Vehicle Sideslip Angle and Road Friction Estimation Using Online Gradient Descent Algorithm

Estimating vehicle sideslip angle and road friction in real time is of great significance for vehicle stability control and intelligent vehicle lateral control. These parameters are often difficult to obtain directly and the high cost of measuring instruments restricts their application in general vehicle control. Therefore, an estimation method based on online gradient descent (OGD) algorithm for vehicle sideslip angle and road friction is proposed. For the front-wheel-steer and front-wheel-drive vehicle, the vehicle's lateral dynamics model is established with smaller assumptions. And an unknown input observer is designed to estimate the tire's lateral force of the rear wheel. On the basis of this, the parameter estimation is transformed into the parameter optimization problem and the cost function is designed by using the nonlinear tire model, i.e., magic formula, and its gradient formula. Then, the OGD algorithm is used to estimate the sideslip angle and road friction, respectively. The effectiveness of the proposed method is evaluated via numerical simulation based on MATLAB/Simulink and CarSim software platform. The results show that the method can reliably and accurately estimate the vehicle sideslip angle and the road friction under a variety of test conditions. The proposed algorithm can effectively suppress the influences of sensor noise, longitudinal velocity change, and tire force nonlinearity on the estimation results.