IPAB Seminar-10/02/2022 Title: Control Synthesis based on Temporal Logic Trees with Application to Shared Autonomy Abstract: Traditional control methods, such as model predictive control, are able to efficiently incorporate state and input constraints into the control synthesis problem. In this talk, we will discuss how more complex specifications, e.g., linear temporal logic (LTL), can be included. We introduce the new notion of temporal logic trees (TLT) and show how they can be derived from any LTL formula using classical reachability analysis. The presented TLT-based framework allows the treatment of uncertain systems in continuous spaces and time-varying specifications, different from many other approaches. We give an online control synthesis algorithm, under which a set of feasible control inputs can be generated at each time step, and show that this algorithm is recursively feasible. The proposed method is demonstrated in applications of automated vehicles and shared-autonomy systems. This talk is mainly based on join work with Frank J. Jiang, Mirco Giacobbe, Alessandro Abate, Lihua Xie, and Karl H. Johansson. Bio: Yulong Gao is a postdoctoral researcher at KTH Royal Institute of Technology, Sweden. He received the B.E. degree in Automation in 2013, the M.E. degree in Control Science and Engineering in 2016, both from Beijing Institute of Technology, and the joint Ph.D. degree in Electrical Engineering in 2021 from KTH and Nanyang Technological University, Singapore. He was a visiting student in Department of Computer Science, University of Oxford in 2019. His research interests include automatic verification, stochastic control and model predictive control with application to safety-critical systems. Feb 10 2022 13.00 - 14.00 IPAB Seminar-10/02/2022 Dr Yulong Gao (KTH Royal Institute of Technology) Online: Zoom
IPAB Seminar-10/02/2022 Title: Control Synthesis based on Temporal Logic Trees with Application to Shared Autonomy Abstract: Traditional control methods, such as model predictive control, are able to efficiently incorporate state and input constraints into the control synthesis problem. In this talk, we will discuss how more complex specifications, e.g., linear temporal logic (LTL), can be included. We introduce the new notion of temporal logic trees (TLT) and show how they can be derived from any LTL formula using classical reachability analysis. The presented TLT-based framework allows the treatment of uncertain systems in continuous spaces and time-varying specifications, different from many other approaches. We give an online control synthesis algorithm, under which a set of feasible control inputs can be generated at each time step, and show that this algorithm is recursively feasible. The proposed method is demonstrated in applications of automated vehicles and shared-autonomy systems. This talk is mainly based on join work with Frank J. Jiang, Mirco Giacobbe, Alessandro Abate, Lihua Xie, and Karl H. Johansson. Bio: Yulong Gao is a postdoctoral researcher at KTH Royal Institute of Technology, Sweden. He received the B.E. degree in Automation in 2013, the M.E. degree in Control Science and Engineering in 2016, both from Beijing Institute of Technology, and the joint Ph.D. degree in Electrical Engineering in 2021 from KTH and Nanyang Technological University, Singapore. He was a visiting student in Department of Computer Science, University of Oxford in 2019. His research interests include automatic verification, stochastic control and model predictive control with application to safety-critical systems. Feb 10 2022 13.00 - 14.00 IPAB Seminar-10/02/2022 Dr Yulong Gao (KTH Royal Institute of Technology) Online: Zoom
