Conventional ultra-wideband device-based localization schemes predominantly depend on static anchors, which restrict localization services to predefined areas. This paper presents a novel localization scheme employing mobile ultra-wideband anchors, significantly extending the scope of localization services. These mobile anchors have the capability to auto-calibrate in real time, relative to each other, while simultaneously delivering localization services to the target. We delve into the effects of disparities in tag heights and variations in anchor formation shapes on localization accuracy. To maintain the target within the high-quality localization service range, we devise formation control algorithms for the mobile anchors. These algorithms ensure the anchors attain the desired formation and preclude mutual collisions, underpinned by robust mathematical proofs. Both simulations and experiments corroborate the theoretical conclusions and the localization scheme proposed in this paper.

Balancing the size of vehicle fleet and the quality of transportation service is important yet challenging for urban transportation systems. This paper studies the optimal fleet selection problem in dial-a-ride regime. To achieve this, a two-stage mixed-integer programming formulation is presented. It is demonstrated that there exists an optimal vehicle set provided that the considered dial-a-ride problem is weakly solvable. The strong solvability problem is also discussed by introducing a discount factor to strike a trade-off between the fleet size and routing cost associated. When the probability measure of the vehicle set is unavailable, the Wasserstein measure, anchored by an empirical distribution derived from training data, is used for approximation. It is revealed that a nontrivial duality gap exists, leading to an infeasibility of the Lagrangian argument employed in the distributionally robust framework that transforms an infinite stochastic program into a finite alternative. By employing a robust primal decomposition method, it is shown that the relaxed version of the considered program is equivalent to a finite-dimensional quadratic conic program. Subsequently, a set of disjunctions is constructed to extract the optimizer with a finite number of iterations. Furthermore, the “distance” between the empirical and true vehicle sets is discussed as well. Finally, the benchmark data is carried out to illustrate the theoretical justification and significance of the proposed method.

Multi-task robotic bimanual manipulation is becoming increasingly popular as it enables sophisticated tasks that require diverse dual-arm collaboration patterns. Compared to unimanual manipulation, bimanual tasks pose challenges to understanding the multi-body spatiotemporal dynamics. An existing method ManiGaussian [30] pioneers encoding the spatiotemporal dynamics into the visual representation via Gaussian world model for single-arm settings, which ignores the interaction of multiple embodiments for dual-arm systems with significant performance drop. In this paper, we propose ManiGaussian++, an extension of ManiGaussian framework that improves multi-task bimanual manipulation by digesting multi-body scene dynamics through a hierarchical Gaussian world model. To be specific, we first generate task-oriented Gaussian Splatting from intermediate visual features, which aims to differentiate acting and stabilizing arms for multi-body spatiotemporal dynamics modeling. We then build a hierarchical Gaussian world model with the leader-follower architecture, where the multi-body spatiotemporal dynamics is mined for intermediate visual representation via future scene prediction. The leader predicts Gaussian Splatting deformation caused by motions of the stabilizing arm, through which the follower generates the physical consequences resulted from the movement of the acting arm. As a result, our method significantly outperforms the current state-of-the-art bimanual manipulation techniques by an improvement of 20.2% in 10 simulated tasks, and achieves 60% success rate on average in 9 challenging real-world tasks. Our code is available at https://github.com/April-Yz/ManiGaussian_Bimanual.

This paper presents a distributed observer-based non-linear control framework to address a prescribed-time output formation tracking problem of heterogeneous nonlinear networks under a nonlinear leader system and a digraph with a spanning tree, wherein dynamics and dimensions of leader-follower systems can be different. To solve this issue, prescribed-time nonlinear distributed observers are firstly developed to estimate the nonlinear leader’s state, and the prescribed-time distributed control law is further presented. Under this framework, the zero-error output formation tracking is obtained within a pre-specified and user-defined time by leveraging the time transformation approach. The proposed algorithm is free of global knowledge, and its convergence time is irrelevant to initial conditions, control parameters or network structures. Simulation and experimental results are given to verify the designs’ effectiveness. The main features of developed designs lie in that: (1) prescribed-time output formation tracking of heterogeneous nonlinear systems can be achieved under directed graphs, wherein both dynamics and dimensions of leader-follower nonlinear systems can be different; (2) the convergence time is pre-specified and thus, irrelevant to any initial conditions, control parameters, or network structures; (3) the proposed algorithm that does not depend on any global information, is distributed under a general communication graph with a directed spanning tree; and (4) the prescribed-time output formation tracking solution can be extended beyond the terminal time.

