Model predictive control of cyber-physical systems

Cyber-Physical Systems (CPS) represent a groundbreaking technological advancement that integrates physical processes with computational resources and networking capabilities, heralding a significant leap in efficiency, functionality, and adaptability across various applications. From revolutionizing transportation through self-driving cars to enhancing energy distribution via smart grids, CPS are poised to be pivotal in the fourth industrial revolution, fundamentally altering daily life and work in a manner akin to the transformative impacts of the internet and the World Wide Web. Originating from the concept of merging digital and physical realms, CPS aim to create systems that are inherently intelligent, adaptive, and resilient, extending beyond traditional embedded systems by leveraging advancements in computing, communication, and control. These systems are characterized by a core architecture comprising physical components (sensors and actuators), cyber elements (computational and communication infrastructure), and control mechanisms (algorithms and software), working in unison through a feedback loop to dynamically interact with and respond to their environment. In this respect, the work done in this thesis is the application of Model Predictive Control (MPC) framework to CPS with the aim to an increase in operational efficiency, an increase in optimality with respect to resource allocation, and an increase in general responsiveness and adaptability of such systems to ambient variability. This thesis attempts to manifest the future implications in applying MPC to transform the management and control of these sophisticated cyber-physical systems within their crucial sectors via theoretical development and practical implementation of case studies. The control methodology discussed in this thesis regards the application of MPC in three case studies: in the frame of Power Systems, Smart Cities and Industry 4.0 in the space sector. The first work deals with the emerging complexities in modern transmission and distribution grids that arise through integration with distributed energy resources such as electric energy storage systems, renewable energy plants, and plug-in electric vehicles. The new issues are the intermittency in power generation from renewable sources and in the demand from electric vehicles present a new challenge to grids requiring advancement in grid control and optimization. Considering these challenges, in this work the candidate proposes a novel reconfiguration algorithm based on MPC for the dynamic configuration and re-configuration (topology) of the grid to minimize losses and to improve operational resilience in the presence of adverse events like faults or (cyber-)attacks. The algorithm progresses over the existing methods by removing the necessity of constantly connected grids to let autonomous grid islands be formed that can dynamically get connected and disconnected from the main grid. This research provides a critical review of existing network reconfiguration strategies, spanning between classic optimization-based methods, heuristics ((meta)heuristics), and machine learning-based solutions with their respective advantages and limits. It is hence observed that while the classic optimization methods actually give optimum solutions, they are afflicted by high computational costs. (Meta)heuristics are computationally efficient, though void of guarantees about the optimality of solutions. Machine learning based approaches, in particular Reinforcement Learning, promise policies that are near optimal but come at an enormously high demand for computational resources during training and also offer serious concerns about safety. In such a way, the proposed MPC-based solution combines the features of optimal control at a lower computational cost and adaptability for real-time applications. This means to be the breakthrough approach in network reconfiguration, bridging the gaps that exist within today's available methodologies and thereby offering a powerful, robust, efficient, flexible solution to meet challenges posed by today's modern, dynamic grid environment. The second work addresses a crucial challenge that urban greenhouse gases (GHG), primarily produced by buildings and transportation, with a focus on optimization of the intelligent traffic light (TL) control systems in mitigating road congestion. Given the global climate change efforts like the 2016 Paris Agreement and the EU 2019 Green Deal, the study would emphasize the need for viable urban traffic management strategies that could lead to significant GHG emission reductions, as a majority of such emissions originate from urban settings. Although an extensive literature on Intelligent TL controls is available today, it is found that there is a gap in adaptability and efficiency, mainly in real-time traffic conditions. A novel model predictive control strategy based on mixed-integer optimization has been proposed in this thesis to enhance the timings of TLs at intersections by an original approach different from classical fixed-timing strategies without any real-time reaction. The main contribution of this thesis lies in proposing an integrated MPC controller which determines both the optimal signal timing for the TLs and optimal trajectories for Automatically Driven Vehicles (ADVs), while modelling also Manually Driven Vehicles (MDVs) dynamics, leading to significant reduction of queue length and waiting times. In these terms the controller is adaptive, allowing it to operate in mixed scenarios. In addition, several innovative constraints that have been introduced within the MPC formulation allow recursive feasibility to be ensured in constraint-activating events, for instance, when a vehicle approaching the TL during red signal could bring the problem towards infeasibility because some constraints cannot be violated. About Industry 4.0, the most important challenge this thesis tackles is the optimization of task scheduling and controlling in the spaceport within the dynamically changing space industry, which previously limited to governmental entities is now expanding to include private companies. This research was carried out within the framework of the H2020 SESAME project--partnership led by ArianeGroup--that aims to enhance the schedule of assembly operations of space vehicles to maximize the launch throughput at the Guiana space center in Kourou. In the literature they are referred to as Assembly Line Balancing Problems (ALBP) and the key contribution of this work is the development of a scalable MPC algorithm, integrated with a Mixed-Integer Linear Program (MILP) model, to optimize campaign planning in real-time leverages both static and dynamic data, addressing scalability, flexibility and the ability to manage complex constraints, and real-time disturbances. Simulation results confirm the merit of proposed efficient task scheduling algorithm which retains the characteristics of standard MPC and outperforms state-of-the-art optimal scheduling heuristics maintaining similar speed which makes it suitable for real-time implementation.