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Peer-to-peer (P2P) energy sharing network formed by the direct interconnection of prosumers will be a future trend of the emerging prosumer era. The prosumer-oriented nature of P2P energy sharing network naturally drives the integration of cyber, physical and social layers. In this article, a cyber-physical-social system (CPSS) approach is proposed for P2P energy sharing of prosumers, and based on which many-to-many matching theory is proposed for prosumers with respect to their mutual preferences towards each other. First, a bipartite network is designed to present the relations between the seller set and buyer set, where the weights of the network’s edges are bilateral valued by the physical-social coupled preference utilities, which leverages both the physical and social layer knowledge to capture the prosumers preference in energy sharing. Then, the energy sharing problem is modelled as a two-stage optimization problem, where the first stage is to solve the problem of maximizing the total welfare of all participants with network constraints, and the second stage is to solve the efficient P2P energy sharing problem between prosumers by utilizing many-to-many matching theory. Furthermore, and a price-iterative-based Modified Deferred Acceptance (MDA) many-to-many algorithm is proposed to solve the problem. Finally, the simulation results verify the feasibility and performance of the proposed method.

In this study, we investigate the accelerated learning control schemes for point-to-point tracking systems (PTSs) with measurement noise. The asymptotic convergence of the generated input sequence has been a long-standing open issue for point-to-point tracking problems because there are infinite possible input candidates that can drive the system dynamics to track the desired reference at specified time instants. An accelerated gradient algorithm and its generalized version with a novel direction regulation matrix are proposed, with the learning gain is adaptively triggered by the practical tracking errors. The learning gain remains constant at the early stage and begins to decrease after a certain number of iterations. The input sequence generated by the proposed scheme converges to a specified limit for any fixed initial input, with the limit being closest to the initial input, in a certain sense. Numerical simulations are provided to verify the theoretical results.

The Internet of Things (IoT) is widely acknowledged as an innovative paradigm that engenders profound alterations along with society's development due to its inevitability and universality. To effectively control IoT systems for specific tasks, it is important to tackle their emerging challenges, such as information transmission and sensing for the environment or specific task. Integrating sensing and communication (ISAC), which achieves the communication and sensing functionalities over one hardware platform, has significant advantages over dedicated sensing and communication, and has been regarded as a key technique for IoT ecosystems with the upcoming 6G communication revolution. Bearing this in mind, we provide an overview of ISAC-enabled IoT systems in terms of their framework and key technologies. In particular, we first give a basic introduction to control systems, which guides the design of our considered system structure. Then, ISAC and other enabling technologies are illustrated to facilitate our proposed system. Benefiting from the communication and sensing functionalities, our proposed ISAC-enabled IoT systems enable task-oriented control. Future challenges and directions for more efficient task-oriented IoT are also discussed.

In this work, we consider the safe deployment problem of multiple robots in an obstacle-rich complex environment. When a team of velocity and input-constrained robots is required to move from one area to another, a robust collision-avoidance formation navigation method is needed to achieve safe transferring. The constrained dynamics and the external disturbances make the safe formation navigation a challenging problem. A novel robust control barrier function-based method is proposed which enables collision avoidance under globally bounded control input. First, a nominal velocity and input-constrained formation navigation controller is designed which uses only the relative position information based on a predefined-time convergent observer. Then, new robust safety barrier conditions are derived for collision avoidance. Finally, a local quadratic optimization problem-based safe formation navigation controller is proposed for each robot. Simulation examples and comparison with existing results are provided to demonstrate the effectiveness of the proposed controller.

The process industry is an industrial field of interdisciplinary nature involving electrical engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the sustainable development. As a main source of CO2 emissions, the process industry will undertake a large part of the emission reduction task. In order to incorporate the impact of social factors, such as environment, society, and human to support the future process industry, the cyber–physical–social system (CPSS) framework should be considered as a promising way to enhance the transformation of the process industry. The development of CPSS technologies will fundamentally change the infrastructure of conventional industrial systems, offering a great opportunity for the greenization, high-value, and digitalization in the process industry. This article first presents the current status of the process industry. Through a CPSS framework, the current developments of the process industry as well as the main challenges and opportunities are discussed. A vision for the future process industry based on CPSS is described by focusing on three aspects, namely, the greenization and low carbon, high-value and high-end, digitalization, and intellectualization in process manufacturing. Finally, the advanced technologies and approaches in CPSS driven by artificial intelligence and industrial digitalization, which are important in achieving the sustainable development of the process industry, are outlined. The development of the comprehensive digital technologies, such as virtual reality, digital twin, blockchain, and big data, will stimulate the implementation of a ground-breaking concept formed in the CPSS framework called industrial metaverse.

