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In this paper, we consider a polynomial problem with equilibrium constraints in which the constraint functions and the equilibrium constraints involve data uncertainties. Employing a robust optimization approach, we examine the uncertain equilibrium constrained polynomial optimization problem by establishing lower bound approximations and asymptotic convergences of bounded degree diagonally dominant sum-of-squares (DSOS), scaled diagonally dominant sum-of-squares (SDSOS) and sum-of-squares (SOS) polynomial relaxations for the robust equilibrium constrained polynomial optimization problem. We also provide numerical examples to illustrate how the optimal value of a robust equilibrium constrained problem can be calculated by solving associated relaxation problems. Furthermore, an application to electric vehicle charging scheduling problems under uncertain discharging supplies shows that for the lower relaxation degrees, the DSOS, SDSOS and SOS relaxations obtain reasonable charging costs and for the higher relaxation degrees, the SDSOS relaxation scheme has the best performance, making it desirable for practical applications. [ABSTRACT FROM AUTHOR]

In this paper, we study the finite-time convergence of the time-varying dynamical systems for solving convex and nonconvex optimization problems in different scenarios. We first show the asymptotic convergence of the trajectories of dynamical systems while only requiring convexity of the objective function. Under the Kurdyka–Łojasiewicz (KL) exponent of the objective function, we establish the finite-time convergence of the trajectories to the optima from any initial point. Making use of the Moreau envelope, we adapt our finite-time convergent algorithm to solve weakly convex nonsmooth optimization problems. In addition, we unify and extend the contemporary results on the KL exponent of the Moreau envelope of weakly convex functions. A dynamical system is also introduced to find a fixed point of a nonexpansive operator in finite time and fixed time under additional regularity properties. We then apply it to address the composite optimization problems with finite-time and fixed-time convergence. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of robust two-stage quadratic multiobjective optimization problems. We formulate new necessary and sufficient optimality conditions for a robust two-stage multiobjective optimization problem. The obtained optimality conditions are presented by means of linear matrix inequalities and thus they can be numerically validated by using a semidefinite programming problem. The proposed optimality conditions can be elaborated further as second-order conic expressions for robust two-stage quadratic multiobjective optimization problems with separable functions and ellipsoidal uncertainty sets. We also propose relaxation schemes for finding a (weak) efficient solution of the robust two-stage multiobjective problem by employing associated semidefinite programming or second-order cone programming relaxations. Moreover, numerical examples are given to demonstrate the solution variety of our flexible models and the numerical verifiability of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider a bilevel polynomial optimization problem, where the constraint functions of both the upper-level and lower-level problems involve uncertain parameters. We employ the deterministic robust optimization approach to examine the bilevel polynomial optimization problem under data uncertainties by providing lower bound approximations and convergences of sum-of-squares (SOS) relaxations for the robust bilevel polynomial optimization problem. More precisely, we show that under the convexity of the lower-level problem and either the boundedness of the feasible set or the coercivity of the objective function, the global optimal values of SOS relaxation problems are lower bounds of the global optimal value of the robust bilevel polynomial problem and they converge to this global optimal value when the degrees of SOS polynomials in the relaxation problems tend to infinity. Moreover, an application to an electric vehicle charging scheduling problem with renewable energy sources demonstrates that using the proposed SOS relaxation schemes, we obtain more stable optimal values than applying a direct solution approach as the SOS relaxations are capable of solving these models involving data uncertainties in dynamic charging price and weather conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a new fixed-time sliding mode (FSM) control, where the settling time for reaching the system origin is bounded to a constant independent of the initial condition; this is in contrast to the initial condition-dependent constants used in the traditional linear sliding mode (LSM) and terminal sliding mode (TSM) controls. First, a new sliding mode control with a single power term is discussed, where the power term can have any nonnegative value. Except for the traditional LSM and TSM controls, a new sliding mode control called power sliding mode (PSM) is proposed, whose power term is larger than 1. Then, a new FSM control with two power terms is investigated, whose design is based on the combination of TSM and PSM. In particular, the two power terms on the plane in the first quadrant are carefully discussed, and a detailed classification is provided. Here, the first quadrant can be classified into six categories, including LSM, generalized LSM, TSM, fast TSM (FTSM), PSM, and FSM. Furthermore, the analytical settling time is calculated, and three different estimation bounds of the settling time are given for reaching the origin under any initial condition. It is also interesting to derive the lowest bound for the settling time. Finally, FSM control design for general nonlinear dynamical systems with the relative degree from the control input to the output is also discussed. [ABSTRACT FROM AUTHOR]

