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Learning based Predictive Error Estimation and Compensator Design for Autonomous Vehicle Path Tracking

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Document pages: 5 pages

Abstract: Model predictive control (MPC) is widely used for path tracking of autonomousvehicles due to its ability to handle various types of constraints. However, aconsiderable predictive error exists because of the error of mathematics modelor the model linearization. In this paper, we propose a framework combining theMPC with a learning-based error estimator and a feedforward compensator toimprove the path tracking accuracy. An extreme learning machine is implementedto estimate the model based predictive error from vehicle state feedbackinformation. Offline training data is collected from a vehicle controlled by amodel-defective regular MPC for path tracking in several working conditions,respectively. The data include vehicle state and the spatial error between thecurrent actual position and the corresponding predictive position. According tothe estimated predictive error, we then design a PID-based feedforwardcompensator. Simulation results via Carsim show the estimation accuracy of thepredictive error and the effectiveness of the proposed framework for pathtracking of an autonomous vehicle.

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