Output-Feedback-Based Adaptive Leaderless Consensus for Heterogenous Nonlinear Multiagent Systems With Switching Topologies.

This article investigates the leaderless output consensus control problem for a class of nonlinear multiagent systems with heterogenous system orders and unmatched unknown parameters via output-feedback control. The interaction topology among the agents is undirected and jointly connected. Due to the heterogenous system orders and switching topology among the agents, the classical distributed adaptive backstepping-based control technique cannot be applied to solve the problem considered in this article.

Decentralized Adaptive Secure Control of Uncertain Nonlinear Time-Varying Interconnected Systems Against Sensor and Actuator Attacks.

In this article, the decentralized adaptive secure control problem for cyber-physical systems (CPSs) against deception attacks is investigated. The CPSs are formed as a type of nonlinear interconnected strict-feedback systems with uncertain time-varying parameters. The attack affects the information transmission between sensor and actuator in a multiplicative manner.

Adaptive Anti-Disturbance Control for a Class of Uncertain Nonlinear Systems With Composite Disturbances.

High-precision and safety control in face of disturbances and uncertainties is a challenging issue of both theoretical and practical importance. In this article, new adaptive anti-disturbance control schemes are proposed for a class of uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated.

Asymptotic Tracking Control With Bounded Performance Index for MIMO Systems: A Neuroadaptive Fault-Tolerant Proportional-Integral Solution.

It is technically challenging to maintain stable tracking for multiple-input-multiple-output (MIMO) nonlinear systems with modeling uncertainties and actuation faults. The underlying problem becomes even more difficult if zero tracking error with guaranteed performance is pursued. In this work, by integrating filtered variables into the design process, we develop a neuroadaptive proportional-integral (PI) control with the following salient features: 1) the resultant control scheme is of the simple PI structure with analytical algorithms for auto-tuning its PI gains; 2) under a less conservative controllability condition, the proposed control is able to achieve asymptotic tracking with adjustable rate of convergence and bounded performance index collectively; 3) with simple modification, the strategy is applicable to square or nonsquare affine and nonaffine MIMO systems in the presence of unknown and time-varying control gain matrix; and 4) the proposed control is robust against nonvanishing uncertainties/disturbances, adaptive to unknown parameters and tolerant to actuation faults, with only one online updating parameter.

Data-Driven Practical Cooperative Output Regulation Under Actuator Faults and DoS Attacks.

This article addresses the resilient practical cooperative output regulation problem (RPCORP) for multiagent systems subjected to both denial-of-service (DoS) attacks and actuator faults. Fundamentally different from the existing solutions to RPCORPs, the system parameters considered in this article are unknown to each agent, and a novel data-driven control approach is introduced to handle such an issue. The solution starts with developing resilient distributed observers for each follower in the presence of DoS attacks.

Adaptive Event-Triggered Fast Finite-Time Stabilization of High-Order Uncertain Nonlinear Systems and its Application in Maglev Systems.

This article is concerned with the global fast finite-time adaptive stabilization for a class of high-order uncertain nonlinear systems in the presence of serious nonlinearities and constraint communications. By renovating the technique of continuous feedback domination to the construction of a serial of integral functions with nested sign functions, this article first proposes a new event-triggered strategy consisting of a sharp triggered rule and a time-varying threshold. The strategy guarantees the existence of the solutions of the closed-loop systems and the fast finite-time convergence of original system states while reaching a compromise between the magnitude of the control and the trigger interval.

Optimal Control of Temporal Networks With Variable Input and Node-Source Connection.

Many networked systems built upon real-life physical or social interactions have time-varying connections among individual units, where the temporal changes in connectivity and/or interaction strength lead to complicated dynamics. The temporal network model was proposed in the form of controlled linear dynamical systems acting in an ordered sequence of time intervals. One of the core challenges in network science is the control of networks and the optimization of the control strategy.

Event-Triggered Practical Prescribed Time Output Feedback Neuroadaptive Tracking Control Under Saturated Actuation.

