Jing Sun
Department of Naval Architecture and Marine Engineering
University of Michigan Ann Arbor, MI, USA
Email: [email protected]
Abstract
Integrated Perturbation Analysis and Sequential Programming (IPA-SQP), which leverages the complementary features of two optimization algorithms to balance computational efficiency and solution accuracy, has been shown to have computational advantages in real-time optimization for model predictive control (MPC) implementation. More recently, IPA-SQP formulation has been used to derive adaptive model predictive control to address control problems for systems with constraints and uncertainties. In this presentation, we discuss the integration of the parameter estimation with the receding horizon model predictive control in the IPA-SQP framework. An adaptive MPC algorithm that performs MPC updates based on parameter estimation will be elaborated. Along with the discussion of algorithm derivation and implementation, examples will be used to illustrate the performance of the IPA-SQP based schemes for control and adaptation.
Biography
Prof. Jing Sun received her Ph. D degree from University of Southern California in 1989, with Prof. Petros Ioannou as her advisor. From 1989-1993, she was an assistant professor in the Electrical and Computer Engineering Department at Wayne State University. She joined Ford Research Laboratory in 1993, where she worked on advanced powertrain system controls. After spending almost 10 years in industry, she came back to academia in 2003 and joined the Naval Architecture and Marine Engineering Department at the University of Michigan where she is a professor now. She holds 37 US patents and has co-authored (with Petros Ioannou) a textbook on Robust Adaptive Control. She is an IEEE Fellow and one of the three recipients of the 2003 IEEE Control System Technology Award.
