Kok Lay Teo教授学术报告：A Composite Time Scaling Transform for Switching Time Optimal Control Problems
发布时间：2022-12-12 浏览次数：34
报告题目：A Composite Time Scaling Transform for Switching Time Optimal Control Problems
报告人：Kok Lay Teo（马来西亚双威大学）
报告时间：2022年12月12日下午14:00--18:00
报告地点：雁山校区理科组团724会议室，腾讯会议号：655 939 761
报告人简介：Kok Lay Teo教授，国际系统与控制科学院院士、亚太人工智能学会会士，马来西亚双威大学数学科学学院教授、副院长，澳大利亚科廷大学（Curtin University）荣誉退休教授。博士毕业于加拿大渥太华大学，1998年至2005年任香港理工大学应用数学系的首席教授和系主任，2005年至2010年任科廷大学数学与统计系的首席教授和系主任，2011年至2019年为澳大利亚科廷大学（Curtin University）数学与统计系杰出教授（John Curtin Distinguished Professor）。研究兴趣主要包括最优控制、优化理论与应用、通讯信号处理、金融优化决策理论等方面研究，领导开发了用于求解非线性最优控制问题的专门软件包MISER3.3等。出版英文专著6本，发表高水平科研论文近500篇，应邀在著名国际学术会议上做主题发言、大会报告和邀请报告30余次，作为大会主席组织多个专题国际学术大会。担任国际SCI杂志《Journal of Industrial and Management Optimization》（JIMO）主编，《Automatica》、《Journal of Global Optimization》、《Journal of Optimization Theory and Applications》（JOTA）等十余个杂志的编委。
报告摘要：The control parameterization technique used in conjunction with the time scaling transform is an effective computational method for solving various optimal control problems. More specifically, the control parameterization method approximates the control function as a piecewise constant function with its heights and switching times as decision variables. The time scaling transform maps variable time points into fixed time points in a new time horizon. Thus, the optimal control problem is approximated as an optimal parameter selection problem, which is a finite dimensional optimization problem, and hence can be solved by gradient-based optimization methods. However, the conventional time-scaling transformation requires that the switching times for all the control components switch simultaneously, which can be undesirable in practice. In this paper, we introduce a novel technique where the switching times for each of the control components can switch independently. This technique is referred to as the composite time scaling transform. To demonstrate the effectiveness and flexibility of the proposed approach, four example problems are solved using the method proposed. Numerical results show that the new method achieves much better objective function values with some increase in computation time compared to the conventional time-scaling transformation technique.