Optimization-based control for constrained systems with fast dynamics

基于优化的快速动态约束系统控制

• 批准号: 341518-2010
• 负责人: Milman, Ruth
• 金额: $ 1.46万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659827
• 关键词: Optimization; based; control; constrained; systems

The primary objective of this research program is to advance the knowledge and application of optimization- based control for constrained systems with fast dynamics, such as those prevalent in the automotive, robotics and aerospace fields. Although many optimization schemes exist for unconstrained systems, difficulties are encountered when incorporating constraints directly into the problem formulation. Real-time implementation of constrained optimal controllers on fast systems has only recently become a realistic possibility thanks to the advanced computational power of modern computers. My research program aims to develop control algorithms that allow for consistent and practical implementation of constrained optimization schemes on systems with fast dynamics with two key objectives - the advancement of knowledge, and the implementation of control algorithms on relevant industrial systems. The focus of my current research has been the optimization-based model predictive control methodology.** The application of control systems allows for collaborative interdisciplinary research, yet there is often a visible gap between research in control systems and application of that technology in industry. The implementation of advanced controls to real industrial systems will impact industry dramatically. In this case, the advancement of knowledge must be supplemented with practical application of that knowledge to real systems. My research team is currently working on applications of optimization in the areas of vision systems, remotely controlled systems and automotive systems. The long term objective of these applications is to integrate some of these systems in order to implement an autonomous vehicle.** Research in this area of constrained optimization and control would advance the field with the realistic possibility that consequential technologies could be transferred to Canadian industry with positive returns. Furthermore, the importance of this area of research to industry makes it a fundamental area of importance for the training of HQP.****************

该研究计划的主要目标是推进基于优化的控制的知识和应用，用于具有快速动态的约束系统，例如在汽车，机器人和航空航天领域中普遍存在的系统。 虽然存在许多优化方案的无约束系统，遇到的困难时，将约束直接到问题的制定。 由于现代计算机的先进计算能力，在快速系统上实时实现约束最优控制器只是最近才成为现实的可能性。 我的研究计划旨在开发控制算法，允许在具有快速动态的系统上一致和实际地实施约束优化方案，具有两个关键目标-知识的进步和控制算法在相关工业系统上的实施。 我目前的研究重点是基于优化的模型预测控制方法。** 控制系统的应用允许跨学科的合作研究，但控制系统的研究和该技术在工业中的应用之间往往存在明显的差距。 将先进控制应用于真实的工业系统将对工业产生巨大的影响。 在这种情况下，知识的进步必须辅之以知识在真实的系统中的实际应用。 我的研究团队目前正在研究视觉系统、远程控制系统和汽车系统领域的优化应用。 这些应用的长期目标是集成其中一些系统，以实现自动驾驶汽车。 在这一领域的约束优化和控制的研究将推进该领域的现实可能性，相应的技术可以转移到加拿大工业的积极回报。 此外，这一研究领域对行业的重要性使其成为HQP培训的重要基础领域。