A new resilient distributed secondary control for AC microgrids is studied based on event-triggered mechanisms and trust-reputation evaluation methods. When distributed generators (DGs) in a microgrid are subject to attacks, their transmitted state information would be tampered and thus affect the dynamics of normal generators. In order to isolate possible attacks, two types of trust evaluation metrics with different attack indices and time scales are designed, by which the performance of neighboring DGs can be assessed for specific practical demands. Based on the trust values of each neighbor, a reputation-propagation method is introduced at triggered time instants to determine whether a DG is under attack by comprehensively incorporating the opinion of mutual neighbors. The dynamic updating law of the communication edge weights is utilized with the derived reputation values. Based on this, a distributed Zeno-free event-triggered control protocols for voltage/frequency restoration and active power sharing are proposed. Sufficient conditions for picking proper control parameters are given in the main theorem. Lastly, the simulations are conducted in MATLAB/SimPowerSystems for several scenarios to validate the effectiveness of the proposed algorithms.

Energy demand prediction can be obtained for different customer categories or geolocations, e.g., predicting the energy demand over different cities. Traditionally, these prediction tasks are solved independently without considering the common problem-solving knowledge sharing among them. However, addressing one task may help facilitate the training process or improve the prediction performance of another one via knowledge transfer. In this article, we propose a two-stage multitasking prediction (TS-MTP) framework to address the energy demand prediction problem over multiple locations, in which each task has a deep neural network (DNN) model as the predictor. TS-MTP includes single-tasking learning (STL) and multitasking learning (MTL) stages. The STL stage focuses on addressing each prediction task independently with a gradient descent-based optimization algorithm until the training accuracy cannot be improved, so that the optimal DNN structure parameters for each task can be achieved. In the MTL stage, for a specified target task, the knowledge, i.e., DNN connection weights and biases acquired in STL, is extracted and transferred from the source tasks and reused in the target task to help further improve its prediction accuracy. To decide the amount of knowledge to be reused, a coefficient is assigned to each source task, and particle swarm optimization is applied to obtain the optimal coefficients. The performance of TS-MTP is verified on several problem sets that are created from different step-ahead predictions. The superiority of TS-MTP is demonstrated in comparison to several state-of-the-art DNNs that are popular in the time-series prediction domain. The results show that TS-MTP can lead to a more than 35% accuracy improvement compared with the STL without knowledge transfer.

In this article, we consider the safe consensus tracking problem for uncertain second-order nonlinear multiagent systems subject to position, velocity, and input constraints. The agents are required to cooperatively track a desired leader's trajectory while always satisfying their local state and input constraints. Therefore, conflicting objectives may exist for an agent when the desired trajectory violates its local constraints. We propose to solve this problem using a control barrier function (CBF)-based approach. The cooperative tracking objective is encoded by a novel control Lyapunov function-based condition while the state and input constraints are handled by CBF-based constraints. For the relative degree two position constraint, two classes of CBF-based conditions are proposed based on high-order CBFs and a modified CBF design, respectively. It is proven that, with the modified CBF, there always exist feasible control inputs that satisfy all the CBF-based constraints. Then, unified quadratic programming-based controllers are formulated and the performances are analyzed. Simulation examples are provided to verify the obtained results.

This brief studies the tracking consensus of multi-agent systems (MASs) with varying number of agents under actuator attacks. Solving such a problem is quite challenging due to the effect of actuator attacks and the switching dynamics of tracking error signals caused by the entry and departure of agents. To facilitate the analysis, it is assumed that the dynamic changes of tracking error and estimation error signals around the switching time instants can be characterized by several inequalities. To solve the tracking consensus problem under consideration, a full-order distributed observer designed based only on the output information of agent is proposed for each following agent to observe its states. Then, a kind of fault-tolerant controllers is skillfully constructed based upon the observer states and the upper bound of actuator attack signals to guarantee the achievement of tracking consensus under actuator attacks. It is demonstrated that the tracking consensus in the closed-loop MASs can be ensured if there is at least one directed path from the leader to each following agent and the dynamics of each agent is controllable. Finally, a numerical simulation is used to demonstrate the effectiveness of the designed controller.

This article proposes a novel event-triggered second-order sliding mode (SOSM) control method for uncertain nonlinear systems. First, three saturated-like functions are predesigned to construct a new switched triggering mechanism. Under the proposed triggering mechanism, an event-triggered SOSM controller is designed to ensure that the states of the SOSM system finite-time converge to a domain of the origin and never escape from the domain. Then, to avoid Zeno behavior, two positive minimum inter-execution intervals are obtained based on different triggering conditions. Finally, a simulation study is given to verify the effectiveness of the control strategy.

