Browse Publications Technical Papers 2022-01-0086

Model in the Loop Control Strategy Evaluation Procedure for an Autonomous Parking Lot Sweeper 2022-01-0086

A path tracking controller is essential for an autonomous vehicle to navigate a complex environment while avoiding obstacles. Many research studies have proposed new controller designs and strategies. However, it is often unclear which control strategy is the most suitable for a specific Autonomous / ADAS user application. This study proposes a benchmark workflow by comparing different control observer models and their control strategies integration for an autonomous parking lot sweeper in a complex and dense environment at low-speed utilizing model-in-the-loop simulation. The systematic procedure consists of the following steps: (1) vehicle observer model validation (2) control strategy development (3) model-in-the-loop simulation benchmark for specific user scenarios. The kinematic and dynamic vehicle models were used to validate the truck’s behavior using physical data. Various lateral controllers, including Model predictive control (MPC), Linear Quadratic Regulator (LQR), Stanley controller, Pure pursuit, and PID controller, were implemented and tested in the IPG model in the loop (MIL) simulator to determine the best control strategy for the autonomous sweeper. Control effort, trajectory smoothness, cross-track error, heading error, and computation time were used as evaluation metrics to assess the performance of the different controllers. The model validation analysis determined that the dynamic bicycle model best approximates the truck’s dynamics. Simulation results indicate that MPC obtained the lowest control effort and the smoothest trajectory compared to other controllers. The systematic procedure presented in this study effectively determined the control strategy best suited for a parking lot sweeper; nevertheless, it can be applied to establish the control strategy for other applications.


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