Achieving asymptotical cooperative goal for multi-agent systems (MASs) with non-autonomous leaders (i.e., leaders with nonzero inputs) is a critical but challenging issue. Traditional approach is to use discontinuous controllers which may cause chattering phenomenon in practical applications. How to achieve the asymptotical goal via a chattering free cooperative controller remains to be open so far. In this paper, an adaptive continuous controller is designed to achieve zero error consensus tracking in multiple Lur'e systems with a non-autonomous leader under directed switching topology. Firstly, an unknown input observer (UIO) based on relative outputs is given to estimate the relative full states. Then an adaptive continuous controller is designed by introducing a decay function which remains positive into the term that plays the role of eliminating the impacts of leader's nonzero inputs. Secondly, by using multiple Lyapunov functions (MLFs) technique, it is proven that zero error consensus tracking can be achieved if the average dwell time (ADT) is greater than a positive threshold. Finally, theoretical result is verified by performing simulations on Chua's circuits. Compared with existing work, the proposed controller can not only achieve asymptotical consensus, but also is chattering free. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay. [ABSTRACT FROM AUTHOR]

A robust output regulation of a control system aims at disturbance rejection and reference tracking in the presence of uncertainties. Technically, the control design contains two mechanisms: the compensation of nontrivial steady states caused by disturbances and/or reference trajectories and the stabilization of an error system relative to steady states. Feedforward compensation and the internal model (including an approximate integrator) are the two main techniques for the former, while the latter is on a case-by-case basis. This article studies an alternative sliding-mode-based approach that can address the two mechanisms simultaneously, thanks to the new concept of sliding-mode-based output zeroing manifold. This concept bridges the two widely studied areas: robust output regulation and sliding-mode control. The approach is also verified by its application in permanent magnet synchronous motor servo systems. [ABSTRACT FROM AUTHOR]

This article’s primary motivation is to propose a nonsingular and continuous terminal sliding-mode (TSM) control with reduced chattering, which is able to rapidly stabilize the second-order plant with high precision. To this end, a novel sliding-mode manifold, coined as practical TSM (PTSM) one, is first constructed. Once the proposed sliding surface is reached, a Lipschitz-continuous but slope-steep generalized velocity will be established at the origin such that the global nonsingularity of the equivalent control is ensured. The fast dynamic response with local high gain characterizes the sliding behavior inside the unit neighborhood of the origin. Importantly, the analytical solution of the proposed sliding-mode reduced-order system is deduced, which indicates that the finite time taken to slide into a preset small neighborhood of the origin can be calculated. The above designability of convergence time is necessary for control practices with accurate time sequence planning. Further, the corresponding globally singular-free PTSM reaching law with reduced chattering is designed based on the super-twisting algorithm. Finally, several groups of linear-motor-based control experiments verify the superiorities of proposed controllers. [ABSTRACT FROM AUTHOR]

This article investigates the distributed time-varying optimization problem for second-order multiagent systems (MASs) under limited interaction ranges. The goal is to seek the minimum of the sum of local time-varying cost functions (CFs), where each CF is only available to the corresponding agent. Limited communication range refers to the scenario where the agents have limited sensing and communication capabilities, that is, a pair of agents can communicate with each other only if their distance is within a certain range. To handle such a problem, a new continuous connectivity-preserving mechanism is presented to preserve the connectivity of the considered network. Then, two distributed optimization algorithms are presented to solve the optimization problem with time-varying CFs and time-invariant CFs, respectively. Theoretical analysis and two numerical examples are provided to verify the effectiveness of the methods. [ABSTRACT FROM AUTHOR]

The sensorless speed regulation problem for a surface-mount permanent magnet synchronous motor system is studied in this article. First, a new nonsmooth observer scheme is proposed to estimate the rotor position, speed, and lumped disturbance of the system. Based on the estimated information, a composite speed controller is then designed, in which the nonsmooth control technique is employed in the feedback design, and the estimated value of disturbance is used in the feedforward compensation. By Lyapunov theory, rigorous analysis guarantees the stability of the closed-loop system and shows that the proposed method can enhance the disturbance rejection property of the system. Finally, simulation tests and experiment results are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article aims to establish an appointed-time observer-based framework to efficiently address the resilient consensus control problem of linear multiagent systems with malicious attacks. The local appointed-time state observer is skillfully designed for each agent to estimate the agent’s actual state value at the appointed time, even in the presence of unknown malicious attacks. Based on the state estimation, a new kind of resilient control strategy is proposed, where a virtual system is constructed for each agent to generate an ideal state value such that the consensus of normal agents can be achieved with the exchange of ideal state values among neighboring agents. To specify the consensus trajectory while achieving resilient consensus, the leader–follower resilient consensus is further studied, where the leader is assumed to be a trusted agent with a bounded control input. Compared with the existing results on the resilient consensus, the proposed distributed resilient controller design reduces the requirement on communication connectivity significantly, where the allowable communication graph is only assumed to contain a directed spanning tree. To verify the theoretical analysis, numerical simulations are finally provided. [ABSTRACT FROM AUTHOR]