Energy demand prediction can benefit electricity consumers, distribution network service providers, and system operators. It heavily depends on auxiliary factors, such as weather parameters (e.g., ambient temperature), which makes this problem more complex. Moreover, for many industrial applications, instead of aiming for the highest prediction accuracy, a more easily understandable and interpretable model that can lead to higher accuracy against the baseline model is the priority. Therefore, this problem still requires more investigation, especially when there is a specified prediction baseline to be compared with. This paper proposes a machine learning (ML) based prediction framework that investigates how temperature combined with energy consumption and simple and interpretable ML methods can be used to provide more precise demand forecasts and thus baselines closer to actual load profiles. The proposed framework is tested on two different real-world energy demand datasets. The analysis shows that using a simple ML model, such as a polynomial regression model, results in a more accurate prediction than the current baselines used in the energy market. The proposed ML models are not black-box type models, and thus are easier to explain and interpret. The ML-based forecasted demand is used as a baseline for demand response (DR) and is compared with the existing baselines used in the demand response market of Australia’s national grid.

Knowledge of the Laplacian eigenvalues of a network provides important insights into its structural features and dynamical behaviours. Node or link removal caused by possible outage events, such as mechanical and electrical failures or malicious attacks, significantly impacts the Laplacian spectra. This can also happen due to intentional node removal against which, increasing the algebraic connectivity is desired. In this article, an analytical metric is proposed to measure the effect of node removal on the Laplacian eigenvalues of the network. The metric is formulated based on the local multiplicity of each eigenvalue at each node, so that the effect of node removal on any particular eigenvalues can be approximated using only one single eigen-decomposition of the Laplacian matrix. The metric is applicable to undirected networks as well as strongly-connected directed ones. It also provides a reliable approximation for the “Laplacian energy” of a network. The performance of the metric is evaluated for several synthetic networks and also the American Western States power grid. Results show that this metric has a nearly perfect precision in correctly predicting the most central nodes, and significantly outperforms other comparable heuristic methods.

With the advance in communication facilities and information technologies, the state estimation (SE) of distribution networks is subject to intensified cybersecurity threats caused by false data injection attacks (FDIAs). To address the issues, this paper proposes a novel FDIA detection strategy for unbalanced distribution networks. The SE model and corresponding general imperfect FDIA are introduced first to emulate the attacking behavior in practice. To achieve FDIA detection, we propose a square-root unscented Kalman filter (SR-UKF) based forecasting-aided SE (FASE) to generate estimation results. By modifying the filtering step of the proposed FASE into a redundant linear regression form, random outliers can be effectively detected and suppressed by leveraging the projection statistics (PS). Afterward, based on the acquired SE results, a generalized likelihood ratio test (GLRT) is designed to detect FDIAs on consecutive snapshots. In the GLRT, the dynamic time warping (DTW) distance between two innovation sequences is set as the test variable, which is compared with the offline determined detection threshold under a specific false alarm rate. The feasibility of the proposed general imperfect FDIA and the effectiveness of the proposed FDIA detection strategy are validated through extensive numerical simulations.

In this article, we investigate the asymptotical consensus tracking problems for multiple-input-multiple-output linear multiagent systems with directed switching topology and a nonautonomous leader subject to nonzero unknown inputs. First, we design a full-order unknown input observer (UIO) based on relative outputs to estimate the relative full states. Based on this UIO, we design a continuous consensus controller by introducing a decay function, which remains positive into the term that is used to eliminate the effects of the leader's nonzero inputs. And by using the multiple Lyapunov functions method, we prove that asymptotical consensus can be achieved if the average dwell time is greater than a positive threshold. Second, we design a continuous consensus controller based on a reduced-order UIO that can significantly simplify calculations. Finally, we give two examples of multiple unmanned aerial vehicles to verify the theoretical results. Compared with existing works, the continuous controllers here can not only achieve zero-error consensus tracking but also are chattering-free.

In this article, both the fixed-time distributed consensus tracking and the fixed-time distributed average tracking problems for double-integrator-type multiagent systems with bounded input disturbances are studied. First, a new practical robust fixed-time sliding-mode control method based on the time-based generator is proposed. Second, two fixed-time distributed consensus tracking observers for double-integrator-type multiagent systems are designed to estimate the state disagreement between the leader and the followers under undirected and directed communication, respectively. Third, a fixed-time distributed average tracking observer for double-integrator-type multiagent systems is designed to measure the average value of multiple reference signals under undirected communication. Note that all the proposed observers are constructed with time-based generators and can be trivially extended to that for high-order integrator-type multiagent systems. Furthermore, by combining the proposed fixed-time sliding-mode control method with the information provided by the fixed-time observers, the fixed-time controllers are designed to solve the fixed-time distributed consensus tracking and the distributed average tracking problems. Finally, a few numerical simulations are shown to verify the results.

In distributed optimization (DO), the designed algorithms are expected to have a fast convergence rate but less computation cost. Moreover, the boundedness of the control inputs is generally required for practical networking agent systems with actuator limitations. Motivated by these observations, we first revisit the well-known finite-time distributed average tracking (FTDAT) problem where a novel sufficient condition on the control gain and the finite settling time estimation are derived based on the minimum cut of the underlying topology. Then, based on FTDAT, three types of discontinuous dynamics with bounded inputs are designed for solving unconstrained and constrained DO problems, respectively. The first algorithm can successfully find the optimal solution for an unconstrained DO in finite time. For DO problems with a common constraint set or separated equality constraints, two projection-based algorithms are designed and utilized to first make the agents' states achieve consensus in finite time, and then drive the common state to the optimum exponentially. Finally, several case studies and extensive numerical simulations are conducted to testify the designed algorithms.

