Advanced nonlinear control of vehicles and their formations

Automated control of ground vehicles is a challenging task of the automotive industry. It is an extensively researched topic that became the foundation of control of future driverless vehicles. The thesis takes a step further in the field of nonlinear autonomous vehicle and formation control that is capable of coping with high velocity cruising. In the first part, a three-level hierarchical framework is proposed for the optimal control and trajectory planning of four wheel driven (4WD) ground vehicles considering state and input constraints. The high-level controller’s objective is to compute global time optimal and local time sub-optimal control based on the simplified two-wheel driven vehicle model for fixed or arbitrary paths. This is accomplished by solving a dynamic nonlinear optimal control problem based on direct discretization and multiple shooting method with receding horizon extension. The middle-level controller optimally distributes the driving forces to all four wheels in a least squares sense. The low-level controller keeps the deviation from the given trajectory at minimum while initial state perturbations are present. The closed loop utilizes a discrete low-level model predictive (MPC) integral controller resulting optimal trajectory tracking of the nonlinear 4WD vehicle. In the second part, a two-level autonomous vehicle control system is developed. It allows high-speed (30 m/s) tracking control of unmanned ground vehicles despite of load variations. Changes in the load caused by the unknown center of mass point are considered based on the realistic 16 degree of freedom multibody vehicle model. The vehicle is modeled as a tree-structured mechanical system with moving base and its dynamics is obtained by utilizing Appell’s method and the Gibbs-function. The high-level controller incorporates the kinematics of the vehicle in a finite horizon linear time-varying quadratic optimal controller that locally stabilizes the closed loop system about the perturbations from the prescribed trajectories. The states of the vehicle are estimated with a two-stage Kalman Filter (KF) using realistic GPS and inertial (IMU) sensory information based on the kinematic differential equation of navigation. Furthermore, the effect of rolling and pitching during the maneuver is minimized by low-level controller system which employs PID-type controllers based on selectable error differential equations. The simulations show promising results with small lateral path deviation for aggressive maneuvers near the physical limitations of the vehicle. In the third part, two decentralized leader-follower formation structure is developed for underactuated ground vehicles. The first approach elaborates an adaptive control law based on kinematics equations. The follower vehicles have limited knowledge about the leader’s states and the unknown term containing the velocity information of the leader is estimated using neural networks with online adaptive weight tuning laws. All closed loop error signals are guaranteed to be uniformly ultimately bounded. The second approach exploits the principles of analytical mechanics. The group of vehicles are considered as a multibody system and coordinated motion is accomplished by imposing constraint forces. These forces maintain the prescribed formation and by stabilizing feedback all closed loop error signals can be rendered globally exponentially stable, giving rise for an easily configurable formation control framework.