Continuous time Model Predictive Control for Economic Optimization
This thesis addresses the design of optimization based control laws for the case where convergence to a desired set point, minimization of an arbitrary performance index, or a combination of the two, is the desired objective. The results are developed within the sample data Model Predictive Control (MPC) framework considering constrained nonlinear continuous time time varying dynamical systems. For a given time sampling, a sample data MPC control strategy consists of i) choosing among all future finite horizon predictions of state and input trajectories of the system the one that minimizes the given performance index, ii) applying the optimal input trajectory to the system until a new time sample is reached, and iii) iterating this process. The performance index is chosen to describe the specific control problem under consideration. In a classic Tracking MPC framework, where the main goal is to steer the state of the system to a desired steady state, the performance index is properly chosen to penalize the distance from the current state to a desired one. In order to capture more complex control objectives, in recent years a growing attention has been dedicated to a new class of controllers that goes under the name of Economic MPC. Here, the term economic is used to stress the fact that the performance index is a general index of interest that we wish to minimize, e.g. economic, which does not denote the distance to a desired set point. This setting makes full use of the potentialities of optimization based control strategies. Although, it comes with disadvantages. In fact, by choosing an arbitrary performance index, it is difficult to predict, and therefore certify, the evolution of the closed loop system, which could potentially manifest undersirable behaviors. This thesis provides analysis and certification of a variety of closed loop behaviors stemming from the use Economic MPC controllers. A set of tools for design of provably correct MPC controllers is provided for the case where the performance index is of the Tracking MPC type, purely economic, or a combination of the two. The results focus the certification of both closed loop economic performance and closed loop state evolution. The proposed strategies are applied to a range of motion control problems for underactuated vehicles. An MPC controller for Trajectory Tracking and Path Following with convergence guarantees is first proposed and then extended, using the results presented on Economic MPC, to address the control problems of distributed formation keeping, energy efficient trajectory tracking, and target following through highly observable trajectories.