This article studies the conflicting goals of high-precision tracking and quick convergence speed, which is a longstanding problem in the learning control of stochastic systems. In such systems, a decreasing gain sequence is necessary to ensure the asymptotic convergence of the generated input sequence to a fixed limit. However, the convergence speed is adversely affected by gain sequences of this nature. In this article, we propose a novel multistage learning control strategy to resolve this conflict, where each stage consists of several iterations. The learning gain remains constant in each stage but is reduced at the transition from a given stage to the subsequent stage. The switching iteration between two stages is determined by the tracking performance index of the contracted input error and the accumulated noise drift. Furthermore, an improved mechanism is proposed to optimize the lengths of the different stages. The asymptotic convergence of the input sequence generated by the newly proposed strategy is strictly established by thoroughly analyzing the properties of the proposed gain sequence. Numerical simulations are presented to verify the theoretical results.

It is challenging and critical to achieve zero error consensus tracking in multiagent systems (MASs) with nonautonomous leaders (i.e., leaders with nonzero inputs). The traditional approach is to use discontinuous controllers which may cause a chattering phenomenon. How to achieve zero error consensus tracking via a chattering-free controller is still open. We propose a class of adaptive continuous controllers to achieve zero error consensus tracking for Lipschitz nonlinear MASs with a nonautonomous leader and directed communication topology. Unlike existing works that use discontinuous functions to eliminate the impacts of leaders’ inputs, we use a continuous function by introducing an exponential decay function into the denominator. First, we design a continuous controller with fixed coupling strengths and prove that zero error consensus tracking can be achieved if the coupling strengths are greater than some positive constants. Second, we design a continuous controller with dynamic coupling strengths under which fully distributed zero error consensus tracking can be achieved. Moreover, the case with undirected communication topology is studied. Finally, three examples are given to verify the theoretical results. Specifically, convergence results between the continuous controller here and that is developed via the boundary layer technique are compared. Compared with existing works, the designed adaptive continuous controllers here can not only achieve zero error consensus tracking but also is chattering free.

The digitization of process manufacturing involves converting information and knowledge into a digital format through technologies, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. This transformation promotes extension and optimization within the industrial, supply, and value chains, aiming to enhance decision-making efficiency, enable agile operations, and ensure information security and privacy. However, the current learning and operational approaches in the process industry remain rooted in traditional informatization, falling short of the vision for digital transformation. To address this gap, it is crucial to implement fusion analysis, deepen understanding, adopt autonomous learning, and enable intelligent optimization based on life-cycle data. Therefore, it is of fundamental importance to realize the transformation of process manufacturing toward digitalization and intelligentization, i.e., the use of artificial intelligence with decision-making capability, via new learning theories, methods, and algorithms.

With the increased uptake of renewable energy resources (RES), power generation from Photovoltaics (PV) cells is gaining momentum and heavily depends on environmental and meteorological conditions. Predicting PV power generation plays an important role in power management and dispatch optimization. Due to the scales of PV systems, the generation may vary significantly, which is difficult to have accurate PV power generation prediction over different categories of customers (e.g., residential, agricultural, industrial, and commercial) using a single model. In this paper, we define the PV power generation prediction as a multitasking prediction problem, where PV generation over each of the categories is modeled as a separate prediction task. To address this problem, we employ a recurrent neural network (RNN) as the predictor for each prediction task and propose a multitasking RNN (MT-RNN) framework. Instead of addressing each task individually, MT-RNN performs knowledge transfer among different tasks to improve the prediction accuracy of each task, where the knowledge is represented via connection weights and biases in each RNN. In comparison to several state-of-art deep neural network (DNN) models that solve each task individually, the superior performance of MT-RNN in terms of prediction accuracy is demonstrated.

In this paper, a Model Predictive Control (MPC) for community Battery Energy Storage Systems (BESS) is proposed to mitigate the Electric Vehicle (EV) charging demand while maintaining voltage regulation in residential power grids. With the increased penetration of renewable-based Distributed Energy Resources (DERs) and the EV uptake, distribution grids are highly subject to voltage fluctuations due to back power feed from DERs or significant EV charging demand. Conventionally, Distribution Network Operators (DNOs) have addressed this issue by adjusting the taps of transformers. However, managing the naturally unpredictable renewable generation and the power consumption behaviour of technology owners requires advanced real-time algorithms. In this paper, we apply a real-time control algorithm, based on Model Predictive Control (MPC), to a community BESS in order to reduce voltage fluctuations. The MPC algorithm performs in real-time based on readings from smart meters of households. It predicts the future values of voltages at households based on the current and past readings of smart meters, and calculates the BESS charging or discharging rate such that future voltage deviations are minimised. Simulations on the real-world power consumption data from Victorian residential consumers, in Australia, show that the proposed controller performs significantly better than the fixed charging/discharging BESS schedule in both voltage regulation and BESS degradation.

