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Xiaofeng Wang is an assistant professor in the Department of Electrical Engineering at the University of South Carolina (UofSC), Columbia. He earned his B.S. degree in Applied Mathematics and M.S. in Operation Research and Control Theory from East China Normal University, China, in 2000 and 2003, respectively, and obtained his PhD degree in Electrical Engineering at the University of Notre Dame in 2009. After that, he worked as postdoctoral research associate at the University of Illinois at Urbana and Champaign before he joined UofSC. His research interests include robotics, cyber-physical systems, autonomous systems, multi-agent systems, networked and real-time control systems, robust fault-tolerant control, and optimization. He is associate editor of Journal of The Franklin Institute since 2015. He was the recipient of the best paper award in the Annual Conference of the Prognostics and Health Management Society in 2014 and the finalist of the best paper award in the International Conference on Cyber- Physical Systems in 2013. |
Lebesgue-Approximation Model Predictive Control of Nonlinear Sampled-Data Systems
Abstract:
In computer-controlled systems model predictive control (MPC) algorithms are often implemented in a completely discrete manner, even though the plant is continuous-time. Such discretization refers to not only the sampled-data nature but also the finite-horizon optimal control problem (FHOCP) itself that has to be solved at each triggered time instant. Inappropriate discretization may place a heavy computational burden on the processor and possibly lead to violation of system constraints, instability, and even infeasibility of MPC. This talk will discuss discrete-time implementation of MPC for continuous-time nonlinear systems.
In particular, we focus on aperiodic MPC implementation, given the observation that aperiodic approaches in sampled-data systems may have the potential to reduce computation costs. The concept of Lebesgue approximation model (LAM), inspired by Lebesgue sampling, will be introduced into the MPC framework, which leads to the LAMPC (LAM-based MPC) algorithm. We will discuss the design principles in formulating the LAM-based FHOCP in MPC and the scheduling schemes that triggers the computation of the FHOCP, as well as feasibility and stability of LAMPC.