Lateral control strategy of RBF neural sliding mode for autonomous vehicles(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2018年05期
- Page:
- 238-248
- Research Field:
- 汽车与机械工程
- Publishing date:
Info
- Title:
- Lateral control strategy of RBF neural sliding mode for autonomous vehicles
- Author(s):
- HE Yi-lin; MA Jian; ZHAO Dan; LIU Xiao-dong; ZHANG Yi-xi; ZHANG Kai
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- Keywords:
- automotive engineering; lateral motion control strategy; sliding mode control; autonomous vehicle; RBF neural network; particle swarm optimization
- PACS:
- -
- DOI:
- -
- Abstract:
- To enhance the lateral motion control of autonomous vehicles, a sliding mode control strategy based on improved RBF neural network was proposed. First, the state equation of lateral motion was obtained based on the single point preview model and the twofreedom model of the vehicle. Subsequently, the RBF neural network was used to replace the switching control of the sliding mode structure. On the basis of sliding mode control, the RBF network was optimized using an improved particle swarm optimization method, which enabled a faster sliding mode plane reaching and a more efficient buffeting reduction. The vehicle lateral motion model and the proposed control strategy were verified under various conditions simulation experiments using the MATLAB/CarSim software. Further, a rapid prototype development system of autonomous vehicles was built with A&D 5435, on which vehicle tests were carried out. The results show that precise control for vehicle lateral motion can be realized through a sliding mode control strategy based on the improved RBF neural network. The uncertainty of system modeling is compensated to a certain extent and buffeting of the steering wheel angle can be reduced. Moreover, the lateral distance deviation and heading angle deviation are controlled within acceptable range. The vehicles trajectory highly matches the desired path. When the vehicle is driven with a constant speed on the testing ground, the relative error of maximum difference of the steering wheel angle between the experimental and simulation results is 5.8%. The relative error of maximum difference of the lateral deviation between the experimental and simulation results is 6.2%, and the relative error of maximum difference of the heading angle deviation between the experimental and simulation results is 5.4%, based on the designed control strategy. The test results are consistent with the simulation results. 〖JP2〗Compared with the traditional sliding mode control strategy, under Alt 3 from FHWA condition, the relative error of maximum lateral deviation and heading angle deviation are reduced by 90.8% and 67.6%, respectively by using improved RBF neural network sliding mode control strategy. Under the double lane change condition, the relative errors are reduced by 63.4% and 69.9% respectively, and the under Sshaped lane condition, the relative errors are reduced by 54.4% and 39.6% respectively. 2 tabs, 10 figs, 22 refs.
Last Update: 2018-10-23