Particle Filtering and Optimal Control for Vehicles and Robots
This thesis covers areas within estimation and optimal control of vehicles, in particular four-wheeled vehicles. One topic is how to handle delayed and out-of-sequence measurements (OOSMs) in tracking systems. The motivation for this is that with technological development and exploitation of more sensors in tracking systems, OOSMs gain more significance in various applications. The thesis derives a Bayesian formulation of the OOSM problem for nonlinear state-space models, when a linear, Gaussian substructure is present. This formulation is utilized when developing two particle-filter algorithms for the OOSM problem. The algorithms improve estimation accuracy and tracking robustness, compared with methods that do not utilize the linear substructure. A second topic is sensor fusion for improved autonomy in vehicles. A novel approach to model-based joint wheel-slip and motion estimation of four-wheeled vehicles is developed. Unlike other approaches, the method explicitly models the nonlinear slip dynamics in the state and measurement equations. Excellent and consistent accuracy for all relevant states are reported, both during steady-state driving and aggressive maneuvering. The method applies to general classes of four-wheeled vehicles and it only assumes kinematic relationships. Optimization-based control methods have found their way into automotive applications. Optimal control for vehicles typically results in control inputs that give aggressive maneuvering. Proper models are therefore crucial. An investigation on what impact different vehicle models and road surfaces have on the optimal trajectories in safety-critical maneuvers is presented. One conclusion is that the control-input behavior is highly sensitive to the choice of chassis and tire models. Another conclusion is that the optimal driving techniques are different depending on tire-road characteristics. The conclusions motivate the design of a novel, two-level hierarchical approach to optimal trajectory generation for wheeled vehicles. The first novelty is the use of a nonlinear vehicle model with tire modeling in the optimization problem at the high level. This provides for better coupling with the low-level controller, which uses a nonlinear model predictive controller (MPC) for allocating the torques and steer angles to the wheels. This is combined with a linear MPC, which is used when the nonlinear MPC fails to converge in time. The thesis also describes a hierarchical design flow for performing online, minimum-time trajectory generation for four-wheeled vehicles with independent steer and drive actuation, combined with real-time obstacle avoidance. The approach is based on convex optimization. It therefore allows fast computations, both for trajectory generation and online feedback-based obstacle avoidance. The proposed method is fully implemented on a pseudo-omnidirectional mobile platform and evaluated in experiments in a path-tracking scenario.
Control Engineering, particle filtering, optimal control, automotive systems, out-of-sequence measurement, autonomy, model predictive control, sensor fusion, wheel slip, dynamic optimization
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Department of Automatic Control, Lund Institute of Technology, Lund University