Author(s):  
Yong Bin Dai
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Shape Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Main Idea ◽  
Generalized Predictive Control ◽  
Multivariable System ◽  
Improved Genetic Algorithm ◽  
Error Weight

The paper proposes a new method for decoupling multivariable system based on generalized predictive control (GPC) with constrains. It is the main idea of proposed control method that the error weight can change with output deviation caused by reference changes in order to reduce interactions in the system and improve dynamic performance of coupling loops. With improved genetic algorithm to optimize the performance index of GPC, the algorithm is applied to auto shape control and auto gauge control (ASC-AGC). The simulation results demonstrate the efficiency and correctness of approach proposed.

scholarly journals Research on Pressurizer Pressure Control Based on Adaptive Prediction Algorithm

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Qian ◽  
Yuan Yuan ◽  
Yu Wang ◽  
Gaofeng Jiang ◽  
Ting Yang
Keyword(s):  
Performance Optimization ◽  
Nuclear Power ◽  
Power Plants ◽  
Control Method ◽  
Engineering Application ◽  
Pressure Control ◽  
Open Loop ◽  
Prediction Algorithm ◽  
Control Performance ◽  
Dynamic Matrix

According to the high control quality requirements of nuclear power plants and the features of the pressurizer pressure with large inertia, time-varying, nonlinear, multi-interference, difficulty in obtaining accurate mathematical model, and open-loop unstable dynamic characteristic, the advanced control strategy is needed for pressurizer pressure control performance optimization. To tackle the problem, an adaptive predictive control method for pressurizer pressure is devised in this paper. Firstly, the non-self-regulating system is stabilized and the adaptive dynamic matrix controller is designed by identifying the controlled object online. In order to realize the engineering application for this controller, then the control signal output is obtained. Finally, the control system simulation platform is built. Simulation results reveal a superior control performance, disturbance rejection, and adaptability. Furthermore, it provides a solution for the application of dynamic matrix control algorithm in non-self-regulating system.

Research for Fuel Common Rail System about Marine Diesel Engine

2014 ◽  
Vol 960-961 ◽  
pp. 1455-1459
Author(s):  
Yuan Qing Wang ◽  
Guang Ren ◽  
Zhi Qiang Zhou
Keyword(s):  
Diesel Engine ◽  
Control Method ◽  
Pressure Control ◽  
Common Rail ◽  
Rail Pressure ◽  
Control Logic ◽  
Fuzzy Pid Control ◽  
Common Rail System ◽  
Rail System ◽  
Rail Pressure Control

Wartsila RT-flex 60C diesel engine is selected as the object to research pressure control of fuel common rail system, building the system simulation model, and analyzing main factors influencing rail pressure in the system. it was concluded that they were diesel engine speed, load. In the process of MATLAB/Simulink simulation, the rail pressure control logic was designed by adopting maps with the two factors as independent variables to get theory feedforward quantity of fuel rack, feedback quantity of fuel rack is obtained by fuzzy PID control. The simulation experiments show that the improved control method reduces the rail pressure fluctuation, at the same time the overshoot volume of dynamic rail pressure control is greatly reduced, response velocity quickened. The control method has been used in a Marine simulator.

scholarly journals Vehicle Drive Control Using Fuzzy Based PI Speed Controller

2020 ◽  
Vol 2 (2) ◽  
pp. 68-75
Author(s):  
Dr. Subarna Shakya
Keyword(s):  
Fuzzy Control ◽  
Control Theory ◽  
Control Method ◽  
Dynamic Performance ◽  
Permanent Magnet Synchronous Motor ◽  
Reference Value ◽  
Simulation Models ◽  
Response Speed ◽  
Speed Controller ◽  
Drive Control

This paper proposes a fuzzy PI speed controller which is used in electric vehicle’s drive control system insensitive to parameter change and disturbance, using fuzzy control theory. A permanent magnet synchronous motor (PMSM) is modelled mathematically in d-q reference frame. In this paper, a sliding model and fuzzy control theory are used to simulate the PMSM models using fuzzy control theory. Simulink software is used to analyze and simulate the simulation models which show that the proposed PI control based on fuzzy logic will have better anti-interference, better dynamic performance, and faster dynamic response speed when compared with the sliding motor control. Hence the proposed methodology is considered to be the ideal control method with a predefined vector control reference value for the electric vehicle’s motor.

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