A Novel Dual-Phase Based Approach for Distributed Event-Triggered Control of Multiagent Systems With Guaranteed Performance.

This article presents a novel dual-phase based approach for distributed event-triggered control of uncertain Euler-Lagrange (EL) multiagent systems (MASs) with guaranteed performance under a directed topology. First, a fully distributed robust filter is designed to estimate the reference signal for each agent with guaranteed observation performance under continuous state feedback, which transforms the distributed event-triggered control problem into a centralized one for multiple single systems. Second, an event-triggered controller is constructed via intermittent state feedback, making the output of each agent follow the corresponding estimated signal with guaranteed tracking performance.

Event-Triggered Learning-Based Fault Accommodation for a Class of Nonlinear Interconnected Systems.

In this article, a distributed learning-based fault accommodation scheme is proposed for a class of nonlinear interconnected systems under event-triggered communication of control and measurement signals. Process faults occurring in the local dynamics and/or propagated from interconnected neighboring subsystems are considered. An event-triggered nominal control law is used for each subsystem before detecting any fault occurrence in its dynamics.

Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems.

This paper addresses decentralized tracking control (DTC) problems for input constrained unknown nonlinear interconnected systems via event-triggered adaptive dynamic programming. To reconstruct the system dynamics, a neural-network-based local observer is established by using local input-output data and the desired trajectories of all other subsystems. By employing a nonquadratic value function, the DTC problem of the input constrained nonlinear interconnected system is transformed into an optimal control problem.

Optimal intervention strategies to mitigate the COVID-19 pandemic effects.

Governments across the world are currently facing the task of selecting suitable intervention strategies to cope with the effects of the COVID-19 pandemic. This is a highly challenging task, since harsh measures may result in economic collapse while a relaxed strategy might lead to a high death toll. Motivated by this, we consider the problem of forming intervention strategies to mitigate the impact of the COVID-19 pandemic that optimize the trade-off between the number of deceases and the socio-economic costs.

A Data-Driven ILC Framework for a Class of Nonlinear Discrete-Time Systems.

In this article, we propose a data-driven iterative learning control (ILC) framework for unknown nonlinear nonaffine repetitive discrete-time single-input-single-output systems by applying the dynamic linearization (DL) technique. The ILC law is constructed based on the equivalent DL expression of an unknown ideal learning controller in the iteration and time domains. The learning control gain vector is adaptively updated by using a Newton-type optimization method.

Online Learning With Adaptive Rebalancing in Nonstationary Environments.

An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area.

Data-Driven Iterative Learning Control for Nonlinear Discrete-Time MIMO Systems.

This article considers the tracking control of unknown nonlinear nonaffine repetitive discrete-time multi-input multi-output systems. Two data-driven iterative learning control (ILC) schemes are designed based on two equivalent dynamic linearization data models of an unknown ideal learning controller, which exists theoretically in the iteration domain. The two control schemes provide ways of selecting learning controllers based on the complexity of the controlled nonlinear systems.

Optimal Elevator Group Control via Deep Asynchronous Actor-Critic Learning.

In this article, a new deep reinforcement learning (RL) method, called asynchronous advantage actor-critic (A3C) method, is developed to solve the optimal control problem of elevator group control systems (EGCSs). The main contribution of this article is that the optimal control law of EGCSs is designed via a new deep RL method, such that the elevator system sends passengers to the desired destination floors as soon as possible. Deep convolutional and recurrent neural networks, which can update themselves during applications, are designed to dispatch elevators.

Distributed Fault-Tolerant Control of Multiagent Systems: An Adaptive Learning Approach.

This paper focuses on developing a distributed leader-following fault-tolerant tracking control scheme for a class of high-order nonlinear uncertain multiagent systems. Neural network-based adaptive learning algorithms are developed to learn unknown fault functions, guaranteeing the system stability and cooperative tracking even in the presence of multiple simultaneous process and actuator faults in the distributed agents. The time-varying leader's command is only communicated to a small portion of follower agents through directed links, and each follower agent exchanges local measurement information only with its neighbors through a bidirectional but asymmetric topology.

Fault Estimation and Accommodation of Interconnected Systems: A Separation Principle.

This paper addresses the fault estimation (FE) and accommodation issues of interconnected systems by using two new concepts namely interconnected separation principle and constrained interconnected separation principle that allow for the separate design not only between diagnostic observer and fault tolerant controller for each subsystem, but also between observer/controller of each subsystem and those of other ones. Sufficient fault recoverability conditions are established, under which both distributed and decentralized FE and accommodation schemes are provided. The new results help to provide a framework for observer-based fault diagnosis and fault tolerant control of interconnected systems, and are further applied to the meta aircraft configuration that consists of multiple aircraft joined together to illustrate their efficiency.

Semantically Enhanced Online Configuration of Feedback Control Schemes.

Recent progress toward the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability, and scalability of control system components.

Integrated modelling of medical emergency response process for improved coordination and decision support.

The medical emergency response comprises a domain with complex processes, encompassing multiple heterogeneous entities, from organisations involved in the response to human actors to key information sources. Due to the heterogeneity of the entities and the complexity of the domain, it is important to fully understand the individual processes in which the components are involved and their inter-operations, before attempting to design any technological tool for coordination and decision support. This work starts with the gluing together and visualisation of the interactions of involved entities into a conceptual model, along the identified five workspaces of emergency response.

An Integrated Learning and Filtering Approach for Fault Diagnosis of a Class of Nonlinear Dynamical Systems.

This paper develops an integrated filtering and adaptive approximation-based approach for fault diagnosis of process and sensor faults in a class of continuous-time nonlinear systems with modeling uncertainties and measurement noise. The proposed approach integrates learning with filtering techniques to derive tight detection thresholds, which is accomplished in two ways: 1) by learning the modeling uncertainty through adaptive approximation methods and 2) by using filtering for dampening measurement noise. Upon the detection of a fault, two estimation models, one for process and the other for sensor faults, are initiated in order to identify the type of fault.

Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines.

Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the majority of the data belong to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, SVM classification is a computationally demanding task and existing hardware architectures for SVMs only consider monolithic classifiers. This paper proposes the acceleration of cascade SVMs through a hybrid processing hardware architecture optimized for the cascade SVM classification flow, accompanied by a method to reduce the required hardware resources for its implementation, and a method to improve the classification speed utilizing cascade information to further discard data samples.

Adaptive approximation for multiple sensor fault detection and isolation of nonlinear uncertain systems.

This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local sensor fault detection and isolation (SFDI) modules are designed using a dedicated nonlinear observer scheme.

Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams.

This paper considers a heterogeneous team of cooperating unmanned aerial vehicles (UAVs) drawn from several distinct classes and engaged in a search and action mission over a spatially extended battlefield with targets of several types. During the mission, the UAVs seek to confirm and verifiably destroy suspected targets and discover, confirm, and verifiably destroy unknown targets. The locations of some (or all) targets are unknown a priori, requiring them to be located using cooperative search.