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.

This article addresses the dynamic task allocation problem with limited communication and velocity. The main challenge lie in the selection of $k$ fittest winner participants and the participant contention that one winner participant may be selected by multiple targets simultaneously. Existing methods take the distance between the targets and participants as the evaluation index to select winners, which may lead to futile selection since the winner participant locating at the opposite direction of the target cannot intercept the target with limited velocity. By carefully considering both the distance between the targets and participants and the motion direction of the targets, an improved evaluation index for each target is proposed and employed such that the futile selection can be avoided in the executing process of the algorithm. Moreover, an extra evaluation index for each winner participant is presented to select one winner target to overcome the participant contention. Based on these, the control protocols are developed for targets interception, and their stability is proven by the Lyapunov theory under some suitable conditions. Finally, simulation examples are presented to illustrate the effectiveness and advantages of the proposed algorithms.

A finite-time discontinuous control method is proposed for controlling nonholonomic chained-form systems. Through a general discontinuous coordinate transformation, the nonholonomic system is transformed into two decoupled subsystems that are independently stabilized with control laws. Sufficient conditions for the existence of decoupled subsystems and finite-time convergence of system states are given. Furthermore, the finite-time discontinuous control laws are proposed to ensure that the system states reach the equilibrium in a finite time. Simulation results are presented to demonstrate the effectiveness and advantages of the proposed approach.

Model-free predictive current control (MFPCC) has been widely used in voltage source inverters (VSIs). The output current performance of look-up-table-based MFPCC is affected by the number of the applied voltage vectors (VVs), the current gradient updating and the sampling disturbance. To improve the current performance, a novel modulated MFPCC is proposed for VSI, while real-time current gradient updating and sampling disturbance suppression can be achieved. First, three basic VVs are picked, whose duration time are computed according to the current gradients. Second, an advanced current gradient updating method is presented by establishing the equations of different current gradients under different VVs. Furthermore, the sampling disturbance effect on MFPCC is analyzed and an extended state observer is designed to suppress such effect. Simulation model and experimental platform of the VSI system are structured to verify the preponderance of the proposed MFPCC in reducing the total harmonic distortion of output current.

In this article, we propose fixed-time gradient dynamics with time-varying coefficients for continuous-time optimization. We first investigate the Lyapunov stability conditions that allow us to achieve fixed-time stability of the time-varying dynamical systems. We then use them to deal with continuous-time optimization problems. We show that under the proposed fixed-time gradient dynamics and by choosing time-varying coefficients, the searching trajectories converge to their optima in fixed-time from any initial points with a very fast rate. Simulation results are given to show the effectiveness of the proposed fixed-time gradient dynamics with tunable time-varying coefficients for continuous-time optimization.

For state-dependent cooperation-competition networks, the output bipartite consensus problem of heterogeneous uncertain agents is studied in this article. In our framework, all the agents are described by second-order continuous-time nonlinear systems with different intrinsic dynamics, and the agents' uncertainties are characterized by unknown parameters in the intrinsic term. Then, the edge evolution rules with hysteresis coefficients are proposed via the classification strategy. To solve this problem, the distributed Lyapunov-based redesign method with the potential function term is first applied to such a second-order heterogeneous uncertain multiagent system. Through the three-step correlation design, the explicit expressions of the distributed controllers and the unknown parameter estimators are obtained, and it is shown that the state-dependent cooperation-competition network is always connected and maintains structural balance for all time if an initial structurally balanced and connected network topology is provided. On this basis, it follows from the total Lyapunov function that output bipartite consensus can be achieved asymptotically for the heterogeneous uncertain multiagent system. Finally, a numerical simulation is provided to validate the structural balance of state-dependent networks and output bipartite consensus of heterogeneous uncertain agents.

This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It is therefore of great significance to investigate how to ensure the precise consensus tracking to a given reference leader using heavily contaminated information. To this end, a novel distributed learning consensus scheme is proposed, which consists of a classic distributed control structure, a preliminary correction mechanism, and a separated design of learning gain and regulation matrix. The influence of biased and unbiased randomness is discussed in detail according to the convergence rate and consensus performance. The iterationwise asymptotic consensus tracking is strictly established for linear MAS first to demonstrate the inherent principles for the effectiveness of the proposed scheme. Then, the results are extended to nonlinear systems with nonidentical initialization condition and diverse gain design. The obtained results show that the distributed learning consensus scheme can achieve high-precision tracking performance for an MAS under unreliable communications. The theoretical results are verified by two illustrative simulations.

