Learning???based robust control methodologies under information constraints

This editorial is dealing with the collection and report of some recent advances in learning-based robust control methodologies under information constraints. Both theoretical and practical contributions focusing on this theme are partly addressed in this special issue. Particularly, the latest progress of learning-based control in autonomous systems, large-scale systems, interconnected systems, robotics, industrial mechatronics, transportation and variously broad applications are introduced to the literature through this special issue. Within the past decade, various learning-based control technologies have prosperously emerged in both academic and industrial communities, and have expectantly performed remarkable superiority in terms of intelligence, autonomy, conciseness, reliability, resilience, and so forth. At the early stage, neural/fuzzy learning architectures have been widely deployed to online capture complex unknowns including unmodeled dynamics, uncertainties, and disturbances pertaining to the plant which might be a nonlinear system addressing the vehicles, robotics, transportations, mechatronics, informatics, circuits, and so forth.