This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems. As the real-time measurements of the scheduling parameters are typically available, in the presented formulation, we allow the RCI set description along with the invariance-inducing controller to be scheduling parameter dependent. The considered formulation thus leads to parameter-dependent conditions for the set invariance, which are replaced by sufficient Linear Matrix Inequality (LMI) conditions via Polya's relaxation. These LMI conditions are then combined with a novel volume maximization approach in a Semidefinite Programming (SDP) problem, which aims at computing the desirably large RCI set. In addition to ensuring invariance, it is also possible to guarantee performance within the RCI set by imposing a chosen quadratic performance level as an additional constraint in the SDP problem. The reported numerical example shows that the presented iterative algorithm can generate invariant sets which are larger than the maximal RCI sets computed without exploiting scheduling parameter information.
Trajectory Tracking Control Design for Autonomous Helicopters with Guaranteed Error Bounds
Institute of Flight Systems, German Aerospace Center (DLR)（飞行系统研究所，德国航天中心）；Institute for Production Technology and Systems, Leuphana University of Lueneburg（生产技术与系统研究所，路比哈大学）
Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, PR China（自动化与智能感知学院，上海交通大学，中国）；Department of Mechanical Engineering, University of Victoria, Victoria, Canada（机械工程系，维多利亚大学，加拿大）
Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Coordinated Science Laboratory（协调科学实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