A new super-twisting-like fractional (STLF) controller is proposed in this article to improve the control performance for surface-mounted permanent magnet synchronous motor (SPMSM) system. Compared with the conventional super-twisting sliding mode (STSM) algorithm, the most distinctive characteristic of the proposed strategy is to replace the discontinuous switching function hidden under the integration with a nonsmooth term, which can significantly optimize the performance of SPMSM system. Meanwhile, the introduction of nonsmooth term can provide stronger anti-disturbance capability than the widely used smooth controller. In addition, the desired control performance can be achieved by adjusting the parameters of the proposed STLF controller according to the specific situations. Comparative experiments among the proposed STLF, conventional STSM and proportional integral controllers are carried out to demonstrate the feasibility and effectiveness of the proposed STLF technique. [ABSTRACT FROM AUTHOR]

The rapid introduction of electric vehicles (EVs) in the transportation market has initiated the concept of vehicle-to-grid (V2G) technology in smart grids. However, where V2G technology is intended to facilitate the power grid ancillary services, it could also have an adverse effect on the aging of battery packs in EVs. This is due to the instant depletion of power during the charge and discharge cycles, which could eventually impact the structural complexity and electrochemical operations in the battery pack. To address this situation, a median expectation-based regression approach is proposed for parameter estimation of vehicle batteries in V2G systems. The proposed method is built on the property of uncertainty prediction of Gaussian processes for parameter estimation while considering the cell variations as an exogenous function. First, a median expectation-based Gaussian process model is derived to predict the fused and individual cell variations of a battery pack. Second, a magnitude-squared coherence model is developed by the error matrix to detect and isolate each variation. This is obtained by extracting the cross-spectral densities for the measurements. The proposed regression-based approach is evaluated using experimental measurements collected from lithium-ion battery pack in EVs. The parametric analysis of the battery pack has been verified using D-SAT Chroma 8000ATS hardware platform. Performance evaluation shows an accurate estimation of these dynamics even in the presence of injected faults. [ABSTRACT FROM AUTHOR]

This article investigates the distributed optimal cooperation problems for multiple high-order systems, in which the dynamics of each agent is allowed to be subject to unknown nonlinearities. To eliminate the effect caused by unknown nonlinearities, a nonlinearity estimator is developed based on agents’ states, which successfully reconstructs the nonlinear dynamics if the unknown nonlinearities are bounded. And to minimize the sum of multiple local nonlinear cost functions with Lipschitz-type gradients, a couple of static and adaptive state-dependent algorithms are designed, respectively, where each agent may only have access to its own local cost function. It is challenging to solve such an optimal cooperation problem as the performance of the whole multiagent network is evaluated by the sum of all local performance functions. In order to fulfill the goal of cooperative optimization, a state-dependent distributed optimal cooperation algorithm is proposed first. By utilizing tools from the Lyapunov stability theory and convex optimization analysis, it is proven that the considered distributed optimal cooperation problem for high-order nonlinear systems can be solved by the proposed optimal cooperation algorithm if the state-dependent parameters are suitably selected. It is noted that the selections of the state-dependent parameters depend on some global information of the multiagent systems. Furthermore, by incorporating the proposed optimal cooperation algorithm with adaptive parameters strategy, the optimal cooperation problem is solved in a fully distributed manner. Finally, a numerical simulation is shown to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A colored traveling salesman problem (CTSP) as a generalization of the well-known multiple traveling salesman problem utilizes colors to distinguish the accessibility of individual cities to salesmen. This work formulates a precedence-constrained CTSP (PCTSP) over hypergraphs with asymmetric city distances. It is capable of modeling the problems with operations or activities constrained to precedence relationships in many applications. Two types of precedence constraints are taken into account, i.e., 1) among individual cities and 2) among city clusters. An augmented variable neighborhood search (VNS) called POPMUSIC-based VNS (PVNS) is proposed as a main framework for solving PCTSP. It harnesses a partial optimization metaheuristic under special intensification conditions to prepare candidate sets. Moreover, a topological sort-based greedy algorithm is developed to obtain a feasible solution at the initialization phase. Next, mutation and multi-insertion of constraint-preserving exchanges are combined to produce different neighborhoods of the current solution. Two kinds of constraint-preserving $k$ -exchange are adopted to serve as a strong local search means. Extensive experiments are conducted on 34 cases. For the sake of comparison, Lin–Kernighan heuristic, two genetic algorithms and three VNS methods are adapted to PCTSP and fine-tuned by using an automatic algorithm configurator-irace package. The experimental results show that PVNS outperforms them in terms of both search ability and convergence rate. In addition, the study of four PVNS variants each lacking an important operator reveals that all operators play significant roles in PVNS. [ABSTRACT FROM AUTHOR]

