DocumentCode :
3231716
Title :
Hybrid Model Predictive Control based on modified Particle Swarm Optimization
Author :
Xiao, Degui ; Song, Dan ; Peng, Lixiang ; Li, Tingli
Author_Institution :
Sch. of Comput. & Commun., Hunan Univ., ChangSha, China
fYear :
2010
fDate :
23-26 Sept. 2010
Firstpage :
385
Lastpage :
390
Abstract :
Hybrid Model Predictive Control (HMPC) framework is used to design a vehicular Adaptive Cruise Control (ACC) system. Modified Particle Swarm Optimization (MPSO) algorithm, combining standard Particle Swarm Optimization (PSO) with multi-objective optimization method, is used in the process of the receding horizon optimization of the HMPC. Firstly, we design a hybrid model for the ACC system, and make use of Hybrid Systems Description Language (HYSDEL) to transform the hybrid model into a problem of Mixed Integer Linear Programming (MILP). Then, we apply MPSO algorithm to solve the MILP problem online, and the results are used to change the velocity of the cruising vehicle. Simulation results indicate that the proposed method can make the cruising vehicle follow the leading vehicle very well. Moreover, the MPSO algorithm efficiently accelerates the process of HMPC.
Keywords :
adaptive control; control system synthesis; integer programming; linear programming; particle swarm optimisation; predictive control; road vehicles; hybrid model predictive control framework; hybrid systems description language; mixed integer linear programming; modified particle swarm optimization; multiobjective optimization method; receding horizon optimization; vehicular adaptive cruise control system design; Lead; Particle swarm optimization; Programming; Vehicles; Adaptive cruise control; hybrid model predictive control; mixed integer linear programming; particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-6437-1
Type :
conf
DOI :
10.1109/BICTA.2010.5645289
Filename :
5645289
Link To Document :
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