Decentralized receding horizon control with application to multiple vehicle systems

Receding horizon control (RHC) has been one of the most popular control approaches recently due to its capability to achieve optimal performance in the presence of saturation constraints. There have been numerous new research results for RHC (also referred to as model predictive control) in the process control community. However, due to the high computational cost, associated with the numerical optimization problem, RHC has not often been successfully implemented on multiple vehicle systems with fast dynamics. Decentralized receding horizon control (DRHC) is a new promising approach to reduce the computational burden of RHC. It allows the division of the computation problem into smaller parts which are solved using a group of computational nodes. This results in a substantial reduction in the computational time required for RHC. This thesis involves modeling of wheeled and hovercraft vehicles including actuator dynamics. It then applies the DRHC approach to the vehicles and implements the DRHC systems in virtual reality simulations and an experimental setup. Together, these results establish a new and useful framework for applying RHC to multiple vehicle problems