In this paper, we consider an electricity market with a batch of energy prosumers (EPs), who can trade energy with the day-ahead market (DAM) and energy balancing market (EBM). To coordinate the behavior of EPs, an energy aggregator (EA) is established, who is responsible for making aggregation strategies for the EPs to improve the benefit of the whole EP community. Specifically, two pricing schemes are provided by the EA: pay-as-you-go (PAYG) scheme and lump sum (LS) scheme, which can be flexibly selected by EPs based on their own preferences. By considering the dual-pricing principle of the EBM, a stochastic Stackelberg game between the EPs and EA is formulated and a two-level optimization algorithm is introduced based on the proposed feasible region partitioning (FRP) method. The performance of the algorithm is demonstrated with a two-settlement market model in the simulation.Note to Practitioners—This work aims to optimize the social cost of an EP community in a two-settlement electricity market. In contrast to the existing works, the main innovations and the resulting challenges lie in both the market modelling and the theoretical approach. In particular, different from the existing uniform-pricing schemes, this work explores the realization of mixed-pricing scheme, which can introduce more customized factors into the decision-making process of EPs. In addition, to adapt to a wider range of practical EBMs, the dual-pricing principle of the balancing energy is considered, which leads to a stochastic Stackelberg game where the social cost function is nondeterministic with respect to the strategy of EA. To address the aforementioned challenges, a two-level optimization algorithm is introduced based on the proposed FRP method, which enriches the existing methods for Stackelberg games. The proposed market model can be more capable of addressing some practical issues in the sense that the mixed-pricing scheme can adapt to the different preferences of market participants and the self-centric instinct of EPs is considered based on Nash games. In addition, the dual-pricing principle of the EBM is motivated by many real electricity markets and can be more general than the single-pricing counterpart.

This paper concerns a distributed control strategy to deal with possible actuator outages of multi-agent systems (MASs). With the designed strategy, the control process is monitored by a fault detection system (FDS). Once the actuator of one agent in use partially or completely fails, the FDS will detect the fault timely and a controller activator will produce its effect. By this means, the healthy actuator can replace the faulty one for control at the delicately designed instants, and the prescribed performance of the MAS can always be satisfied in the presence of possible actuator outages with the help of prescribed performance functions. Moreover, to achieve more diversified constraints to meet wider requirements in practice, the proposed strategy is further extended to combine with a time-varying barrier Lyapunov function so that the time-varying output constraint can be achieved, which makes the designed fault-tolerant control more flexible and sensitive in dealing with failures. With the proposed distributed control method, uncertain actuator failures, especially the actuator outage, can be addressed in the presence of disturbances and uncertain dynamics. Since the failure is allowed to occur multiple times with only one actuator operating for control at any time, the designed approach can be more economical in energy saving compared with the existing methods. Numerical simulations are provided for multiple agents to verify the effectiveness of the proposed algorithm. Note to Practitioners—In this paper, a control strategy is proposed to handle the actuator failure of a class of MASs. To deal with possible actuator outages, which pose a threat to system safety, actuator redundancy is introduced in the proposed strategy. By developing a controller activator to specify the activation time of healthy actuators with the help of a designed FDS, the prescribed performance of the agents can always be guaranteed and the tracking error can be constrained within specified time-varying function curves as expected. Since the proposed strategy enables the successive triggering of actuators, only one actuator works at any instant, which contributes to its energy-saving efficiency. Since the proposed strategy can accommodate possible actuator outages in an economical way with guaranteed prescribed performance, it can be used in various industrial scenarios where high safety of agents is required, for instance, multiple aircrafts, multiple high-speed trains, and multiple industrial robot systems.