The P-type update law has been the mainstream technique used in iterative learning control (ILC) systems, which resembles linear feedback control with asymptotical convergence. In recent years, finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback. In this paper, we show that such mechanism can equally ramp up the learning speed in ILC systems. We first propose a fractional power update rule for ILC of single-input-single-output linear systems. A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process. Using the nonlinear mapping approach, fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven. The limit cycles are shown to be tunable to determine the steady states. Numerical simulations are provided to verify the theoretical results.

This article studies the finite-time (FT) convergence of a fast primal–dual gradient dynamics (PDGD), called FT-PDGD, for solving constrained optimization with general constraints and cost functions. Based on the nonsmooth analysis and augmented Lagrangian function, sufficient conditions are established for FT-PDGD to enable the realization of primal–dual optimization in FT. A specific class of nonsmooth sign-preserving functions is defined and analyzed for ensuring FT stability. Particularly, the matrix of linear equations is not required to have a full-row rank and the cost function is not necessary to be strictly convex. By introducing auxiliary variables for general linear inequality constraints, reduced sufficient conditions are further derived for the optimization with linear equality and inequality constraints after transformation. In addition, by the nonsmooth analysis, the switching dynamics evolved in both primal and dual variables are carefully investigated and the upper bound on the convergence time is explicitly provided. Moreover, as applications of FT-PDGD, several FT convergent distributed algorithms are designed to solve distributed optimization with separated and coupled linear equations, respectively. Finally, two case studies are conducted to show the performance of the proposed algorithms.

This article studies the point-to-point (P2P) learning and tracking problem for networked stochastic systems with fading communications by iterative learning control. The P2P tracking problem indicates that only partial positions rather than the whole reference are required to achieve high tracking precision. An auxiliary matrix is introduced to connect the entire reference and the required tracking targets. The fading communication introduces multiplicative randomness to the transmitted signals, which leads to the biased available information. A direct correction mechanism is employed using statistics of the communication channel. A learning control scheme is then proposed with a decreasing gain sequence to ensure steady convergence in the presence of various types of randomness. Two scenarios of varying initial states are considered. The convergence of the proposed scheme is strictly established. The validity of the proposed algorithm is verified by two simulation examples.

The work proposes a generalized supertwisting algorithm (GSTA) and its constructive design strategy. In contrast with the conventional STA, the most remarkable characteristic of the proposed method is that the discontinuous term in the conventional STA is replaced with a fractional power term, which can fundamentally improve the performance of the conventional STA. It is shown that if the fractional power in the nonsmooth term becomes −1/2, the GSTA will reduce to the conventional STA. Under the GSTA, it will be rigorously verified by taking advantage of strict Lyapunov analysis that the sliding variables can finite-time converge to an arbitrarily small region in a neighborhood of the origin by tuning the gains and the fractional power. Finally, simulation studies are provided to demonstrate the superiority of the theoretically obtained results.

This article proposes an adaptive second-order sliding mode (ASOSM) controller design by means of the Lyapunov method. The notable feature of the proposed algorithm is that it only needs boundedness of the uncertainties, whereas boundedness of the derivatives of uncertainties is not demanded. Under the proposed ASOSM control scheme, the gain can be dynamically tuned, which avoids gain overestimation. The finite-time stability of the closed-loop ASOSM dynamics is proved via the Lyapunov theory. Finally, the simulation results are shown to validate the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article investigates the second-order consensus issue for multiagent systems subject to both limited communication resources and replay attacks. First, an asynchronous dynamic edge event-triggered (DEET) communication scheme is developed to reduce the utilization of network resources in the absence of attacks. Then, we further consider the case of replay attacks launched by multiple adversaries, under which the transmitted information is maliciously replaced by a previous unnecessary message. To overcome the impact caused by replay attacks, a modified DEET scheme and an effective attack-resilient consensus protocol are well constructed, both of which successfully guarantee second-order consensus in the presence of replay attacks. In addition, internal dynamic variables are utilized in the proposed DEET schemes such that the triggering time sequence does not exhibit Zeno behavior. Finally, one numerical example is provided to illustrate our theoretical analysis. [ABSTRACT FROM AUTHOR]

A novel sliding mode control design method is developed to address the robust stabilization problem for uncertain time-varying networked control systems subject to state delay, which utilizes auxiliary matrices to avoid the difficulties of finding quadratic Lyapunov functions and solving the linear matrix inequalities. By embedding the original system into a higher dimensional system, simplified piecewise nonnegative functions can be used to analyze the system. Stability criteria are obtained which can be satisfied and verified easily. An optimization problem is defined based on these criteria, to obtain the sliding mode control parameters to ensure the exponential stability of the closed-loop system. [ABSTRACT FROM AUTHOR]

