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Research On Hybrid Control Strategy Of Semi-active Quarter Vehicle Suspension System Based On Magnetorheological Damper

Posted on:2021-04-23Degree:MasterType:Thesis
Country:ChinaCandidate:R X XuFull Text:PDF
GTID:2392330611479696Subject:Mechanical engineering
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With the progress of society,science and technology,and the rapid development of the automotive industry,people are increasingly demanding automotive performance.As one of the important components of vehicle driving systems,the performance of the suspension system will directly affect ride comfort and stable handling and driving safety.The traditional passive suspension improves the vehicle vibration through the cooperation of springs and damping elements,but its structural parameters are fixed and cannot be changed,so the damping effect is limited.The semi-active suspension can change the suspension damping or stiffness coefficient through the control algorithm and control the output damping force,so as to control the suspension performance,so it has a good damping effect.And compared with the active suspension,the semi-active suspension has a simple structure,low energy consumption,and low cost.The semi-active suspension using magnetorheological damper(MRD)has excellent performance,can achieve the vibration reduction effect similar to that of the active suspension system,and has low energy consumption,fast response speed,large output damping force,and wide adjustable range,and the advantages of adjustable forward and reverse.So it has important application value.The two core issues in the study of magneto-rheological semi-active suspension systems are the establishment of mathematical models that accurately describe the mechanical properties of magneto-rheological dampers and the design of semi-active suspension control strategies.This paper establishes a suspension system model and conducts numerical simulation analysis for these two issues.The main tasks are as follows:(1)The working principle and working mode of the magnetorheological damper are introduced.The different mechanical models of the magnetorheological damper and the characteristics of each model are described.Use the INSTRON experimental system to test the existing MRD of the research group,analyze its speed characteristics and displayed power characteristics,and select the improved hyperbolic tangent model as the mathematical model of the MRD in this study.The genetic algorithm was used for parameter identification,and the simulation results were compared with the experimental values.The results show that the model obtained by parameter identification can describe the mechanical characteristics of MRD well.(2)Establish a road surface input model and a 1/4 vehicle semi-active suspension model,and establish the system state equation.Select vehicle acceleration,suspension dynamic travel,and tire dynamic displacement as the evaluation indicators of suspension performance.Analyze the influence of suspension parameters on suspension transmission characteristics(3)Design the fuzzy controller and fuzzy PID controller,and control the input current of the magnetorheological damper by designing reasonable fuzzy rules,so as to control the output damping force of the damper,and finally achieve the role of controlling the suspension vibration reduction.Modeling and simulation in MATLAB / Simulink environment,the results verify that these two control methods are effective,and fuzzy PID control is better.At the same time,due to the shortcomings of dependency and subjectivity of fuzzy control rule formulation,complicated rule formulation process and tedious debugging,a fuzzy LQG control strategy is proposed.Compared with fuzzy control and fuzzy PID control,this method has better damping effect through comparison and analysis.When driving on random roads,the rms value of vehicle body acceleration is reduced by 43.83% compared with passive suspension,which has better vehicle smoothness and driving safety.
Keywords/Search Tags:magnetorheological damper, semi-active suspension, mathematical model, fuzzy control, fuzzy PID control, fuzzy LQG control
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