In this paper, we study the package delivery problem where the transportation tasks, including arriving at the delivery locations and returning to the depots, are required to be completed within a time window. To this end, a time-homogeneous preset-time algorithm is presented to coordinate multiple vehicles within an identical time window, where arrival time delays are used to characterize the arrival time of the vehicles. Consequently, the considered package delivery problem is formulated by two phases: “Active phase” and “Sleeping phase”, over which vehicles use the neighboring information to achieve coordination. For the proposed algorithm, we use Lyapunov based method to derive condition guaranteeing the coordination of vehicles, under a very mild communication topology condition. As an extension, a time-heterogeneous preset-time algorithm is designed. We demonstrate that coordination among vehicles is determined by the maximal preset-time. Built upon this observation, we further show that there is an equivalence between the proposed formulation and the dynamical equation of the interacting vehicles. Finally, a numerical example for a package delivery problem, comprised of two coordination phases, is carried out to illustrate the proposed preset-time coordination formulation, and the time-heterogeneous case is further discussed as well.

This paper advances a strategic paradigm for sensor power scheduling within Cyber- Physical Systems (CPSs) under adversarial scenarios, leveraging a Markov Stackelberg game combined with a Signal-to-Interference-plus-Noise Ratio (SINR) framework. A novel approach, which introduces reinforcement learning for the adaptive optimization of power distribution, is proposed to ascertain energy efficient sensor transmission tactics in the presence of jamming. Empirical simulations are given to validate the superiority of the proposed algorithm, underscoring its effectiveness in diminishing estimation inaccuracies and alleviating the adversarial influence.

This article addresses finite-time connectivity-preserving rendezvous problems of networked uncertain Euler-Lagrange systems, where two types of time-varying leaders are investigated, and only a subset of followers can have access to the leader’s trajectory. The distributed estimation and control architecture is then established to solve this problem with an emphasis on the settling-time estimation. In particular, in the first layer, the finite-time distributed estimators are developed to estimate and reconstruct the states of both linear and nonlinear leaders, respectively. In the second layer, distributed controllers are designed for consensus tracking in a finite-time using estimated leader information. Further, to account for limited sensing ranges, another distributed algorithm is given via an artificial potential field to guarantee finite-time rendezvous. Numerical simulation results are given to validate the effectiveness of the proposed designs.

This article addresses a resilient exponential distributed convex optimization problem for a heterogeneous linear multiagent system under Denial-of-Service (DoS) attacks over random digraphs. The random digraphs are caused by unreliable networks and the DoS attacks, allowed to occur aperiodically, refer to an interruption of the communication channels carried out by the intelligent adversaries. In contrast to many existing distributed convex optimization works over a perfect communication network, the global optimal solution might not be sought under the adverse influences that result in performance degradations or even failures of optimization algorithms. The aforementioned setting poses certain technical challenges to optimization algorithm design and exponential convergence analysis. In this work, several resilient algorithms are presented such that a team of agents minimizes a sum of local nonquadratic cost functions in a safe and reliable manner with the global exponential convergence. Numerical simulation results are further presented to validate the effectiveness of the proposed distributed approaches.

This article is concerned with a fault-tolerant formation tracking problem of nonlinear systems under unknown faults, where the leader's states are only accessible to a small set of followers via a directed graph. Under these faults, not only the amplitudes but also the signs of control coefficients become time-varying and unknown. The current setting will enhance the investigated problem's practical relevance and at the same time, it poses nontrivial design challenges of distributed control algorithms and convergence analysis. To solve this problem, a novel distributed control algorithm is developed by incorporating an estimation-based control framework together with a Nussbaum gain approach to guarantee an asymptotic cooperative formation tracking of nonlinear networked systems under unknown and dynamic actuator faults. Moreover, the proposed control framework is extended to ensure an asymptotic task-space coordination of multiple manipulators under unknown actuator faults, kinematics, and dynamics. Lastly, numerical simulation results are provided to validate the effectiveness of the proposed distributed designs.