This article works on the consensus problem of nonlinear multiagent systems (MASs) under directed graphs. Based on the local output information of neighboring agents, fully distributed adaptive attack-free protocols are designed, where speaking of attack-free protocol, we mean that the observer information transmission via communication channel is forbidden during the whole course. First, the fixed-time observer is introduced to estimate both the local state and the consensus error based on the local output and the relative output measurement among neighboring agents. Then, an observer-based protocol is generated by the consensus error estimation, where the adaptive gains are designed to estimate the unknown neural network constant weight matrix and the upper bound of the residual error vector. Furthermore, the fully distributed adaptive attack-free consensus protocol is proposed by introducing an extra adaptive gain to estimate the communication connectivity information. The proposed protocols are in essence attack-free since no observer information exchange among agents is undertaken during the whole process. Moreover, such a design structure takes the advantage of releasing communication burden. [ABSTRACT FROM AUTHOR]

Current resilient consensus algorithms deal with multiagent networks with lower order nodal dynamics where each agent excludes certain extreme values from its neighbors. This does not scale well in general networks. In this article, a new type of resilient consensus algorithm based on distributed attack isolation (DAI-RC) is proposed for general higher order networks. In the DAI-RC algorithm, the evolution of each normal agent can avoid the influence from those neighbors which are isolated as victims of attack. To characterize a feasible communication topology to isolate attacked agents, a notion of graph isolability is proposed based on which a sufficient condition to isolate the attacked agents is presented. Simulations are finally provided to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With fast developments in communication technologies, a large number of practical systems adopt the networked control structure. For this structure, the fading problem is an emerging issue among other network problems. It has not been extensively investigated how to guarantee superior control performance in the presence of unknown fading channels. This article presents a learning strategy for gradually improving the tracking performance. To this end, an iterative estimation mechanism is first introduced to provide necessary statistical information such that the biased signals after transmission can be corrected before being utilized. Then, learning control algorithms incorporating with a decreasing step-size sequence are designed for both output and input fading cases. The convergence in both mean-square and almost-sure senses of the proposed schemes is strictly proved under mild conditions. Illustrative simulations verify the effectiveness of the entire learning framework. [ABSTRACT FROM AUTHOR]

This paper studies price-based residential demand response management (PB-RDRM) in smart grids, in which non-dispatchable and dispatchable loads (including general loads and plug-in electric vehicles (PEVs)) are both involved. The PB-RDRM is composed of a bi-level optimization problem, in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company (UC) by selecting optimal retail prices (RPs), while the lower-level demand response (DR) problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior. The challenges here are mainly two-fold: 1) the uncertainty of energy consumption and RPs; 2) the flexible PEVs' temporally coupled constraints, which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM. To address these challenges, we first model the dynamic retail pricing problem as a Markovian decision process (MDP), and then employ a model-free reinforcement learning (RL) algorithm to learn the optimal dynamic RPs of UC according to the loads' responses. Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches (i.e., distributed dual decomposition-based (DDB) method and distributed primal-dual interior (PDI)-based method), which require exact load and electricity price models. The comparison results show that, compared with the benchmark solutions, our proposed algorithm can not only adaptively decide the RPs through on-line learning processes, but also achieve larger social welfare within an unknown electricity market environment.

This article proposes a unified Lyapunov framework for analyzing the stochastic asymptotic and finite-time convergence/stability for Itô stochastic nonlinear systems. By exploring the coupling effect between the drift and the diffusion parts of the system, novel almost sure convergence/stability criteria are established. For the finite-time case, the stability criteria not only capture the stabilizing effect of stochastic noise but also include the existing finite-time stability criteria as special cases. For the asymptotic case, it removes the local Lipschitz conditions and the nonzero property of the solution demanded by the existing results. The proposed theoretical results are further applied to solve the sliding mode control and the optimal finite-time/asymptotic stabilization problems. [ABSTRACT FROM AUTHOR]

This article investigates the economic dispatch problem in a smart grid with the event-triggered framework under uncertain communication networks. Unlike most of the existing works on economic dispatch, where the distributed consensus-based algorithms are conducted continuously or periodically, the event-triggered mechanism is employed for saving communication resources. For uncertainties, whose upper bounds may be even more significant than nominal weights, we design a general framework for the distributed event-triggered algorithm and an adaptive law for adjusting the coupling weights. The upper bounds of the communication uncertainties are not required in the design, and the adaptive tuning of coupling weights is proved to be able to compensate for the adverse effects caused by uncertainties in the consensus-seeking phase. Noteworthy, the design of event-triggered parameters is independent of the communication topology, which makes the proposed algorithm fully distributed. We also prove that the Zeno triggering of the event-triggered algorithms does not exist. Finally, a case study with the communication network represented by the random multiradius geographical graph is conducted to verify the effectiveness of the derived algorithms.

