Towards A Practical Model Predictive Control Framework for Networked and Distributed Dynamic Systems

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Networked and distributed dynamic systems play essential roles in many emerging industrial applications such as cyber-physical systems, smart grid, intelligent transportation systems, distributed process, sensor networks, and multi-agent systems. The insertion of communication networks into a control system can bring many distinct advantages; but it also presents tremendous challenges to control system designers mainly because of the effects and constraints induced by communication networks. Model predictive control (MPC) is the leading paradigm for high-performance and cost-effective control of complex systems in industrial applications. Its inherent prediction ability makes it a good candidate for the control of networked and distributed dynamic systems. Yet, the classical and robust MPC formulations cannot systematically deal with the random measurement noise, stochastic constraints, parametric uncertainties, and exogenous disturbances that are ubiquitous in networked and distributed dynamical systems. This research program will address the most important challenges facing the control of networked and distributed systems. It will entail fundamental research to overcome the current limitations of MPC. The proposed research program will incorporate practical network-induced constraints, statistic uncertainties and probabilistic constraints, and control performance specifications into the practical MPC framework. It will develop new stochastic and adaptive MPC methods and fast real-time optimization algorithms. Two general types of control problems will be investigated: (1) MPC for networked dynamic systems (with a single plant to be controlled over network), and (2) Distributed MPC for multi-agent systems (with multiple agents connected over a communication network). The MPC analysis and synthesis framework that will be established from the research program proposed in this application will be tested and validated on experimental systems, e.g., (1) the networked control system of a quadrotor, (2) the networked control system of a robot manipulator, and (3) a multi-agent system including multiple quadrotors and mobile robots. The proposed research program will fill the gap between theory and practice; it will provide control engineers with new tools for analysis and synthesis of the stochastic, adaptive and fast MPC for networked and distributed dynamic systems; it will establish a novel unified paradigm addressing practical constraints. Furthermore, the program will greatly benefit graduate students through technology- and industry-relevant research training.