We investigate a distributed time-varying formation control problem for an uncertain Euler-Lagrange system. A time-varying optimization-based approach is proposed. Based on this approach, the robots can achieve the expected formation configuration and meanwhile optimize a global objective function using only neighboring and local information. We consider the time-varying optimization where the objective functions can change in real time. In this case, the consensus-based formation tracking control issues and formation containment tracking control issues in the literature can be solved by the proposed approach. By a penalty-based method, the robots' states asymptotically converge to the estimated optimal solution to an equivalent time-varying optimization problem, whose optimal solution can achieve simultaneous formation and optimization. Furthermore, we consider two more general scenarios: 1) the local objective functions can have non-neighbor's information and 2) the optimization problems can have inequality constraints.

This article studies the distributed dimensionality reduction fusion estimation problem with communication delays for a class of cyber-physical systems (CPSs). The raw measurements are preprocessed in each sink node to obtain the local optimal estimate (LOE) of a CPS, and the compressed LOE under dimensionality reduction encounters with communication delays during the transmission. Under this case, a mathematical model with compensation strategy is proposed to characterize the dimensionality reduction and communication delays. This model also has the property of reducing the information loss caused by the dimensionality reduction and delays. Based on this model, a recursive distributed Kalman fusion estimator (DKFE) is derived by optimal weighted fusion criterion in the linear minimum variance sense. A stability condition for the DKFE, which can be easily verified by the exiting software, is derived. In addition, this condition can guarantee that the estimation error covariance matrix of the DKFE converges to the unique steady-state matrix for any initial values and, thus, the steady-state DKFE (SDKFE) is given. Note that the computational complexity of the SDKFE is much lower than that of the DKFE. Moreover, a probability selection criterion for determining the dimensionality reduction strategy is also presented to guarantee the stability of the DKFE. Two illustrative examples are given to show the advantage and effectiveness of the proposed methods.

This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations. The S-curve acceleration and deceleration scheme is utilized to adjust the elongation rate of the arm. A kind of novel observer is put forward to tackle unknown extraneous disturbances. Auxiliary systems and barrier Lyapunov functions are introduced to meet the asymmetric input-output constraints. With the help of Lyapunov's theory, the global exponential stability and uniform boundedness are analyzed. The numerical simulations are finally provided to illuminate its availability of the designed control schemes.

In this work, we study the generalized Nash equilibrium (GNE, see Definition 1) seeking problem for monotone generalized noncooperative games with set constraints and shared affine inequality constraints. A novel projected gradient-based regularized penalized dynamical system is proposed to solve this issue. The idea is to use a differentiable penalty function with a time-varying penalty parameter to deal with the inequality constraints. A time-varying regularization term is used to deal with the ill-poseness caused by the monotonicity assumption and the time-varying penalty term. The proposed dynamical system extends the regularized dynamical system in the literature to the projected gradient-based regularized penalized dynamical system, which can be used to solve generalized noncooperative games with set constraints and coupled constraints. Furthermore, we propose a distributed algorithm by using leader-following consensus, where the players have access to neighboring information only. For both cases, the asymptotic convergence to the least-norm variational equilibrium of the game is proven. Numerical examples show the effectiveness and efficiency of the proposed algorithms.

This article studies distributed optimization algorithms for heterogeneous multiagent systems under an undirected and connected communication graph. Two types of heterogeneities are discussed. First, we consider a class of multiagent systems composed of both continuous-time dynamic agents and discrete-time dynamic agents. The agents coordinate with each other to minimize a global objective function that is the sum of their local convex objective functions. A distributed subgradient method is proposed for each agent in the network. It is proved that driven by the proposed updating law, the agents' position states converge to an optimal solution of the optimization problem, provided that the subgradients of the objective functions are bounded, the step size is not summable but square summable, and the sampling period is bounded by some constant. Second, we consider a class of multiagent systems composed of both first-order dynamic agents and second-order dynamic agents. It is proved that the agents' position states converge to the unique optimal solution if the objective functions are strongly convex, continuously differentiable, and the gradients are globally Lipschitz. Numerical examples are given to verify the conclusions.

