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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615000
• 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.

该研究计划的重点是动态机电系统的分布式参数识别、控制和优化，采用新型的协作和自适应方法来完成一类任务，包括：（i）通过车载传感器网络在线识别机电系统的参数，例如自主汽车或移动的机器人平台;（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）极值搜索。对于这些任务，该计划将开发广泛适用的（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MAS上运行算法的低级实用控制器。所有结果都将通过在汽车、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的数学分析和实时测试与验证，确保稳定性、性能和鲁棒性。 使用开发的在线合作识别算法的传感器/估计器网络将提高估计的可靠性和准确性相比，使用一个单一的传感器/估计器单元，在先进的控制不确定的机电系统，如汽车系统与不同的道路和车辆条件，和环境和生物医学目标跟踪系统与未知的信号传播系数。 开发的合作和自适应MAS控制方案用于合作驾驶和应急机动的（自主）汽车排;使用无人驾驶飞行器（UAV）、无人驾驶地面车辆（UGV）、自主水下车辆（AUV）、航天器或机器人的团队进行合作监视;协调移动的无线传感器网络;分离的空间探索单元的编队获取和调节，例如分离的望远镜、天线和卫星。 极值寻求研究将集中在提高性能和可靠性;（电磁，声学，辐射，热）信号强度为基础的目标跟踪传感器MAS的环境和生物医学监测;和估计器/控制器参数优化通过成本最小化搜索测量数据的汽车，机器人和工业自动化应用。 该计划的成果将有利于几个战略加拿大和全球应用领域，包括高性能和可靠的信息/通信技术（ICT）传感器系统设计，机电一体化的最佳协调，（特别是汽车、工业自动化和航空航天）网络，基于位置的网络信息系统，智能网络监控，联网检测人类干预和气候变化对生态系统的影响，以及监测空气和水的废物和污染。