Power grids are increasingly evolving into more efficient smart grids thanks to advancements in information technology. However, these intelligent communication-dependant power systems are becoming more susceptible to cyberattacks than their traditional peers. Power inverters are one of the main network connected devices supporting grid stability, which can potentially be targets for cyber-threats. This article presents an attack-resilient model predictive adaptive controller for a class of inverters, called Z-source inverters, to protect them against deception attacks. The proposed control structure includes a model predictive controller equipped with an unknown input Kalman filter and an estimator for system perturbations. The group method of data handling technique is used to estimate system uncertainties and make the system robust against perturbations. The unknown input Kalman filter is also adopted to estimate the states and unknown inputs in the presence of noisy measurements and cyberattacks on the control signal. We mathematically prove that in the presence of noise and perturbations, the proposed controller guarantees the stability of the system under a deception attack, which causes a delay, less than a sampling time, in control signals. Simulation results reveal the effectiveness of the presented controller in protecting the system against pulse, scaling and random attacks in the presence of system uncertainties, source and load fluctuations and output noises. [ABSTRACT FROM AUTHOR]

Parking problem has become a bottle neck of urban traffic management. On one hand, there is no effective way to get timely information of available parking lots. On the other hand, owners of private parking spaces are less willing to share their spare lots. To deal with the parking problem, a new parking sharing network named ParkChain is firstly proposed using blockchain to build a decentralized but trustworthy network. Herein, ParkChain provides a distributed autonomous infrastructure for both vehicle drivers and park granters, which can promote the extensive parking resource sharing and relieve the parking problem. A new blockchain consensus protocol named Proof-of-Planned-Behavior (PoPB) is proposed. It builds the model of the autonomous consensus process based on the theory of planned behavior (TPB), derives a computable threshold for qualification of block data authorizers, and then develops a dynamic authorizer group mechanism for creditability and decentrality considerations. Furthermore, various smart contracts are developed to carry out the parking services transparently. Finally, the prototype has been successfully implemented in a university campus setting to demonstrate the effectiveness of ParkChain. [ABSTRACT FROM AUTHOR]

The consensus problems of fractional-order multi-agent systems(FOMAS) are investigated under a fixed interaction graph. A consensus protocol that has current and time-delay states is designed. The conception of practical consensus of FOMAS is proposed. The asymptotical consensus condition is obtained by taking advantage of Laplace transform. Furthermore, the practical consensus condition of the FOMAS with the protocol is given by direct derivation. Through numerical simulation, we find that the standard control protocol can not achieve consensus in some situations. However, the proposed protocol could make the states of multi-agent be consensus or even practical consensus. [ABSTRACT FROM AUTHOR]

In this technical note, free-will arbitrary time terminal sliding mode control design is proposed. With such a design, it is possible to control the time duration of sliding in the sliding phase. Under the assumption that the chosen arbitrary time is greater than the reaching phase time ($t_{r}$), it is shown that the $n^{\textrm {th}}$ order chain of integrators converges to zero within the selected arbitrary time. An algorithm is also proposed for the case when $t_{r}$ is not known to the designer. Stability analysis based on Lyapunov theory has been provided. [ABSTRACT FROM AUTHOR]

In this article, the free-will arbitrary time consensus is formulated for multiagent systems. This consensus protocol is independent of initial conditions and any other system parameters. With such a protocol, the multiagent system is shown to attain consensus as well as average consensus within the prespecified arbitrary time. Agents rendezvous can also be accomplished with the given protocol. Communication imperfections are easily handled with the designed protocol. Robust free-will arbitrary time consensus protocol is also designed. The stability of such nonlinear nonautonomous protocols is established using suitable Lyapunov functions. Simulation examples confirm the theoretical findings. [ABSTRACT FROM AUTHOR]

Imagine you want to send money to a friend overseas. Wouldn’t it be good if you didn’t have to pay hefty fees to the intermediaries, and your friend received the funds very quickly? Now imagine ordering parts to make a product in your manufacturing plant. Wouldn’t it be great if you were able to verify where each part comes from and have access to a reliable certificate on its quality automatically? Also think about dealing with energy use or selling off your excess solar energy as a prosumer. Wouldn’t it be nice if you could purchase cheaper energy or sell it profitably at ease? [ABSTRACT FROM AUTHOR]