This paper addresses task-space adaptive coordinated tracking of networked manipulators with an inner/outer-loop closed control architecture, considering all the uncertain kinematics, dynamics, disturbances, and unavailable task-space velocities. One observation is that modern robotic applications may encounter situations that task-space controllers cannot be implemented on robots with the closed control architecture. In addition, it assumes that the combination of the inner/outer loop is stable and effects of unknown robotic dynamics are neglected. Existing papers in [9]-[12] provide task-space synchronization with an open control architecture and require each robot to fully access a desired global task, and to communicate via undirected or strongly connected digraphs. In contrast, this paper proposes a distributed framework over a directed graph so that a robust, distributed, outer-loop control scheme is developed to achieve task-space coordination with dynamic effects being considered and not modifying the inner control loop. Numerical simulations are presented to show the effectiveness of the design.

Due to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) has been made possible with real-time multiple sensor measurements. However, due to inevitable sensor errors or communication failures, the raw data are usually incomplete with corrupted values, lost values, or undetected missing values. In practice, the incomplete data are usually dealt with by directly excluding incomplete measurements and abnormal spikes. In addition, some preprocessing methods, which naively impute data though averaging or smoothing, have also been widely applied. In this article, we address the building FDD problem with incomplete data by proposing a new approach, the adjacent information recovery (AIR) filter. The AIR filter is utilized to deal with the FDD for a typical air handling unit (AHU) system with incomplete data based on the ASHRAE Research Project 1312. Experimental results show that the proposed method improves FDD performance by recovering missing sensor measurements and outperforms the state-of-the-art methods.

The control of heating, ventilation, and air-conditioning (HVAC) systems has raised extensive attention due to their high energy consumption cost and operation patterns far from being energy-efficient. However, most of the existing methods suffer limitations in scalability and computational efficiency for large buildings due to the centralized structures. To compensate for such defects, this article studies the scalable control of multizone HVAC systems with the target to reduce energy cost while maintaining thermal comfort. In particular, the thermal couplings due to heat transfer among the adjacent zones are incorporated, which has been ignored or not well studied due to complexity in the literature. To overcome the computational challenges of the nonlinear and nonconvex problem caused by the complex system dynamics, this article proposes a decentralized approach composed of three main steps: 1) relaxing the bilinear system dynamics; 2) solving the relaxed problem in a decentralized manner using the accelerated distributed augmented Lagrangian (ADAL) method; and 3) recovering the recursive feasibility of the solution. Through a comparison with the centralized method, the suboptimality of this approach is demonstrated. In addition, the superior performance of this approach is illustrated through a comparison with the distributed token-based scheduling strategy (DTBSS). The numerical results imply that for buildings with a relatively small number of zones (less than 20), the two methods are competitive. However, for larger cases, the proposed approach performs better with a considerable reduction both in energy cost and computation time.

This paper develops a fully distributed framework to investigate the cooperative behavior of multiagent systems in the presence of distributed denial-of-service (DoS) attacks launched by multiple adversaries. In such an insecure network environment, two kinds of communication schemes, that is, sample-data and event-triggered communication schemes, are discussed. Then, a fully distributed control protocol with strong robustness and high scalability is well designed. This protocol guarantees asymptotic consensus against distributed DoS attacks. In this paper, "fully" emphasizes that the eigenvalue information of the Laplacian matrix is not required in the design of both the control protocol and event conditions. For the event-triggered case, two effective dynamical event-triggered schemes are proposed, which are independent of any global information. Such event-triggered schemes do not exhibit Zeno behavior even in the insecure environment. Finally, a simulation example is provided to verify the effectiveness of theoretical analysis.

This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.

