PID Parameters Tuning For Vehicle Lateral Stability Based On Optimization Strategy

Yaw rate is an important indicator to characterize vehicle lateral stability, so itbecomes a key control variable in the vehicle handling and stability control system.On the other hand, PID is one of the most common methods since it is easy to beimplemented, but its engineering application is limited for parameters tuning. Basedon surrogate model theory, taking the amplitude of front wheel steering angle and theparameters of PID controller as the inputs of surrogate model, and taking the keypoints of the vehicle yaw rate curve as the outputs of surrogate model, an8degrees offreedom vehicle dynamics surrogate model with PID controller and anADAMS/MATLAB vehicle dynamic surrogate model integrated with PID controllerare built. This method can not only avoid considering the time state factors ofsurrogate model, but also can optimize PID controller parameters quickly. Numericalsimulations demonstrate that the surrogate model based on the presented method ishigh precision, and the vehicle yaw rate can track the desired yaw rate accuratelyrecurred to the PID controller, by which the vehicle lateral stability is improvedeffectively. The main contents of this paper are as follows:（1）An8degrees of freedom vehicle dynamics surrogate model is builtintegrated with PID controller. The objective function available for optimization isconstructed by establishing the response surface of the key points of yaw rateresponse curve, and then the PID parameters required are optimized rapidly.（2）An ADAMS/MATLAB vehicle dynamic surrogate model integrated withPID controller is built. Take the key points of the vehicle yaw rate curve to bulidradial basis function surrogate model.Construct the objective function available foroptimization.and the PID controller parameters can be optimized quickly.（3）With the combination of the optimum PID controller parameters of8degrees of freedom vehicle dynamics model and a fuzzy controller, a fuzzy PIDcontroller is established. Because that the fuzzy PID control effect partly depended onoriginal setting of PID control parameters, therefore, the optimum PID controlparameters obtained from this paper are regard as the original PID parameters offuzzy PID controller, and its control effect is verified by the method of numericalsimulation.