This article investigates the distributed average tracking (DAT) problem for disturbed second-order multiagent systems, where a crowd of agents is required to track the average of the multiple time-varying signals. First, a new kind of distributed average estimator is developed for each agent to estimate the average of the multiple time-varying signals in finite time. The protocol possesses the distinguished feature of robustness to initialization errors, which can recover from network alterations. Then, an observer-based finite-time tracking protocol is proposed to make each agent exactly track the average of the multiple time-varying signals in finite time in the absence of velocity measurement. By carefully analyzing the dynamic properties of the tracking error system, a suitable Lyapunov function is constructed to estimate the settling time for convergence of the tracking error system theoretically. Furthermore, an adaptive DAT protocol is proposed, which is a fully distributed protocol because it can solve the DAT problem without using any global information. Finally, two simulation examples are provided to verify the effectiveness of the methods. [ABSTRACT FROM AUTHOR]

In this article, we investigate the synchronization of complex networks with general time-varying delay, especially with nondifferentiable delay. In the literature, the time-varying delay is usually assumed to be differentiable. This assumption is strict and not easy to verify in engineering. Until now, the synchronization of networks with nondifferentiable delay through adaptive control remains a challenging problem. By analyzing the boundedness of the adaptive control gain and extending the well-known Halanay inequality, we solve this problem and establish several synchronization criteria for networks under the centralized adaptive control and networks under the decentralized adaptive control. Particularly, the boundedness of the centralized adaptive control gain is theoretically proved. Numerical simulations are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

In this article, the asymptotic tracking consensus problem of higher-order multiagent systems (MASs) with general directed communication graphs is addressed via designing event-triggered control strategies. One common assumption utilized in most existing results on such tracking consensus problem that the inherent dynamics of the leader are the same as those of the followers is removed in this article. In particular, two cases that the dynamics of the leader are subjected, respectively, to bounded input and unknown nonlinearity are considered. To do this, distributed event-triggered observers are first constructed to estimate the state information of the leader. Then, local event-triggered tracking control protocols are designed for each follower to complete the goal of tracking consensus. One distinguishing feature of the present distributed observers lies in the fact that they could avoid the continuous monitoring for the states of the neighbors’ observer states. It is also worth pointing out that the present tracking consensus control strategies are fully distributed as no global information related to the directed communication graph is involved in designing the strategies. Two simulation examples are finally presented to verify the efficiency of the theoretical results. [ABSTRACT FROM AUTHOR]

This article addresses the problem of synchronization of impulsive networks with switching topologies. A new synchronization framework is established with an emphasis on settling time estimation. The impulsive networks consist of physical nodes and cyber modules. For physical nodes, states are changed impulsively at discrete time instants due to some switching phenomena or unexpected sudden noises. For cyber modules, two cases of switching scenarios are considered for information exchange patterns during specific time intervals. In the first case, cyber modules lose all the communication links with others, resulting in disconnected topologies. Then, a distributed controller is proposed for nodes without intrinsic nonlinear dynamics. A distinguished feature of this controller is its capability to estimate a bound for settling time, beyond which the synchronization with respect to a virtual target is guaranteed. In the second case, cyber modules lose some communication links but build other new ones with the help of a smart communication center to form connected topologies. A distributed controller is further designed for nodes in the presence of intrinsic nonlinear dynamics. Accordingly, a sufficient condition is derived to achieve synchronization with an estimated settling time bound. For both cases, the estimated bounds are able to reveal the relationship between the impulsive strength and the synchronization performance. Finally, numerical examples including a case study on a modified IEEE 34 bus test feeder are provided to demonstrate the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

