On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

Distributed Fault-Tolerant Consensus Tracking Control of Multi-Agent Systems under Fixed and Switching Topologies

This paper proposes a novel distributed fault-tolerant consensus tracking control design for multi-agent systems with abrupt and incipient actuator faults under fixed and switching topologies. The fault and state information of each individual agent is estimated by merging unknown input observer in the decentralized fault estimation hierarchy. Then, two kinds of distributed fault-tolerant consensus tracking control schemes with average dwelling time technique are developed to guarantee the mean-square exponential consensus convergence of multi-agent systems, respectively, on the basis of the relative neighboring output information as well as the estimated information in fault estimation. Simulation results demonstrate the effectiveness of the proposed fault-tolerant consensus tracking control algorithm

Event-triggered Control For Semi-global Stabilisation Of Systems With Actuator Saturation

This paper investigates the problem of event-triggered control for semi-global stabilisation of null controllable systems subject to actuator saturation. First, for a continuous-time system, novel event-triggered low-gain control algorithms based on Riccati equations are proposed to achieve semi-global stabilisation. The algebraic Riccati equation with a low-gain parameter is utilised to design both the event-triggering condition and the linear controller; a minimum inter-event time based on the Riccati ordinary differential equation is set a priori to exclude the Zeno behaviour. In addition, the high-low-gain techniques are utilised to extend the semi-global results to event-based global stabilisation. Furthermore, for a discrete-time system, novel low-gain and high–low-gain control algorithms are proposed to achieve event-triggered stabilisation. Numerical examples are provided to illustrate the theoretical results.postprin

Intelligent Design for Real Time Networked Multi-Agent Systems

Past decade has witnessed an unprecedented growth in reasearch for Unmanned Aerial Vehicles (UAVs) both in military and nonmilitary fronts. They have become ubiquitous in almost every military operations which includes domestic and overseas missions. With rapidly advancing technology, open source nature of the flight controllers, and significantly lesser costs than before, companies around the world are delving into UAV market as one of the upcoming lucrative investments. Companies like Amazon Inc., Dominos Pizza Inc. have had some successful test runs which again solidifies the research opportunities. Delivery services and recreational uses seems to have increased in the past 3-4 years which has let the Federal Aviation Administration to update their rules and regulations. Mapping, Surveying and search/rescue mission are some of the applications of UAVs that are most appealing. Making these applications airborne cuts the time and cost at considerable and affordable levels. Using UAVs for operations has advantages in both response time and need of manpower compared to piloted aricrafts. Obtaining prior information of a person/people in distress can become a deciding factor for a successful mission. It can help in making critical decision as which location or type of helicopter / vehicle to be used for extraction, equipment to bring and how many crew members that are needed. The idea here is to make this system of UAVs automated to coordinate with each other without human intervention (other than high level commands like takeoff and land). Researchers and Military experts have recognized the use of drones for search and rescue missions to be of utmost importance. Year 2016 saw a first of its kind UAV search and rescue symposium held in Nevada. The objective was to give a platform for UAV enthusiasts and researchers and share their experiences and concerns while using UAVs as first responders. The biggest drawback of using an aerial vehicle for inspection/search/rescue mission is its airborne time. The batteries used are big and heavy which increases the weight and decreases the flight time. One can go about solving this issue by using a swarm of UAVs which would inspect/search a given area in less amount of time. This has advantage in both response time and need for lesser man power.The main challenges for Multiple Drone Control (MDC) includes 1) Address the periodic sampling frequency issue of information of assets so as to maintain stability; 2) Optimize the communication channel while providing minimum Quality of Service (QoS); 3) Optimal control strategy which includes non-linearity in state space model; 4) Optimal control in presence of uncertainties; 5) Admitting new agents for dynamic agents in the Networked Multi-Agent System (MAS) Scenario.This dissertation aims at building a hardware and a software platform for communication of multiple UAVs upon which additional control algorithms can be implementated. It starts with building a DJI S1000 octacopter from the ground up. The components used are specified in the following sections. The idea here is to make a drone that can autonomously travel to specified location with safety features like geofencing and land on emergency situations. The user has to provide the necessary commands like GPS locations and takeoff/land commands via a Radio Controller (RC) remote. At any point of the flight, the UAV should be able to receive new commands from the ground control stations (GCS). After successful implementation, the UAV would not be restricted to the range of RC remote. It would be able to travel greater distances given the GPS signal remains operational in the field. This is possible at a global scale with limitation of only the batteries and flight time

Event-triggered Synchronization of Multi-agent Systems with Partial Input Saturation

This paper is concerned with the distributed event/self-triggered synchronization problem for general linear multi-agent systems with partial input saturation. Both the event-based and self-triggered laws are designed using the local sampled, possibly saturated, state, which ensures the bounded synchronization of the multi-agent systems, and exclusion of the Zeno-behavior. The continuous communication between agents is avoided under these triggering protocols. Different from the existing related works, we show the fully distributed design for multi-agent systems, where the synchronization criteria, the designed input laws, and the proposed triggering protocols do not depend on any global information of the communication topology. In addition, the computation load of multi-agent systems is reduced significantly

Synchronous MDADT-Based Fuzzy Adaptive Tracking Control for Switched Multiagent Systems via Modified Self-Triggered Mechanism

In this paper, a self-triggered fuzzy adaptive switched control strategy is proposed to address the synchronous tracking issue in switched stochastic multiagent systems (MASs) based on mode-dependent average dwell-time (MDADT) method. Firstly, a synchronous slow switching mechanism is considered in switched stochastic MASs and realized through a class of designed switching signals under MDADT property. By utilizing the information of both specific agents under switching dynamics and observers with switching features, the synchronous switching signals are designed, which reduces the design complexity. Then, a switched state observer via a switching-related output mask is proposed. The information of agents and their preserved neighbors is utilized to construct the observer and the observation performance of states is improved. Moreover, a modified self- triggered mechanism is designed to improve control performance via proposing auxiliary function. Finally, by analysing the re- lationship between the synchronous switching problem and the different switching features of the followers, the synchronous slow switching mechanism based on MDADT is obtained. Meanwhile, the designed self-triggered controller can guarantee that all signals of the closed-loop system are ultimately bounded under the switching signals. The effectiveness of the designed control method can be verified by some simulation results

Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-Agent Systems with Nonidentical Dimensions

A novel feedback distributed adaptive control strategy based on radial basis neural network (RBFNN) is proposed for the consensus control of a class of leaderless heterogeneous nonlinear multi-agent systems with the same and different dimensions. The distributed control, which consists of a sequence of comparable matrices or vectors, can make that all the states of each agent to attain consensus dynamic behaviors are defined with similar parameters of each agent with nonidentical dimensions. The coupling weight adaptation laws and the feedback management of neural network weights ensure that all signals in the closed-loop system are uniformly ultimately bounded. Finally, two simulation examples are carried out to validate the effectiveness of the suggested control design strategy