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

面向网络和分布式动态系统的实用模型预测控制框架

• 批准号: RGPIN-2016-05386
• 负责人: Shi, Yang
• 金额: $ 3.35万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760283
• 关键词: Towards; Practical; Model; Predictive; Control

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.

网络化和分布式动态系统在许多新兴工业应用中发挥着重要作用，例如网络物理系统、智能电网、智能交通系统、分布式过程、传感器网络和多智能体系统。将通信网络插入控制系统可以带来许多独特的优势；但它也给控制系统设计者带来了巨大的挑战，主要是由于通信网络引起的影响和约束。模型预测控制（MPC）是工业应用中复杂系统高性能和经济高效控制的领先范例。其固有的预测能力使其成为网络和分布式动态系统控制的良好候选者。然而，经典且稳健的 MPC 公式无法系统地处理网络和分布式动力系统中普遍存在的随机测量噪声、随机约束、参数不确定性和外生干扰。该研究计划将解决网络和分布式系统控制面临的最重要的挑战。需要进行基础研究来克服 MPC 当前的局限性。拟议的研究计划将把实际的网络引起的约束、统计不确定性和概率约束以及控制性能规范纳入实际的 MPC 框架中。它将开发新的随机和自适应 MPC 方法以及快速实时优化算法。将研究两种一般类型的控制问题：（1）用于网络动态系统的 MPC（通过网络控制单个设备），以及（2）用于多代理系统的分布式 MPC（通过通信网络连接多个代理） ）。根据本申请提出的研究计划建立的 MPC 分析和综合框架将在实验系统上进行测试和验证，例如（1）四旋翼飞行器的网络控制系统，（2）机器人的网络控制系统机械手，以及（3）包括多个四旋翼飞行器和移动机器人的多智能体系统。所提出的研究计划将填补理论与实践之间的空白；它将为控制工程师提供新的工具，用于分析和综合网络和分布式动态系统的随机、自适应和快速 MPC；它将建立一个新颖的统一范式来解决实际限制。此外，该计划还将通过技术和行业相关的研究培训使研究生受益匪浅。