Research And Implication Of Aaptive Mtion Cntrol System For Intelligent Vehicles

The smart car can independently complete tasks such as road identification,obstacle avoidance and motion control during driving,which has outstanding advantages in reducing traffic accidents,traffic congestion and environmental pollution.At present,the pre-attack strategy adopted by most visual navigation type smart cars has the problem that the preview distance cannot be adaptively adjusted following the curvature of the road.When the preattack distance is large,the steering mechanism will cause premature response.In addition,the existing lateral motion control mostly regards the speed as a constant value,and there is a problem that the speed of the vehicle is too high when entering the curve.In order to avoid the premature response of the steering mechanism of the smart car and ensure the ride comfort and safety of the vehicle,this paper proposes a dynamic horizontal and vertical joint control method and control strategy optimization method based on the dynamic double-point preview strategy.The adjustment strategy of the preview distance and its parameter design method,and the effectiveness and accuracy of the proposed control method are verified by simulation and experiment.The content of this article mainly includes:1)This paper constructed the structure of the double closed-loop motion control system based on azimuth deviation and velocity deviation,analyzed the force of the vehicle and derived the mathematical model of the three-degree-of-freedom dynamics of the vehicle.On this basis,this paper further studied the geometry of the vehicle-road and the relationship and the influence of vehicle motion on lateral deviation and azimuth deviation,given the visual preview motion model,and introduced the calculation method of corner safety speed and vehicle centroid coordinate.2)Aiming at the problem of premature response of the steering mechanism in the case of the smart car single-point preview mode,this paper proposed the dynamic double-point preview strategy,and given the two dynamic adjustment methods of the pre-peep point and the method of determining the preview distance.Based on this,this paper designed a horizontal and vertical fuzzy controller based on azimuth deviation and velocity deviation.Based on the design of fuzzy controller,the domain of the fuzzy controller input and output variables,this paper given the quantization factor and the fuzzy method of input and output,introduced The curve form of the typical membership function,and selected then the triangle and trapezoid of the membership function curve,given the method of determining the fuzzy control rules,the expression form of the control rules and the control rules table.Finally,this paper introduced the Mamdani fuzzy inference method and the center of gravity method for defuzzification.3)The fuzzy control rules obtained by relying on the driver's experience have certain subjectivity and limitations.The genetic optimization algorithm was used to optimize the control rules of the fuzzy controller,so that the fuzzy controller has the purpose of selfadaptation.This paper introduced the genetic optimization method around fuzzy control rules,and the coding method of control rules,and given the correspondence between fuzzy linguistic variables and coding.According to the performance index function of horizontal and vertical control,this paper determined the optimal mathematical model to be solved,and given the common methods of genetic manipulation and the basis for selection.The simulation experiment and the experimental results show that the designed dynamic two-point preview strategy can adaptively adjust the preview distance according to the road curvature and vehicle speed.The designed control algorithm can adaptively adjust the vehicle speed according to the curvature of the preview point,which avoid the premature response of the lateral controller caused by the excessive distance of the preview,and ensure that the vehicle can decelerate to a safe speed corresponding to the curvature of the road before entering the curve;in addition,the optimized controller has a faster response performance,the vehicle a higher precision follow-up effect.