Thanks to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) is getting more and more popular with rich data. In the building FDD field, mature supervised learning algorithms and strategies have been applied to detect and diagnose known faults. However, it is out of the question to collect labeled training data for every possible fault. Thus, there is a necessity to study FDD when the training data for some faults are unavailable. To the authors' best knowledge, few works have reported how to identify “unseen faults.” In this paper, authors propose a novel expert knowledge-based unseen fault identification (EK-UFI) method to identify unseen faults by employing the similarities between known faults and unknown faults. The similarity is captured by incorporating essential expert knowledge that is encoded in the fault gene matrix. The fault gene is integrated with a latent incorporation matrix that transfers knowledge from known faults to unseen faults. With application to a real system, the proposed method is proven to be effective in identifying various building unknown faults with a high accuracy. Note to Practitioners- FDD is of great importance for saving energy and improving occupancy comfort levels and building safety levels. Identifying unseen faults in real application is challenging since: 1) building faults are complicated and confusing while well-labeled fault data is rare; 2) experimental fault data collected in laboratory test beds cannot be directly used as judgment criteria for real buildings; and 3) it is impossible to measure every possible fault ahead of time. Although supervised learning methods have been successfully applied in existing works to solve the building FDD, they could not attack the UFI problem. In this paper, a novel EK-UFI method is proposed to identify unseen faults by employing the similarities between known faults and unknown faults. Experimental results show that the proposed method is essential.

Time-varying formation tracking (TVFT) control problems for a team of unmanned aerial vehicles (UAVs) with switching and directed interaction topologies are investigated, where the follower UAVs realize a given time-varying formation while tracking the leader UAV. A TVFT control protocol is firstly constructed utilizing local neighboring information, where the information of the leader UAV is only available to partial followers and the neighborhood can be switching. An algorithm composed of four steps is provided to design the TVFT protocol. It is proved that the UAV swarm system can realize the TVFT using the designed protocol if the dwell time for the switching directed topologies is larger than a fixed threshold and the TVFT feasibility condition is satisfied. Based on the ultrawideband positioning technology, a quadrotor UAV formation control platform with four quadrotor UAVs is given. The obtained theoretical results are applied to solve the target enclosing problems of the UAV swarm systems. A flying experiment for three follower quadrotor UAVs to enclose a leader quadrotor UAV is carried out to verify the effectiveness of the presented results.

This paper studies a multi-agent formation control problem under a leader-follower framework, where the agents are governed by double-integrator dynamics and the objective is to achieve a formation with desired shape and a specified size. Firstly, a distributed control algorithm is developed for the leaders to achieve a desired distance. Then, by combining the control law for the leaders and a relative position estimation based control law for the followers, an estimation based formation control algorithm is developed for the entire multi-agent system to asymptotically achieve a formation with desired shape and a specified size in the case without initial relative position estimation errors and approximately attain the desired formation in the case with initial relative position initial estimation errors. Simulation results are provided to validate the proposed algorithm.

The electricity price forecasting (EPF) is essential for decision-making mechanisms of market participants to survive in the deregulated and competing commercial environment. Due to special features of the electricity such as seasonality, the constant balance between production and consumption required by the system, and environmental dependencies, electricity prices generally shows extreme volatility and price spikes with the heteroscedasticity. This paper provides a survey of main EPF methodologies and the ultimate goal of this survey is to provide readers insights and guidelines for choosing different EPF techniques for day-ahead electricity markets. For each type of method, we briefly introduce its principle and then describe how it is applied in EPF. Many new EPF techniques developed recently are also discussed, especially the artificial intelligence forecasting methods. The pros and cons of each type of method are provided in a final table so that users can pay attention to when choosing them. In the final section, several promising methods and potential directions for further exploration are presented.

In this paper, an efficient method for the day-ahead electricity price forecasting (EPF) is proposed based on a long-short term memory (LSTM) recurrent neural network model. LSTM network has been widely used in various applications such as natural language processing and time series analysis. It is capable of learning features and long term dependencies of the historical information on the current predictions for sequential data. We propose to use LSTM model to forecast the day-ahead electricity price for Australian market at Victoria (VIC) region and Singapore market. Instead of using only historical prices as inputs to the model, we also consider exogenous variables, such as holidays, day of the week, hour of the day, weather conditions, oil prices and historical price/demand, etc. The output is the electricity price for the next hour. The future 24 hours of prices are forecasted in a recursive manner. The mean absolute percentage error (MAPE) of four weeks for each season in VIC and Singapore markets are examined. The effectiveness of the proposed method is verified using real market data from both markets. The result shows that the LSTM network outperforms four popular forecasting methods and provides up to 47.3% improvement in the average daily MAPE for the VIC market.