This work focuses on the issue of event-triggered practical prescribed time tracking control for a type of uncertain nonlinear systems subject to actuator saturation and unmeasurable states as well as time-varying unknown control coefficients. First, a state observer with simple structure is constructed by means of neural network technology to estimate the unmeasurable system states under time-varying control coefficients. Then, with the help of one-to-one nonlinear mapping of the tracking error, an event-triggered output feedback control scheme is developed to steer the tracking error into a residual set of predefined accuracy within a preassigned settling time.

Adaptive Resilient Secondary Control for Microgrids With Communication Faults.

In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions.

A Distributed Lyapunov-Based Redesign Approach for Heterogeneous Uncertain Agents With Cooperation-Competition Interactions.

A swarming behavior problem is investigated in this article for heterogeneous uncertain agents with cooperation-competition interactions. In such a problem, the agents are described by second-order continuous systems with different intrinsic nonlinear terms, which satisfies the "linearity-in-parameters" condition, and the agents' models are coupled together through a distributed protocol containing the information of competitive neighbors. Then, for four different types of cooperation-competition networks, a distributed Lyapunov-based redesign approach is proposed for the heterogeneous uncertain agents, where the distributed controller and the estimation laws of unknown parameters are obtained.

Training Deep Neural Network for Optimal Power Allocation in Islanded Microgrid Systems: A Distributed Learning-Based Approach.

Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation.

Adaptive Bearing-Only Formation Tracking Control for Nonholonomic Multiagent Systems.

In this article, we consider the formation tracking problem of nonholonomic multiagent systems only using relative bearing measurements between the agents. Such a practical and important yet challenging issue has been taken into limited consideration by existing approaches, which usually requires additional measurements such as relative positions. The contributions of this article are two-fold.

A Hierarchical Security Control Framework of Nonlinear CPSs Against DoS Attacks With Application to Power Sharing of AC Microgrids.

In this article, we investigate the distributed resilient observers-based decentralized adaptive control problem for cyber-physical systems (CPSs) with time-varying reference trajectory under denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear multi-input uncertain multiagent systems, which can be used to model an AC microgrid system consisting of distributed generators. When the communication to a subsystem from one of its neighbors is attacked by a DoS attack, the transmitted information is unavailable and the existing distributed adaptive methods used to estimate the bound of the n th-order derivative of the reference trajectory become nonapplicable.

Adaptive Consensus Control for Nonlinear Multiagent Systems With Unknown Control Directions Using Event-Triggered Communication.

In this article, under directed graphs, an adaptive consensus tracking control scheme is proposed for a class of nonlinear multiagent systems with completely unknown control coefficients. Unlike the existing results, here, each agent is allowed to have multiple unknown nonidentical control directions, and continuous communication between neighboring agents is not needed. For each agent, we design a group of novel Nussbaum functions and construct a monotonously increasing sequence in which the effects of our Nussbaum functions reinforce rather than counteract each other.

Bearing-Based Adaptive Neural Formation Scaling Control for Autonomous Surface Vehicles With Uncertainties and Input Saturation.

When a group of autonomous surface vehicles (ASVs) sail from a wide waterway to a narrow waterway, one difficulty is to keep relative formation with collision avoidance. Scaling the formation sizes with formation shapes invariant is a promising way. This article investigates such a formation scaling control problem of ASVs with uncertainties and input saturation.

Target Controllability in Multilayer Networks via Minimum-Cost Maximum-Flow Method.

In this article, to maximize the dimension of controllable subspace, we consider target controllability problem with maximum covered nodes set in multiplex networks. We call such an issue as maximum-cost target controllability problem. Likewise, minimum-cost target controllability problem is also introduced which is to find minimum covered node set and driver node set.

Resilient Cooperative Control for Networked Lagrangian Systems Against DoS Attacks.

