An AUV Systems Model Predictive Control Approach

This thesis concerns a novel control framework of the Model Predictive Control (MPC) designated by attainable Set-MPC type that seeks to conciliate performance optimization and state feedback control under very strict on-line computational constraints. The key novel idea that pervades throughout the main contributions of the thesis consists in transferring very substantial computational burden associated with the building blocks of the conventional MPC scheme to the off-line stage, by taking advantage of the time invariance of fundamental subsystems. The challenges of controlling single AUV and formations of AUV systems served as a key anchor to inspire, develop and illustrate the contributions of the thesis. The main reasons to choose this class of applications are manifold. The underwater environment is complex and poses tremendous challenges for the design of advanced data gathering systems. Space (required for payload and other devices), and energy (required for the actuation, sensing, computation, and communication) are at a premium and communications, typically merely acoustic, are difficult due to very low data rates, unreliability and power hungry. This makes the case extremely efficient management of onboard resources and this implies the need of optimization in a context of a state feedback control. The MPC framework suits these requirements. However, it suffers from the drawback of requiring intensive computation inherent to solving optimal control problems in real-time. Hence, the relevance of the research undertaken in this thesis. Besides the necessary contextual items, notably the problem statement, challenges analysis, and a directed and commented state-of-the-art review, this thesis includes an in depth assessment of the application of conventional MPC scheme to a simple AUV formation control scenario that encompassed not only software simulation but also hardware-in-the-loop with field data context. Based on the assessment of the application of the conventional MPC scheme, the AS-MPC scheme was developed. This requires the off-line computation of the Attainable Set and of the system Value Function to be adapted in the on-line context with a very small computational effort. Results on asymptotic optimality, and asymptotic stability, required to formally ensure the desired properties of the AS-MPC scheme were proved. Moreover, a discussion on robustness and computational tractability and the migration of some conclusions and results from conventional MPC schemes to the ASMPC scheme was included, giving rise to the Robust AS-MPC (RAS-MPC) also developed in the context o this thesis. Given the complexity (even in the off-line stage) of computing Attainable Sets and Value Functions, in this thesis we proposed a novel approach to approximate these sets through a cloud of points with the suitable properties. Finally, given the hybrid that is, discrete event and continuum-time driven nature of the envisaged class of systems, this thesis also includes an analysis of critical issues arising in this context. Now, even for the AS-MPC scheme, there is a lot of on-line computational effort that cannot be transferred to the off-line stage. By resorting to well-established Process Systems Engineering methodologies, an accurate as possible hybrid control system is developed whose a priori decoupling of discrete-event and continuum time components enables to represent the overall system through an hybrid automaton that will provide the controlled dynamics (in a hybrid systems sense) to the AS-MPC (or, obviously, RAS-MPC). The resulting control architecture is explained through illustrative examples related to motion control and obstacle collision avoidance activities. Finally, a number of conclusions and open issues that emerged from the research effort underlying this thesis are presented and discussed.