In this article, we consider the distributed formation navigation problem of second-order multiagent systems subject to both velocity and input constraints. Both collision avoidance and connectivity maintenance of the network are considered in the controller design. A control barrier function method is employed to achieve multiple control objectives simultaneously while satisfying the velocity and input constraints. First, a nominal distributed leader-following formation controller is proposed which satisfies the velocity and input constraints uniformly and handles switching communication graphs. A nonsmooth analysis is employed to prove the global convergence of the controller. Then, a topology-based connectivity maintenance strategy using a new notion of the formation-guided minimum cost spanning tree is proposed and the corresponding barrier function-based constraints are derived. The barrier function-based collision-avoidance conditions are also developed. All barrier function-based constraints are then combined to formulate a quadratic programming problem which modifies the nominal controller when necessary to achieve both collision avoidance and connectivity maintenance. Simulation results demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Distributed stabilization problem is studied in this article for multiple heterogeneous agents in the uncertain strong–weak competition network with exogenous disturbances, where the agents are modeled by the second-order systems with different nonlinear intrinsic dynamics, and the network uncertainty is characterized by unknown nonzero parameters, which contains three different relationships among agents: 1) cooperation; 2) strong competition; and 3) weak competition. To achieve distributed stabilization, the whole network is first divided into two parts: 1) identifiable part and 2) unidentifiable part, and a new distributed robust integral sign of the error (RISE) controller is designed for each agent, where the selection rules of the corresponding parameters are given. It is shown that the heterogeneous multiagent system (MAS) can achieve distributed stabilization no matter whether the identifiable part is structurally balanced or not. Furthermore, it is proved that the global distributed stabilization is achieved for the heterogeneous agents provided that the partial derivatives of the nonlinear intrinsic dynamics are bounded. Finally, two numerical examples are given to demonstrate the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

This article investigates the sliding-mode control design problem for uncertain time-varying delayed systems. A novel non-negative constraints approach is proposed, which can be used to determine the stability of the controlled systems during the sliding motion. A new stability condition is obtained, which is easy to satisfy and verify. The parameters of the sliding-mode surface can be calculated by solving the non-negative constraints-based optimization problem defined in this article. Simulation examples are presented to illustrate the effectiveness and advantages of the developed strategy. [ABSTRACT FROM AUTHOR]

Non-singular terminal sliding mode control (NTSMC) is widely popular due to its various industrial applications. In many practical circuit systems, the control input is actually given to an actuator rather than directly to the plant. Therefore, it is important to study the effect of actuators on NTSMC performance. In this brief, frequency-domain characteristics of NTSMC are analyzed using the describing function method. It is shown that in the presence of actuator dynamics, the system exhibits a self-sustained periodic oscillation known as chattering. The relationship between the fundamental frequency, the amplitude of this periodic motion and the NTSMC parameters is explored. The effectiveness of the theoretical analysis is verified via a practical example based on an industrial emulator system. [ABSTRACT FROM AUTHOR]

For uncertain systems with relative degree two, an output feedback based twisting algorithm is proposed. This algorithm ensures practical asymptotic convergence to the origin based on the information of output and its artificially delayed version. We prove the convergence of the proposed algorithm by a continuous, weighted homogeneous and strict Lyapunov function. [ABSTRACT FROM AUTHOR]

Two types of formation control problems for a group of unmanned surface vessels (USVs) are investigated by using a game‐theoretic approach. In the two formation frameworks, the USVs have the group formation objective of minimizing the formation errors with their local neighbors. Different from the existing formation control schemes for USVs with only the group objective, the formation control framework in this paper takes into account the individual objectives of the USVs in addition to the collaborative formation requirement for the USVs. Specifically, each USV in the first formation framework has the individual objective of aiming to be located at a given static reference target according to its own interests. And the USVs in the second formation framework have the individual objectives of following a given time‐varying reference target. Considering that the individual objectives of the USVs may conflict with the group formation objective, the formation problem of USVs is transformed to a noncooperative game among multiple USVs. Two controllers based on the Nash equilibrium seeking strategy are designed to solve the formation problem of USVs with conflicting objectives. It is proved that the USVs' states form an equilibrium formation pattern driven by the designed controllers. Simulations are performed to verify the theoretical results. [ABSTRACT FROM AUTHOR]

With the rapid development of EV in the transportation system, its increasing influence on the power system has attracted more and more attention in recent years. Under such a background, a virtual EV energy network (VEEN) in traffic level coupled with the corresponding power grid is formed in this paper, considering the energy carried by EVs in a specified area as a virtual node. To further identify the vulnerable components in the coupled energy network, a hybrid approach based on a complex network (CN) is presented. A hybrid power transfer distribution factor (HPTDF) is proposed for the coupled energy network considering the variation of VEENs over time. Then, the line betweenness and the modified time-varying coupling net-ability are designed to evaluate the vulnerability of the coupled network. Furthermore, to demonstrate the applicability of the proposed models, three case studies are performed on the 39-bus IEEE power network coupled with a 19-node VEEN. The simulation result verifies the effectiveness of the method in terms of identifying the critical lines in a coupled energy network, and a vulnerability assessment progress by designing a slow-fast dynamic cascade failure simulator in this paper verifies that a VEEN layer coupling defense strategy can decrease the vulnerability of a power grid layer under random attacks. [ABSTRACT FROM AUTHOR]