State estimation plays an essential role in the monitoring and supervision of cyber-physical systems (CPSs), and its importance has made the security and estimation performance a major concern. In this case, multisensor information fusion estimation (MIFE) provides an attractive alternative to study secure estimation problems because MIFE can potentially improve estimation accuracy and enhance reliability and robustness against attacks. From the perspective of the defender, the secure distributed Kalman fusion estimation problem is investigated in this paper for a class of CPSs under replay attacks, where each local estimate obtained by the sink node is transmitted to a remote fusion center through bandwidth constrained communication channels. A new mathematical model with compensation strategy is proposed to characterize the replay attacks and bandwidth constrains, and then a recursive distributed Kalman fusion estimator (DKFE) is designed in the linear minimum variance sense. According to different communication frameworks, two classes of data compression and compensation algorithms are developed such that the DKFEs can achieve the desired performance. Several attack-dependent and bandwidth-dependent conditions are derived such that the DKFEs are secure under replay attacks. An illustrative example is given to demonstrate the effectiveness of the proposed methods.

This paper investigates distributed Nash equilibrium seeking in multiagent games under switching communication topologies. To be specific, the communication topology is supposed to be switching among a set of strongly connected digraphs, which might suffer from occasional loss of communication due to sensor failure, packet loss, etc. The synthesis of the leader-following consensus protocol and the gradient play is exploited to achieve the distributed Nash equilibrium seeking under the switching communication topologies. Switching topology without loss of communication is firstly considered, followed by switching topology subject to missing communication within some time slots. For both situations, nonquadratic and quadratic games are addressed separately. Local convergence results are presented for nonquadratic games and nonlocal convergence results are provided for quadratic games. The theoretical results are verified by numerical examples.

This paper studies a distributed secure consensus tracking control problem for multiagent systems subject to strategic cyber attacks modeled by a random Markov process. A hybrid stochastic secure control framework is established for designing a distributed secure control law such that mean-square exponential consensus tracking is achieved. A connectivity restoration mechanism is considered and the properties on attack frequency and attack length rate are investigated, respectively. Based on the solutions of an algebraic Riccati equation and an algebraic Riccati inequality, a procedure to select the control gains is provided and stability analysis is studied by using Lyapunov's method.. The effect of strategic attacks on discrete-time systems is also investigated. Finally, numerical examples are provided to illustrate the effectiveness of theoretical analysis.

In this paper, an aggregate game is adopted for the modeling and analysis of energy consumption control in smart grid. Since the electricity users' cost functions depend on the aggregate energy consumption, which is unknown to the end users, an average consensus protocol is employed to estimate it. By neighboring communication among the users about their estimations on the aggregate energy consumption, Nash seeking strategies are developed. Convergence properties are explored for the proposed Nash seeking strategies. For energy consumption game that may have multiple isolated Nash equilibria, a local convergence result is derived. The convergence is established by utilizing singular perturbation analysis and Lyapunov stability analysis. Energy consumption control for a network of heating, ventilation, and air conditioning systems is investigated. Based on the uniqueness of the Nash equilibrium, it is shown that the players' actions converge to a neighborhood of the unique Nash equilibrium nonlocally. More specially, if the unique Nash equilibrium is an inner Nash equilibrium, an exponential convergence result is obtained. Energy consumption game with stubborn players is studied. In this case, the actions of the rational players can be driven to a neighborhood of their best response strategies by using the proposed method. Numerical examples are presented to verify the effectiveness of the proposed methods.

Experiments show that operation efficiency and reliability of buildings can greatly benefit from rich and relevant datasets. More specifically, data can be analyzed to detect and diagnose system and component failures that undermine energy efficiency. Among the huge quantity of information, some features are more correlated with the failures than others. However, there has been little research to date focusing on determining the types of data that can optimally support fault detection and diagnosis (FDD). This paper presents a novel optimal feature selection method, named information greedy feature filter (IGFF), to select essential features that benefit building FDD. On one hand, the selection results can serve as reference for configuring sensors in the data collection stage, especially when the measurement resource is limited. On the other hand, with the most informative features selected by the IGFF, the performance of building FDD could be improved and theoretically justified. A case study on air-handling unit (AHU) is conducted based on the dataset of the ASHRAE Research Project 1312. Numerical results show that, compared with several baselines, the FDD performances of conventional classification methods are greatly enhanced by the IGFF.

