| Suspension is an important subsystem of the automobile,which not only supports force transmission but also cushions and absorbs vibration,ensuring the ride comfort and handling stability of the automobile.Traditional suspension/semi-active suspension cannot output control force,or dynamic adaptive adjustment,in the following performance,and the vibration reduction effect is still behind the active suspension.In active suspension,hydraulic/pneumatic type and hydraulic electric type generally have many modules,large volume,hysteresis in response,and poor sealing performance for long time operation due to their transmission characteristics.The electromagnetic type has the advantages of sensitive response,large controllable stroke,and easier realization of energy regeneration and storage utilization,especially the rotary type has a higher energy regeneration capacity than the linear type.In this paper,multi-link parallel mechanisms and permanent magnet rotary motors are used as energy regeneration and recovery schemes to solve energy regeneration and optimal control problems in suspension systems.Theoretical analysis,system modeling,simulation,and experimental comparison are used to solve the core problems of solving the drive function of the multi-link mechanism from physics and geometry and finding the optimal suspension control strategy based on vehicle estimation.The algebraic geometry and kinematics of the multi-link mechanism and the inverse solution of pavement parameters using suspension vibration parameters are deeply analyzed and studied.First of all,to improve the control effect of active suspension,reduce the energy consumption of output control force,and achieve the goal of energy saving and emission reduction.A new multi-link mechanism is designed.Based on the theory of space mechanism and spinor theory,the relation between vibration amplitude and rotation Angle is deduced and simulated.A general conclusion on the kinematic and geometric characteristics including the motion dead Angle and interference is given.A new parameter K is introduced to describe the enveloping surface of the outer profile hyperboloid when the connecting rod rotates.Then the position and posture of the whole car body are explored to achieve the dimensionality reduction of the control algorithm.The general theoretical formula of energy feed characteristic is deduced,the prototype is manufactured and assembled on the test vehicle,and the bench and real vehicle tests are carried out.The results show that the BA of LQR-controlled energy-fed suspension is 20.56% smaller than that of passive suspension when driving on B-class road surface.The energy regeneration efficiency was 14.28%,and the self-supply efficiency was 54.66%.Secondly,to solve the problems such as the need for special large equipment or the establishment of a large number of training sets,the least square system identification method is introduced,combining the differential equation of vehicle dynamics and the expression of random variable pavement excitation,and the general expression of variable pavement estimation is derived theoretically,which provides a theoretical basis for the reverse estimation of pavement power spectral density(PSD).In the test,the sensor is used to collect the acceleration of the body and chassis of the test vehicle,successfully decouple the vehicle speed,reverse estimate the road surface information difficult to collect by the sensor,and obtain the road elevation in the pure spatial domain.The accuracy is 98.2%,and the relative error is also within 5%,which has a high road surface identification ability.Then,to solve the problem that the active suspension cannot meet the driving requirements of complex and variable working conditions under a single control parameter,four control modes including energy feed mode are established.The switching threshold between different modes is determined with PSD and speed as the observed values,and the discrete mathematical discriminant is established to build the theoretical basis for mode switching.For control optimization under different modes,particle swarm optimization(PSO)is used to solve the optimal state feedback matrix of Linear Quadratic Regulator(LQR)control under different road conditions and working conditions.Simulation and real vehicle tests demonstrate the superiority of the proposed multi-mode switching control strategy over passive and traditional LQR control.Finally,to solve the problem of a large amount of data processing and slow tracking for PSD reverse solution,an active suspension control strategy based on online road identification and offline call to the weight coefficient database was proposed.To solve the problem of a long response time of optimization control,a genetic algorithm(GA)was used to optimize LQR control,and the Mahalanobis distance discrimination model with PSD,speed,and facial expression parameters was established to improve the system response speed.At the same time,the delay strategy is used to solve the complicated problem of frequent switching.Build d SPACE real-time pavement parameter estimation,and assemble Kinect2.0 to collect face information in real-time.Specifically,the model can identify different working conditions in the process of a sudden change of road surface from Class B to Class D,speed from 30 km/h to 60 km/h to 30 km/h,and face model parameters from 0 to-1 to 0.At the same time,the delay strategy is implemented to avoid frequent switching.Finally,the weight coefficient under corresponding working conditions is selected from the constructed offline library.Compared with the passive suspension and the traditional LQR suspension,it realizes the intelligent adjustment of the whole road condition of the vehicle riding comfort and ride comfort under the real-time off-line database calling strategy of different speeds,road surface,and driver expression. |
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