This paper aims to improve the performance of the permanent magnet synchronous motor (PMSM) speed regulation system by combining a novel disturbance observer (DOB) with the super-twisting sliding mode (STSM) technique. First, a STSM controller is constructed to eliminate the adverse effects of the lumped disturbance in the PMSM speed regulation system. A novel DOB is introduced to estimate and compensate the lumped unknown disturbance, which constitutes a composite controller with a feedforward compensation term and a state-feedback control. As a result, the gain of the composite sliding mode controller can be significantly reduced, which will improve the performance of the closed-loop PMSM speed regulation system. The validity and robustness of the proposed composite control scheme are fully verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of global finite-time output stabilization for a class of second-order nonlinear systems with both uncertain nonlinear dynamics and unknown external disturbance. Specifically, at the first step, without considering the external disturbance, a global finite-time state feedback controller is proposed to dominate uncertain nonlinear dynamics of the second-order system. To address the more challenging case where only the system output is available, a novel design idea of the nonseparation principle is employed. By treating the unknown external disturbance as a generalized state, a finite-time convergent extended state observer is constructed to estimate the unmeasured state and the unknown external disturbance. Based on this observer, an output-based composite controller with finite-time convergence is developed. The global uniform finite-time stability of the overall closed-loop system is proven based on the Lyapunov method. Simulation results of the inverted pendulum system demonstrate the superiority of the proposed control method in terms of both convergence performance and disturbance rejection performance. [ABSTRACT FROM AUTHOR]

In building-microgrid communities, renewable generation and time-varying load usually cause power fluctuations, which influence the ancillary support to the main grid. Thermostatically controlled loads (TCLs) can be utilized to compensate such power variations due to their aggregated and controllable power consumptions. Meanwhile, one basic requirement for the users’ side of TCLs is to realize the fair sharing of power states and comfort states. This article proposes a distributed event-based control strategy, where information of neighboring TCLs is exchanged only when a dynamic event-triggered condition is satisfied, and thus it intelligently determines the necessary transmission frequency to save communication resources. From a cybersecurity perspective, the communication network of TCLs may be subject to hybrid attacks, for example, denial-of-service (DoS) and false data-injection (FDI) attacks. During DoS attack intervals, no information can be communicated even through the event-triggered condition is satisfied. Furthermore, the control inputs may also be tampered by FDI attacks. By utilizing the Lyapunov stability and hybrid control theories, sufficient conditions regarding the attack parameters are derived such that fair sharing of power states and comfort states of all involved TCLs can be achieved exponentially. The exclusion of Zeno behaviors is proved and a corollary for ideal communication situations is also deduced. Finally, simulation examples with various attack parameters are conducted to verify the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

Distributed energy resources create a prosumers era. Peer-to-peer (P2P) energy sharing is an effective way to conduct prosumers-based energy management. Integrating prosumers’ social attributes in energy engineering substantially affects their decisions, which brings challenges to the energy scheduling under the cyber-physical-social environment. In this paper, a data-driven stochastic game model with prosumers’ social attributes is proposed for P2P energy sharing. According to social psychology, the prosumers’ social attributes are expressed as subjective probabilities, which are studied by the spatial-temporal graph convolutional networks. In the network, a double-layer feature graph is built to learn the social attributes based on the social survey data and load metering data. The P2P energy sharing incurred randomness comes from social attributes of interactive prosumers, which is formulated as a stochastic game model. In this game, a subjective utility model is proposed for prosumers, and the energy scheduling is conducted with the designed dynamic interval adjustment method. Numerical analysis reveals the results of the learning network and generalized Nash equilibrium. Through comparing with rational scenarios, the influence of prosumers’ social attributes on P2P energy sharing is concluded. [ABSTRACT FROM AUTHOR]

This brief studies distributed event-triggered law design of the Nash equilibrium seeking for noncooperative games under strongly connected graphs. Each player is desired to maximize its payoff function by updating its own action based on other players’ actions. An event-triggered law is proposed, and each player estimates all other players’ actions and exchanges the estimated information with their neighbors only when the triggering conditions are satisfied. Theoretical demonstration is presented to verify that the actions and estimates of players all converge to the unique Nash equilibrium under the proposed event-triggered law and Zeno behavior is excluded. Simulations are provided to show the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

