This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Fuzzy Control of Semi-active Air Suspension for Cab Based on Genetic Algorithms
Technical Paper
2008-01-2681
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Semi-active suspension has been widely applied in commercial vehicle suspension in order to get good riding comfortableness. Fuzzy logic control (FLC) has been widely applied in the field of kinetic control because control rule of FLC is easy to understand. But the gain of fuzzy rules and adjustment of membership functions usually depend on experts' experiences and repeated experiments, thus the fuzzy rules and membership functions has strong subjectivity, also are easily affected by environment of experiments, so the main problem of fuzzy logic controller design is selection and optimization of fuzzy rules and membership functions. Genetic Algorithms (GA) is the algorithm that searches the optimal solution through simulating natural evolutionary process and is one of the evolution algorithms which have most extensive impact. Because GA has some features such as group search and parallel operation that GA is fit for the group optimization of fuzzy rules and membership functions to get overall optimal solution.
In this paper the research subject is heavy commercial vehicle cab suspension system with air-spring. The air-spring deformation parameters are gained from the platform test of air-spring. According to these parameters and parameters of truck, non-linear cab suspension model is set up in ADAMS. The road excitations are generated according to Chinese National Standard. Selecting vibration amplitude and vibration acceleration of cab as the input variable, using rate of air spring as output variable, we build the initial fuzzy logic controller model in MATLAB/Simulink based on professional knowledge and experience. Initial fuzzy rules and membership functions are optimized respectively through GA. By means of MATLAB-ADAMS union simulation comparison, the result shows that the GA-optimized fuzzy controller has better control performance for different road situations, which can help suspension isolating vibration more effectively and improving riding comfortableness.
Recommended Content
Authors
Topic
Citation
Yan, J., Yin, Z., Guo, X., and Fu, C., "Fuzzy Control of Semi-active Air Suspension for Cab Based on Genetic Algorithms," SAE Technical Paper 2008-01-2681, 2008, https://doi.org/10.4271/2008-01-2681.Also In
References
- Gillespie Thomas D. Fundamentals of Vehicle Dynamics SAE International 2006
- Wang Lixin Wang Yingjun A Course in Fuzzy Systems & Control Tsinghua University Press 2006
- Hu Wei Research of GA Optimized Fuzzy Logic Controller Computer System Structure College Chinese Academy of Sciences 1998
- HASHIYAMA Tomonori FURUHASHI Takeshi UCHIKAWA Yoshiki A Study on Finding Rules for Semi-Active Suspension Controllers with Genetic Algorithm IEEE Paper 1995 48
- Siahkalroudi Vahraz Nikzad A New Approach to Control A Semi-Active Suspension Using Different Optimal Strategies. Society of Automotive Engineer Jan. 20
- Giordano Vincenzo Naso David Turchiano Biagio Combining Genetic Algorithms and Lyapunov-Based Adaptation for Online Design of Fuzzy Controllers IEEE Trans. Syst. Man, Cybern 36 5 Oct. 2006
- Chou Chih-Hsun Genetic Algorithm-Based Optimal Fuzzy Controller Design in the Linguistic Space IEEE Trans. Fuzzy Syst 14 3 Jun. 2006
- Park Joonhong Yi Joonmo Lee Daehyeong Investigation into Suspension Dynamic Compliance Characteristics Using Direct Measurement and Simulation Vehicle Dynamics & Simulation Jan. 2004
- Xuan Guangnan Cheng Runwei Genetic Algorithms and Engineer Optimization Tsinghua University Press 2004
- Lam H. K. Leung Frank H. Tam Peter K. S. Design and Stability Analysis of Fuzzy Model-Based Nonlinear Controller for Nonlinear Systems Using Genetic Algorithm IEEE Trans. Syst. Man, Cybern 33 2 April 2003
- Liu Bin-Da Chen Chuen-Yau Tsao Ju-Ying Design of Adaptive Fuzzy Logic Controller Based on Linguistic-Hedge Concepts and Genetic Algorithms IEEE Trans. Syst. Man, Cybern 31 1 Feb. 2001