In this article, we deal with the consensus problem of multiple partial-state coupled linear systems, which are neutrally stable. These systems communicate over dynamic undirected networks, which change continuously and can be disconnected at any time. We develop an analysis framework from uniform complete observability theory to work out the necessary and sufficient conditions for exponential consensus. It turns out that, with a suitably designed feedback matrix, exponential consensus can be realized globally and uniformly if and only if a jointly $(\delta,T)$ -connected condition and an observability condition relying only on system and input matrices are satisfied. A lower bound of the convergence rate is also provided. We figure out the proof by applying matrix analysis and linear functional analysis. A simulation example is presented to illustrate our theoretical findings. [ABSTRACT FROM AUTHOR]

A recursive continuous higher order nonsingular terminal sliding-mode (TSM) controller is proposed in this article for nonlinear systems. A new integral TSM manifold is constructed in a recursive manner by modifying the tool of adding a power of integrator instead of exploring nonrecursive design directly. A super-twisting like reaching law is designed to achieve continuous control action without sacrificing disturbance rejection specification as that in boundary-layer approaches. By the new Lyapunov-based design, the proposed control method admits the following new features: first, rather than imposing some existence condition for nonrecursive design, the proposed method admits the certainty for chosen fractional power to guarantee the finite-time stability of the closed-loop system; second, an explicit Lyapunov function approach is proposed to establish finite-time stability of the closed-loop system; and, third, the proposed method is shown to be tunable to exhibit desired transient performance and control energy restriction. [ABSTRACT FROM AUTHOR]

This article aims to solve the nonsmooth resource allocation problem in the presence of a global network resource constraint and local set constraints in the framework of multiagent system optimization. It is assumed that multiagent systems are subject to some external disturbances, and the control inputs of the agents satisfy Lispchitz continuity. These two distinguished features render the existing distributed optimization algorithms, especially the subgradient-based algorithms inapplicable due to the employment of discontinuity of subgradients. To solve such a challenging resource allocation problem, a new kind of continuous-time proximal algorithm is designed with the aid of convex optimization theory and the internal-model technique. The proximal algorithm is further augmented by introducing an event-based communication scheme such that the continuous-time communication among the agents is avoided successfully. The theoretical analysis shows that the multiagent systems under the proposed algorithms can converge to the optimal solution of the considered problem, while the external disturbances are rejected. Besides, the Zeno behavior can be excluded for the proximal algorithm with event-based communication. Finally, the numerical simulations are given to verify the established theoretical results.

In this study, the stability of a hierarchical network with delayed output is discussed by applying a kind of optimal periodic control. To reduce the number of the nodes of the original hierarchical network, an aggregation algorithm is first presented to take some nodes with the same information as an aggregated node. Furthermore, the stability of the original hierarchical network can be guaranteed by the optimal periodic control of the aggregated hierarchical network. Then, an optimal control scheme is proposed to reduce the bandwidth waste in information transmission. In the control scheme, the time sequence is separated into two parts: the deterministic segment and the dynamic segment. With the optimal control scheme, two targets are achieved: 1) the outputs of the original and aggregated hierarchical system are both asymptotically stable and 2) the nodes with slow convergent rate can catch up with the convergence speeds of other nodes. [ABSTRACT FROM AUTHOR]

A novel data-driven learning control scheme is proposed for unknown systems with unknown fading sensor channels. The fading randomness is modeled by multiplicative and additive random variables subject to certain unknown distributions. In this scheme, we propose an error transmission mode and an iterative gradient estimation method. Unlike the conventional transmission mode where the output is directly transmitted back to the controller, in the error transmission mode, we send the desired reference to the plant such that tracking errors can be calculated locally and then transmitted back through the fading channel. Using the faded tracking error data only, the gradient for updating input is iteratively estimated by a random difference technique along the iteration axis. This gradient acts as the updating term of the control signal; therefore, information on the system and the fading channel is no longer required. The proposed scheme is proved effective in tracking the desired reference under random fading communication environments. Theoretical results are verified by simulations. [ABSTRACT FROM AUTHOR]

With the wide use of networks in repetitive systems, channels between a plant and a controller may experience random fading, which is a common problem in long-distance wireless communication. However, the control problem over fading channels is far from resolved. In this article, we investigate learning control over fading channels to gradually improve tracking performance. We observe that the effect of fading on input transmission greatly compromises tracking ability in practical implementations. We examine three average techniques: moving average, general average with all historical information, and forgetting-based average. The results reveal a tradeoff between learning ability and tracking ability for learning control algorithms, where learning ability refers to the convergence rate of a proposed learning algorithm, and tracking ability refers to the final tracking precision of the output to the desired reference. The convergence results for the three schemes with these averaging techniques are strictly proved. The results demonstrate that the forgetting-based average operator-based scheme can connect the other two schemes by tuning the forgetting factor. We also provide extensions of several general scenarios to expand the application range. Illustrative simulations verify the theoretical results. [ABSTRACT FROM AUTHOR]

In this article, we study the Euler's discretization effect on a sliding-mode control system with a supertwisting algorithm (STA). We start from the undisturbed STA and show that the trajectory eventually converges to an exactly bounded region around the origin. Periodic behaviors are explored. Furthermore, the discretization effect on the disturbed STA is established, and the stability analysis is given. Simulation results demonstrate the effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]

While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance. [ABSTRACT FROM AUTHOR]