Lateral Model Predictive Control for Autonomous Heavy-Duty Vehicles
Sensor, Actuator, and Reference Uncertainties
Time: Tue 2020-09-15 10.00
Subject area: Electrical Engineering
Doctoral student: Goncalo Collares Pereira , Reglerteknik
Opponent: Associate Professor, Electrical engineering Paolo Falcone, Chalmers tekniska högskola
Supervisor: Jonas Mårtensson, Signaler, sensorer och system, Biomedicinsk fysik och röntgenfysik, Integrated Transport Research Lab, ITRL, Reglerteknik; Bo Wahlberg, Signaler, sensorer och system, Optimeringslära och systemteori, Reglerteknik; Henrik Pettersson, Scania AB
Autonomous vehicle technology is shaping the future of road transportation. This technology promises safer, greener, and more efficient means of transportation for everyone. Autonomous vehicles are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where heavy-duty vehicles (HDVs) operate. Although research for autonomous systems has boomed in recent years, there are still many challenges associated with them. This thesis addresses lateral motion control for autonomous HDVs using model predictive control (MPC).
First, the autonomous vehicle architecture and, in particular, the control module architecture are introduced. The control module receives the current vehicle states and a trajectory to follow, and requests a velocity and a steering-wheel angle to the vehicle actuators. Moreover, the control module needs to handle system delays, maintain certain error bounds, respect actuation constraints, and provide a safe and comfortable ride.
Second, a linear robust model predictive controller for disturbed discrete-time nonlinear systems is presented. The optimization problem includes the initial nominal state of the system, which allows to guarantee robust exponential stability of the disturbance invariant set for the discrete-time nonlinear system. The controller effectiveness is demonstrated through simulations of an autonomous vehicle lateral control application. Finally, the controller limitations and possible improvements are discussed with the help of a more constrained autonomous vehicle example.
Third, a path following reference aware MPC (RA-MPC) for autonomous vehicles is presented. The controller makes use of the linear time-varying MPC framework, and considers control input rates and accelerations to account for limitations on the vehicle steering dynamics and to provide a safe and comfortable ride. Moreover, the controller includes a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller is verified through simulations and through experiments with a Scania construction truck. The experiments show an average lateral error to path of around 7 cm, not exceeding 27 cm on dry roads.
Finally, the nonlinear curvature response of the vehicle is studied and the MPC prediction model is modified to account for it. The standard kinematic bicycle model does not describe accurately the lateral motion of the vehicle. Therefore, by extending the model with a nonlinear function that maps the curvature response of the vehicle to a given request, a better prediction of the vehicle's movement is achieved. The modified model is used together with the RA-MPC and verified through simulations and experiments with a Scania construction truck, where the improvements of the more accurate model are verified. The experiments show an average lateral error to path of around 5 cm, not exceeding 20 cm on wet roads.