In this article, we study the distributed resilient cooperative control problem for directed networked Lagrangian systems under denial-of-service (DoS) attacks. The DoS attacks will block the communication channels between the agents. Compared with the existing methods for the linear networked systems, the considered nonlinear networked Lagrangian systems with asymmetric channels under DoS attacks are more challenging and still not well explored.

Adaptive Formation Control of Networked Robotic Systems With Bearing-Only Measurements.

In this article, we address the bearing-only formation control problem of 3-D networked robotic systems with parametric uncertainties. The contributions of this article are two-fold: 1) the bearing-rigid theory is extended to solve the nonlinear robotic systems with the Euler-Lagrange-like model and 2) a novel almost global stable distributed bearing-only formation control law is proposed for the nonlinear robotic systems. Specifically, the robotic systems subject to nonholonomic constraints and dynamics are first transformed into a Euler-Lagrange-like model.

MAS-Based Distributed Resilient Control for a Class of Cyber-Physical Systems With Communication Delays Under DoS Attacks.

In this article, we investigate the distributed resilient control problem for a class of cyber-physical systems with communication delays under denial-of-service (DoS) attacks. In contrast to the previous DoS attacks results based on multiagent systems (MASs), a new distributed resilient control approach is proposed for more general heterogeneous linear MASs with nonuniform communication delays. Two types of sampled-based observers are, respectively, proposed.

False data injection against state estimation in power systems with multiple cooperative attackers.

Given the strong cyber-physical interactions in today's smart grid, false data injection (FDI) attack can readily mislead the state estimation and influence the system operation by manipulating meter measurements. In this paper, a new FDI attack strategy is considered where multiple attackers cooperatively launch an unobservable attack. Firstly, the entire transmission system is partitioned into several subsystems, with each attacker only acquiring and manipulating the measurements in its local area.

Linear Quadratic Optimal Control of Time-Invariant Linear Networks With Selectable Input Matrix.

Optimal control of networks is to minimize the cost function of a network in a dynamical process with an optimal control strategy. For the time-invariant linear systems, · x(t)=A x(t)+B u(t) , and the traditional linear quadratic regulator (LQR), which minimizes a quadratic cost function, has been well established given both the adjacency matrix A and the control input matrix B . However, this conventional approach is not applicable when we have the freedom to design B .

Towards a polynomial algorithm for optimal contraction sequence of tensor networks from trees.

The computational cost of contracting a tensor network depends on the sequence of contractions, but to decide the sequence of contractions with a minimal computational cost on an arbitrary network has been proved to be an NP-complete problem. In this work, we conjecture that the problem may be a polynomial one if we consider the computational complexity instead. We propose a polynomial algorithm for the optimal contraction complexity of tensor tree network, which is a specific and widely applied network structure.

A Unified Event-Triggered Control Approach for Uncertain Pure-Feedback Systems With or Without State Constraints.

Existing schemes for systems with state constraints require the bounds of the constraints for controller design and may result in conservativeness or even become invalid when they are applied to systems without such constraints. In this paper, we study the problem of event-triggered control for a class of uncertain nonlinear systems by considering the cases with or without state constraints in a unified manner. By introducing a new universal-constrained function and using certain transformation techniques, the original-constrained system is converted into an equivalent totally unconstrained one.

Key Nodes Selection in Controlling Complex Networks via Convex Optimization.

Key nodes are the nodes connected with a given number of external source controllers that result in minimal control cost. Finding such a subset of nodes is a challenging task since it impossible to list and evaluate all possible solutions unless the network is small. In this paper, we approximately solve this problem by proposing three algorithms step by step.

Distributed Secure State Estimation and Control for CPSs Under Sensor Attacks.

In this paper, we investigate the distributed secure state estimation and control problems for interconnected cyber-physical systems (CPSs) with some sensors being attacked. First, by exploring the distinct properties of the unidentifiable attacks to a CPS, an explicit sufficient condition that the secure state estimation problem can be solvable is established. Then distributed preselectors and observers are presented to solve the secure state estimation problems.