Coalitional Model Predictive Control based on Partial Cooperation

Résumé

This doctoral thesis investigates the development of coalitional strategies based on model predictive control (MPC). Our study emphasizes the necessity of managing increasingly complex systems while maintaining efficiency and scalability. To this end, we explore the limits of decentralization in coalitional control, developing algorithms that minimize the amount of data communicated in the network to preserve privacy and enhance autonomy, while still maintaining acceptable levels of performance. The proposed strategies achieve a trade-off between computational efforts and performance. While performance may degrade compared to other coalitional methods, this trade-off allows for increased autonomy and reduced communication requirements. First, we survey the existing literature, motivating the need for non-centralized control strategies to manage large-scale systems, and we introduce the main objectives of this PhD dissertation. The contributions of this thesis can be broadly classified into two main parts. In the first one, we introduce a novel variable decomposition for partially cooperative systems. In this regard, we distribute coupling variables among local agents, developing stochastic and robust coalitional MPC approaches that allow agents to share control authority over certain input portions while maintaining privacy over others. Moreover, we study the limits of control authority delegation by establishing the maximum portion of an agent’s input space that can be ceded to another agent while ensuring the feasibility of the global optimization problem. This ensures that decentralized operations respect system constraints with minimal data communication. On the other hand, the second part focuses on systems with shared resources, developing coalitional methods to decouple joint constraints, allowing agents to operate with greater autonomy. Since this thesis has been developed in the framework of OCONTSOLAR project, the control algorithms presented in this second part are applied to the case study of large-scale solar plants. Finally, we provide conclusions and future research directions of our main contributions