Receding Horizon Control for Uncertain Pursuit-Evasion Games

A robust technique for handling parameter and strategy uncertainty in a pursuit-evasion framework is developed. The method is a receding horizon controller valid for problem classes with singularly perturbed trajectories that approximates the optimal feedback solution with small loss in optimality. The receding horizon method is used to ensure the controller is robust to incorrect or extraneous information about an opposing player's dynamics or strategy. A simple analytic pursuit-evasion game motivates the method by demonstrating that the receding horizon solution closely approximates the optimal solution and may be solved much faster. Simulations of a nonlinear game show that the receding horizon controller is especially useful when it is unknown whether the opposing player is performing an active or passive maneuver. In several cases, the receding horizon controller is shown to become more effective than a game-optimal controller acting with an incorrect strategy estimate. The major limitation of the technique for a nonlinear system is the expensive solution time; therefore, the optimal control problem is translated to a nonlinear programming problem and the test cases are repeated. Finally, the test cases are run on hardware to validate the method for real-time practical operation. The singular-perturbation algorithm applied herein is valid only for a small subset of all pursuit and evasion games. Nonetheless, the methods developed here can in theory be used for any generic game scenario, given that sufficient computing power is available to find the numerical solutions.

Model-Guided Data-Driven Optimization and Control for Internal Combustion Engine Systems

The incorporation of electronic components into modern Internal Combustion, IC, engine systems have facilitated the reduction of fuel consumption and emission from IC engine operations. As more mechanical functions are being replaced by electric or electronic devices, the IC engine systems are becoming more complex in structure. Sophisticated control strategies are called in to help the engine systems meet the drivability demands and to comply with the emission regulations. Different model-based or data-driven algorithms have been applied to the optimization and control of IC engine systems. For the conventional model-based algorithms, the accuracy of the applied system models has a crucial impact on the quality of the feedback system performance. With computable analytic solutions and a good estimation of the real physical processes, the model-based control embedded systems are able to achieve good transient performances. However, the analytic solutions of some nonlinear models are difficult to obtain. Even if the solutions are available, because of the presence of unavoidable modeling uncertainties, the model-based controllers are designed conservatively

Simultaneous Nonlinear Model Predictive Control and State Estimation: Theory and Applications

As computational power increases, online optimization is becoming a ubiquitous approach for solving control and estimation problems in both academia and industry. This widespread popularity of online optimization techniques is largely due to their abilities to solve complex problems in real time and to explicitly accommodate hard constraints. In this dissertation, we discuss an especially popular online optimization control technique called model predictive control (MPC). Specifically, we present a novel output-feedback approach to nonlinear MPC, which combines the problems of state estimation and control into a single min-max optimization. In this way, the control and estimation problems are solved simultaneously providing an output-feedback controller that is robust to worst-case system disturbances and noise. This min-max optimization is subject to the nonlinear system dynamics as well as constraints that come from practical considerations such as actuator limits. Furthermore, we introduce a novel primal-dual interior-point method that can be used to efficiently solve the min-max optimization problem numerically and present several examples showing that the method succeeds even for severely nonlinear and non-convex problems. Unlike other output-feedback nonlinear optimal control approaches that solve the estimation and control problems separately, this combined estimation and control approach facilitates straightforward analysis of the resulting constrained, nonlinear, closed-loop system and yields improved performance over other standard approaches. Under appropriate assumptions that encode controllability and observability of the nonlinear process to be controlled, we show that this approach ensures that the state of the closed-loop system remains bounded. Finally, we investigate the use of this approach in several applications including the coordination of multiple unmanned aerial vehicles for vision-based target tracking of a moving ground vehicle and feedback control of an artificial pancreas system for the treatment of Type 1 Diabetes. We discuss why this novel combined control and estimation approach is especially beneficial for these applications and show promising simulation results for the eventual implementation of this approach in real-life scenarios