Cooperative and Adaptive Mechatronic Systems: Identification, Control, and Optimization

协作和自适应机电系统：识别、控制和优化

• 批准号: RGPIN-2016-04340
• 负责人: Fidan, Baris
• 金额: $ 2.11万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709167
• 关键词: Cooperative; Adaptive; Mechatronic; Systems; Identification

The proposed research program focuses on distributed parameter identification, control and optimization of dynamic mechatronic systems, using novel cooperative and adaptive approaches for a class of tasks, including (i) on-line parameter identification of mechatronic systems, e.g. autonomous cars or mobile robotic platforms, via onboard sensor networks; (ii) coordination and control of mechatronic multi-agent (vehicle/robot/work unit) systems (MASs); (iii) extremum seeking. For such tasks, the program will develop widely applicable sets of (a) effective distributed high level algorithms, (b) sensor and actuator network architectures, and (c) low level practical controllers for running the algorithms on various mechatronic MASs. All outcomes will be produced with guaranteed stability, performance, and robustness, via constructive mathematical analysis and real-time testing and verification on automotive, multi-vehicle, reconfigurable robot, and sensor network test platforms. Use of the developed on-line cooperative identification algorithms over a sensor/estimator network will enhance estimation reliability and accuracy compared to the use of a single sensor/estimator unit, in advanced control of uncertain mechatronic systems, such as automotive systems with varying road and vehicle conditions, and environmental and biomedical target tracking systems with unknown signal propagation coefficients. The developed cooperative and adaptive MAS control schemes have use in cooperative driving and emergency maneuvering of (autonomous) car platoons; cooperative surveillance using teams of unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), autonomous underwater vehicles (AUVs), spacecraft or robots; coordination of mobile wireless sensor networks; formation acquisition and regulation of fractionated space exploration units, e.g. fractionated telescopes, antennas, and satellites. The extremum seeking studies will focus on enhancing performance and reliability; (electromagnetic, acoustic, radiation, heat) signal intensity based target tracking by sensory MASs for environmental and biomedical monitoring; and estimator/controller parameter optimization via cost minimization search over measured data for automotive, robotic, and industrial automation applications. The program outcomes will be beneficial for several strategic Canadian and global application fields, including high-performance and reliable information/communication technology (ICT) sensor system design, optimal coordination of mechatronic (particularly automotive, industrial automation, and aerospace) networks, location-based networked information systems, intelligent networked surveillance, networked detection of human intervention and climate change effects to the ecosystem, and waste and contamination monitoring of air and water.

拟议的研究计划侧重于动态机电系统的分布式参数识别、控制和优化，使用新颖的协作和自适应方法来完成一类任务，包括（i）机电系统的在线参数识别，例如通过车载传感器网络的自动驾驶汽车或移动机器人平台； (ii) 机电多智能体（车辆/机器人/工作单元）系统（MAS）的协调和控制； (iii) 求极值。对于此类任务，该计划将开发广泛适用的一组（a）有效的分布式高级算法，（b）传感器和执行器网络架构，以及（c）用于在各种机电一体化 MAS 上运行算法的低级实用控制器。通过在汽车、多车辆、可重构机器人和传感器网络测试平台上进行建设性数学分析以及实时测试和验证，所有结果都将在保证稳定性、性能和鲁棒性的情况下产生。 与使用单个传感器/估计器单元相比，在传感器/估计器网络上使用开发的在线协作识别算法将提高估计的可靠性和准确性，在不确定机电系统的高级控制中，例如具有不同道路和条件的汽车系统。车辆状况以及具有未知信号传播系数的环境和生物医学目标跟踪系统。 开发的协作和自适应MAS控制方案可用于（自主）汽车排的协作驾驶和紧急操纵；使用无人机（UAV）、无人地面车辆（UGV）、自主水下航行器（AUV）、航天器或机器人团队进行合作监视；移动无线传感器网络的协调；分段空间探索单元的编队获取和调节，例如分段望远镜、天线和卫星。 极值寻求研究将侧重于提高性能和可靠性；通过传感 MAS 进行基于（电磁、声、辐射、热）信号强度的目标跟踪，用于环境和生物医学监测；通过对汽车、机器人和工业自动化应用的测量数据进行成本最小化搜索来优化估计器/控制器参数。 该计划的成果将有利于加拿大和全球的几个战略应用领域，包括高性能和可靠的信息/通信技术（ICT）传感器系统设计、机电一体化（特别是汽车、工业自动化和航空航天）网络的最佳协调、定位-基于网络信息系统、智能网络监控、人类干预和气候变化对生态系统影响的网络检测，以及空气和水的废物和污染监测。