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Cooperative Control Of Laterally Interconnected Air Suspension Based On Multi-Agent Theory

Posted on:2021-02-14Degree:MasterType:Thesis
Country:ChinaCandidate:A C ShenFull Text:PDF
GTID:2392330623479442Subject:Traffic and Transportation Engineering
Abstract/Summary:PDF Full Text Request
The air suspension has the advantages of good variable stiffness characteristics and adjustable body height,and is increasingly used in the field of vehicles.As one of the derivative structures of the air suspension,the laterally interconnected air suspension has better vibration isolation and torque reduction capabilities,which can further improve the ride comfort and handling stability of the vehicle.As the controllable unit of the laterally interconnected air suspension,the height and interconnection status of the car body have attracted much attention from scholars at home and abroad.By adjusting the height of the body,the vehicle's trafficability and handling stability can be improved,and the adjustment of the interconnected state can improve the ride comfort of the vehicle.However,multi-controllable units also bring more problems of coupling conflicts.How to achieve the coordination between the height of the vehicle body and the interconnected state in the laterally interconnected air suspension is the research focus of this article.Based on the indepth analysis of domestic and foreign research deficiencies,this paper takes horizontal interconnected air suspension as the research object,and researches on its collaborative control.First,based on the thermodynamics and fluid mechanics theory,a laterally interconnected air spring system model was established.Taking the actual structure of the test sample as a reference,a seven-degree-of-freedom vehicle dynamics model with non-independent suspension was established based on the vehicle dynamics theory.In order to improve the parameters of the simulation model,an air spring characteristic test was designed.Based on the Arduino electronic prototyping platform,a test sample car information acquisition system was constructed,and the actual car test verified the accuracy of the simulation model.Secondly,combined with the agent theory,the body height control agent and the interconnected state control agent are constructed.Relying on the process inference system model framework,the target body height and hysteresis interval selection process are described in detail,and the reinforcement learning algorithm is introduced into the agent interpreter to improve the agent's online learning ability.By designing different simulation conditions,the effectiveness of the agent's learning behavior and the applicability of the learning results are proved.The simulation results show that the body height control agent control improves vehicle handling stability,and the interconnected state control agent control improves vehicle riding comfort.Finally,based on the multi-agent theory,to coordinate the coupling conflict between the height of the vehicle body and the interconnected state,a multi-agent cooperative control system for laterally interconnected air suspension was established.In the collaborative control system,the agent's collaborative control strategy is formulated by dominating the agent's calculation of the agent's control target.The body height control agent and the interconnected state control agent complete the control target issued by the control agent by adjusting the state of the solenoid valve controlled by each.Through mutual cooperation between agents,the coupling conflict between the height of the vehicle body and the interconnected state is optimized to improve the overall performance of the vehicle.Under the environment of MATLAB / Simulink,the system model is established and simulated.The simulation results show that: under the control of the built multiagent cooperative control system,the driving smoothness and handling stability of the vehicle are significantly improved.
Keywords/Search Tags:Air suspension, Laterally interconnected, Vehicle height adjustment, Cooperative control, Multi-agent
PDF Full Text Request
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