Research On Intelligent Vehicle’s Path Tracking Control Strategy

Intelligent vehicle has always been the hotspot and difficulty point in the fieldof modern automobile research. With the development of control theory, more andmore new control theory and control method was applied to path tracking control ofthe intelligent vehicle, which made the question how to choose the most suitablecontrol method according to different road condition and driving condition become anew subject. An intelligent vehicle’s lateral control method based on RBF (RadialBasis Function) neuron network compensation and two kinds of improvedlongitudinal control method are proposed based on the research on several commonused methods in intelligent vehicle’s path tracking control. Additionally, a controlmethod of switching strategies based on different driving conditions is put forward,whose effectiveness is verified by several experiments.The geometic steering model, kinematic vehicle model and dynamic vehiclemodel that commanly used in intelligent vehicle’s path tracking lateral control arestudied.The non-preview geometric steering model lateral control method, thepreview geometric steering model lateral control method, the kinematic vehiclemodel smooth time-varying feedback lateral control method, the dynamic vehiclemodel based on optimal LQR (Linear Quadratic Regulator) method, the LQRmethod with a feed-forward term and the preview optimal LQR method up to6kinds of common control method in intelligent vehicle’s tracking control are studied.Through double lane change test and the circle course test, the respective advantagesand disadvantages of these6methods are studied, including: the controller’srobustness, special requirements of roads, the overshoot, the steady-state lateraltracking error, and their suitable situations, which has been the theoretical basis ofthe multiple control method switching theory.In order the to solve the lackness of the name dynamic vehicle model innonlinear area, a method based on RBF neural network compensation of theintelligent vehicle’s lateral path tracking control is raised.3kinds of RBF networkcompensation methods are studied, separetly based on model uncertain parts, modelpartitioned parts and model united parts. The stability of the system is analyzedbased on lyapunov function method, the advantages and disadvantages of these threekinds of compensation methods are compared through double lane change test,circle course test and a multi curvature road test. The RBF network compensationcontrol method based on model uncertain parts is finally chosen as the lateralcontrol method of an intelligent vehicle in nonlinear area.Two kinds of intelligent vehicle’s longitudinal tracking control methods which separately based on data fitting and FNNC (Fuzzy Neuron Network Control) areproposed on the basis of the analysis of those several common used intelligentvehicle’s speed control model. The data analysis shows that the longitudinal controlmethod based on FNNC is superior to longitudinal control method based on datafitting with respected to the control accuracy, but the output desired speed is toosensitive to the of change the input value and there is a certain degree of noisepollution. A method based on wavelet denoising is proposed in order to handle theoutput speed signal of FNNC, which has obtained good effect.A driving conditions classification method based on LVQ (Learning VectorQuantization) neuron network is raised, several groups of test data show that theLVQ neural network can effectively classify different driving conditions. Thecontrol method switch strategy’s theory is selecting different corresponding controlmethods according to different classification results of LVQ, trying to selectmethods with less calculation on the premise of guarantee the tracking controlaccuracy. The driver-vehicle closed-loop maneuverability evaluation index isintroducted in order to evaluate the designed longitudinal and lateral integratedcontroller of the intelligent vehicle’s path tracking. According to the evaluation testresults of the double lane change test and the serpentine test, the controller is withfarely good control effectiveness, but also with a heavy burden.The build process of the driving simulation platformis introduced from theworking theroy, hardware composition and software design aspects. With the aid ofCCD image acquisition equipment and the vehicle state acquisition equipment, theeffect of the controller is veritied through a real vehicle test, which shows that thetest results and simulation results are basically identical.