Automobile Active Suspension System Optimization Design Based on Modified Multi-Objective Genetic Algorithm

2012 ◽  
Vol 157-158 ◽  
pp. 1515-1518 ◽  
Author(s):  
Yi Zhang ◽  
Chao Lu ◽  
Hu Zhang
Keyword(s):  
Objective Function ◽  
High Dimension ◽  
Optimization Design ◽  
Single Point ◽  
Active Suspension ◽  
System Optimization ◽  
Suspension System ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Set Up

A dynamic model of automobile active suspension system is established, based on which a high dimension objective model for active suspension system is set up. And through linear combinations, high dimension multi-objective function is translated into a low dimension objective function. The modified NSGAII with single point compound crossover has been adopted to realize the optimization. In the paper, the performance active suspension system can realize integrated optimization. The results show that this way can effectively enhance effect of the automobile active suspension system.

Applications of Multiple Objective Genetic Algorithms in the Optimization Design of Tracked Self-Moving Power’s Layout

2013 ◽  
Vol 307 ◽  
pp. 161-165
Author(s):  
Hai Jin ◽  
Jin Fa Xie
Keyword(s):  
Optimization Design ◽  
Pareto Front ◽  
Layout Optimization ◽  
Multiple Objective ◽  
Multi Objective Optimization ◽  
Layout Problem ◽  
Basic Principles ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Set Up

A multi-objective genetic algorithm is applied into the layout optimization of tracked self-moving power. The layout optimization mathematical model was set up. Then introduced the basic principles of NSGA-Ⅱ, which is a Pareto multi-objective optimization algorithm. Finally, NSGA-Ⅱwas presented to solve the layout problem. The algorithm was proved to be effective by some practical examples. The results showed that the algorithm can spread toward the whole Pareto front, and provide many reasonable solutions once for all.

Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Compressor Cascade ◽  
Aerodynamic Optimization ◽  
Navier Stokes Equation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

Hybrid Fuzzy Skyhook Surface Control Using Multi-Objective Microgenetic Algorithm for Semi-Active Vehicle Suspension System Ride Comfort Stability Analysis

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Yi Chen ◽  
Zhong-Lai Wang ◽  
Jing Qiu ◽  
Hong-Zhong Huang
Keyword(s):  
Stability Analysis ◽  
Real Time ◽  
Ride Comfort ◽  
Polynomial Function ◽  
Sliding Mode ◽  
Initial Conditions ◽  
Active Suspension ◽  
Suspension System ◽  
Multi Objective ◽  
Microgenetic Algorithm

A polynomial function supervising fuzzy sliding mode control (PSFαSMC), which embedded with skyhook surface method, is proposed for the ride comfort of a vehicle semi-active suspension. The multi-objective microgenetic algorithm (MOμGA) has been utilized to determine the PSFαSMC controller’s parameter alignment in a training process with three ride comfort objectives for the vehicle semi-active suspension, which is called the “offline” step. Then, the optimized parameters are applied to the real-time control process by the polynomial function supervising controller, which is named “online” step. A two-degree-of-freedom dynamic model of the vehicle semi-active suspension systems with the stability analysis is given for passenger’s ride comfort enhancement studies, and a simulation with the given initial conditions has been devised in MATLAB. The numerical results have shown that this hybrid control method is able to provide real-time enhanced level of reliable ride comfort performance for the semi-active suspension system.

scholarly journals Multi-objective Unified qLPV Observer: Application to a Semi-active Suspension System

2021 ◽  
Vol 54 (8) ◽  
pp. 136-141
Author(s):  
Gia Quoc Bao Tran ◽  
Thanh-Phong Pham ◽  
Olivier Sename
Keyword(s):  
Active Suspension ◽  
Suspension System ◽  
Multi Objective

Dynamic Optimization Design of Sand Milling Collection Machinery Suspensions

2014 ◽  
Vol 536-537 ◽  
pp. 1314-1320
Author(s):  
Hao Hu ◽  
Xiao Feng ◽  
Shi Qiu ◽  
Dan Yang ◽  
Zhong Kai Chen ◽  
...  
Keyword(s):  
Optimization Design ◽  
Surface Soil ◽  
Ride Comfort ◽  
Lagrange Equation ◽  
State Equation ◽  
Suspension System ◽  
Driving Safety ◽  
Rule Change ◽  
Milling Machines ◽  
Set Up

Sand milling collection machinery is a kind of large machinery to collect the surface soil containing ores. The suspension system of milling machines directly affects the ride comfort and driving safety for the operator; for this end, the design of suspension system has been optimized so as to satisfy the requirements of reliability and comfort by making the console and operator set at the maximum station and minimum absolute acceleration and the amplitude within the range. This paper first established a suspension system model of milling collection machines and set up the state equation of suspension system by applying Lagrange equation. On this basis, this paper conducted the optimized calculation encouraged bt the pavement with sine rule change and obtained an optimal suspension system parameters ki and ci.

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