A great deal of attention from various scientific communities has been recently drawn to complex network systems (CNSs), with many profound results established in this active research field. This article provides a state-of-the-art survey on coordination and control of CNSs with switching network topologies, with emphasis on relationships between the switchings among different topology candidates and the network controllability, and between the switchings among different topology candidates and the emergence of coordination behaviors (including synchronization, consensus, and containment) of such CNSs. First, some fundamental properties of CNSs and the essentials of analytical methodologies for the stability of the fixed point of switched dynamical systems are briefly reviewed. Then, network controllability and the emergence of coordination behaviors of CNSs with switching topologies and the corresponding analytical approaches are discussed in detail, where some of the existing results along these topics are presented in a tutorial-like fashion. This article ends by presenting some interesting future research topics on the coordination and control of CNSs with switching topologies. [ABSTRACT FROM AUTHOR]

Distributed consensus tracking problem of networked agent systems with directed topologies under denial-of-service (DoS) attacks is investigated. The considered networked agent network consists of a physical layer with fixed physical links and a cyber layer with cyber control units. Both the communication network connecting these two layers and the cyber communication network within the cyber layer may subject to malicious DoS attacks. These two types of attacks have different impacts on the networked system; the former one affects the timely update of control inputs, and the latter influences the connection weights of the cyber communication graph. First, for DoS signals occurring in the communication network which transmits the state and control input information between the two layers, the distributed control protocol based on the event-triggered scheme and locally deployed estimators are designed. Efficient algorithms for selecting event-triggered control parameters and Zeno-free triggered parameters are given to ensure the mean-square consensus. The relationships between the system’s parameters and the features of DoS attacks are successfully revealed. Second, corresponding theoretical analysis is derived for consensus tracking of the networked systems when DoS attacks are launched within the cyber layer. Conditions concerning the length of repairing time and indexes of DoS attacks are given by utilizing hybrid control theory. At last, the effectiveness of the obtained results is demonstrated by performing simulations on multirobot systems. [ABSTRACT FROM AUTHOR]

This article presents a full mathematical proof of the characteristic modeling approach for high-order linear dynamical systems. It explores the nature of the characteristic model in rigorous mathematical forms, also showing why and how the high-order dynamics can be compressed into the lower-order characteristic model. The relationships between high-order linear continuous dynamical systems, discrete-time characteristic model coefficients, and sampling-time intervals are investigated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple cohesive framework to find an optimal directed control network topology with minimum number of links while a prescribed decay rate is satisfied in the transient response of a distributed control system. In order to guarantee the system’s decay rate to be faster than a prespecified value, a constraint on the dominant eigenvalue of the system is required to be considered. This results in a nonconvex optimization problem as eigenvalue of a parametric nonsymmetric matrix is a nonconvex, nonsmooth, and even non-Lipschitz function. Here, we present a convex equivalent optimization problem whose minimizer also solves this eigenvalue optimization problem. This optimization problem proposes a state-feedback matrix which results in a decay rate faster than a given value while input signal costs are considered. The equivalent optimization problem in combination with sparsity-promoting optimal control constitutes a combinatorial optimization problem. Using alternating direction method of multipliers, the problem is decomposed into a chain of analytically solvable subproblems which are differentiable and separable. The proposed optimization framework includes relative preference between the topology of the control network and the decay rate of the system. The simulation results show the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

With the increase of vehicle travelling range and the decrease of vehicle expense, Electric Vehicles (EVs) are gaining popularity as a new transport option which offers a great opportunity for creating a smart city in terms of transportation electrification. However, the existing research of EV charger planning primarily concentrates on the optimal location seeking from a perspective of global optimization. To achieve a competitive strategy in future, a market-based mechanism is proposed for the problem of EV charger planning with a case study of Sydney where a predict-then-optimise diagram is introduced to predict the EV charging demand by a multi-relation graph convolutional network (GCN)-based encoder-decoder deep architecture and optimise the competitive resource allocation strategy for the charger planning through a Cournot competition game model. A new parallel computational algorithm is proposed to seek the Cournot competition equilibrium with the convergence analysis. Furthermore, the optimal EV charger sizing problem is considered to obtain each service provider’s EV charger number at each zone of the city. Experiments are implemented by using the Sydney public transportation dataset and the corresponding key economic indicators. The results show the effectiveness of our proposed approach which can fill the gap between data and the optimal EV charging infrastructure planning. [ABSTRACT FROM AUTHOR]

In this paper, characteristic model-based modeling and control approaches for complex dynamical networks based on sampled data are studied. It shows that the characteristic model, in which underline network topological structures are simplified, can provide a straightforward and implicit description for network dynamics. The induced parameter estimation method can further make the model adaptive and purely data-driven. Moreover, a control law based on this model is also proposed to govern the network dynamics. Finally, the theoretical results are verified through numerical simulations of modeling and stabilizing a dynamical network. [ABSTRACT FROM AUTHOR]