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.

拟议的研究计划着重于分布式参数识别，控制和优化动态机电系统的系统，使用新型的合作和自适应方法为一类任务，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自动驾驶汽车或移动机器人平台； （ii）机甲多机构（车辆/机器人/工作单元）系统（质量）的协调和控制； （iii）超级寻求。对于此类任务，该程序将开发出（a）有效分布式高级算法，（b）传感器和执行器网络体系结构以及（c）低级实用控制器，用于在各种机器化质量上运行算法。通过建设性的数学分析以及对汽车，多车辆，可重构机器人和传感器网络测试平台的建设性数学分析以及实时测试和验证，将通过确保稳定性，性能和鲁棒性产生所有结果。 与使用单个传感器/估计器单元相比，在传感器/估计器网络上开发的在线合作识别算法的使用将提高估计性和准确性，并在不确定的机电系统（例如具有不同的道路和车辆条件，环境和生物学目标跟踪系统的自动机械系统）的高级控制中，以及不知名的信号propagients nake nocal and automotive Systems。 发达的合作和自适应MAS控制方案已用于合作驾驶和（自治）汽车的紧急操纵；使用无人驾驶汽车（UAV），无人接地车（UGV），自动水下汽车（AUVS），航天器或机器人的合作监视；移动无线传感器网络的协调；分级空间探索单元的形成和调节，例如分离的望远镜，天线和卫星。 超级寻求研究将集中于提高性能和可靠性； （电磁，声学，辐射，热量）基于信号强度的目标跟踪环境和生物医学监测；通过成本最小化搜索对汽车，机器人和工业自动化应用程序的测量数据进行优化。 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监测空气和水。