Learning‐based robust control methodologies under information constraints

Abstract

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. Recently, fruitful machine learning-based approaches, for example, reinforcement learning, deep learning, brain-inspired learning, have been incrementally promoted to innovate traditional learning-based intelligent control methodologies in both theoretical and practical sides. Especially, promising applications have also been developed to autonomous systems and robotics. In addition to booming advances in learning-based control philosophy, within a complex system, unexpected constraints would be inevitably involved, for instance, communication delays, sensor failures/noises, actuator nonlinearities, nonholonomic/underactuated dynamics, and so forth, especially within distributed systems. With a stringent peer review process, there are 34 papers finally included in this Special Issue, which are covering the following aspects: (1) Learning-based control methodologies over network; (2) Estimation and fault detection under information constraints; and (3) Case studies. A brief summary of the accepted papers is discussed in the following. The authors in Reference 1 studied the parameter learning problem for stochastic Boolean networks. Then, a numerical experiment is presented to show the usefulness of the designed parameter learning algorithm. In Reference 2, the authors investigated a distributed online learning problem with privacy preservation, in which the learning nodes in a distributed network aims to minimize the sum of local loss functions over finite-time horizon. The authors in Reference 3 studied the sliding mode control of Markovian jump systems under communication constraints subject to unavailable states and deception attack and a dynamic event-triggered scheme. In Reference 4, the authors designed a sliding mode control for the discrete-time interval type-2 fuzzy singularly perturbed systems under a component-based dynamic event-triggering scheme. The authors in Reference 5 developed an observer-based proportional integral derivative security control for a networked system subject to aperiodic denial-of-service attacks. In Reference 6, the authors investigated the finite-time tracking control problem for nonstrict feedback nonlinear systems subject to full state constraints. Then, an example of the electromechanical system is given to verify the applicability of the purposed method. In Reference 7, the problem of stability analysis is investigated for a class of switched nonlinear systems whose control inputs include time delay and sampling. The authors in Reference 8 studied the finite-iteration tracking control problem for discrete-time linear systems in repetitive process setting with external disturbances based on the robust iterative learning control scheme. In Reference 9, the authors designed a set of dual-mode feedback controllers for a class of Markovian jump Lur'e