This paper studies the distributed robust fusion estimation problem with stochastic and deterministic parameter uncertainties, where the covariance of the Gaussian white noise is unknown, and the covariances of the random variables in the stochastic uncertainties are in a bounded set. By using the discrete-time stochastic bounded real lemma and the matrix analysis approach, each local robust estimator is derived to guarantee an optimal estimation performance for admissible uncertainties, and then necessary and sufficient condition for the distributed robust fusion estimator is presented to obtain an optimal weighting fusion criterion. Note that the local robust estimation problem and the distributed robust fusion estimation problem are both converted into convex optimization problems, which can be easily solved by standard software packages. The advantage and effectiveness of the proposed methods are demonstrated through state monitoring for target tracking system and stirred rank reactor system.

This paper addresses a distributed fault diagnosis and fault-tolerant control problem for a multi-agent system modeling a large-scale power generator network. The goal is to enable all the agents to achieve the control objective of asymptotic stability without losing the system tracking performance. Before solving this DFTC problem, a distributed fault detection (DFD) method is provided for fault detection. Next, the designs focus on a DFTC scheme without estimating the upper bound of the coupled, nonlinear, state-dependent unknown input. A model-based distributed state estimator (DSE) together with a proportional-integral-like nonlinear distributed identifier (DI) is then developed to identify the unknown input. By exploiting the redundancies from the estimated states and unknown input information obtained from the DSE and DI, a novel continuous DFTC is designed to enable the agents to achieve asymptotic consensus tracking without losing the system tracking performance while achieving distributed unknown input identification. A power system example and numerical simulations are provided to illustrate the effectiveness of the proposed DFTC method.

In this paper, the task-space cooperative tracking control problem of networked robotic manipulators without task-space velocity measurements is addressed. To overcome the problem without task-space velocity measurements, a novel task-space position observer is designed to update the estimated task-space position and to simultaneously provide the estimated task-space velocity, based on which an adaptive cooperative tracking controller without task-space velocity measurements is presented by introducing new estimated task-space reference velocity and acceleration. Furthermore, adaptive laws are provided to cope with uncertain kinematics and dynamics and rigorous stability analysis is given to show asymptotical convergence of the task-space tracking and synchronization errors in the presence of communication delays under strongly connected directed graphs. Simulation results are given to demonstrate the performance of the proposed approach.

We solve N -player potential games with dynamical constraint in this paper. Potential games with stable dynamics are first considered followed by one type of potential games without inherently stable dynamics. Different from most of the existing Nash seeking methods, we provide an extremum seeking-based method that does not require explicit information on the game dynamics or the payoff functions. Only measurements of the payoff functions are needed in the game strategy synthesis. Lie bracket approximation is used for the analysis of the proposed Nash seeking scheme. A singularly semi-globally practically uniformly asymptotically stable result is presented for potential games with stable dynamics and an ultimately bounded result is provided for potential games without inherently stable dynamics. For first-order perturbed integrator-type dynamics, we employ an extended-state observer to deal with the disturbance such that better convergence is achievable. Stability of the closed-loop system is proven and the ultimate bound is quantified. Numerical examples are presented to verify the effectiveness of the proposed methods.

For the battery energy storage system (BESS) consisting of multiple battery packages, package-level state-of-charge (SOC) balancing can provide safety redundancy in protecting battery packages from overcharging or overdischarging, and maintain the maximum power capacity of the overall BESS. In this paper, a distributed control scheme is proposed for package-level SOC balancing of a grid-connected BESS. The proposed control scheme comprises three parts. First, each battery package shares its SOC with its neighboring battery packages over the communication network. Based on the SOCs of its own and its neighbors, each battery package is equipped with an energy coordinator. Second, for each battery package, a distributed observer is employed to estimate the average desired power output, which is used to determine the local reference power output together with the energy coordinator. Third, local current controller is synthesized for each battery package to achieve local reference power output tracking. Comprehensive case studies are conducted to evaluate the effectiveness of the proposed control scheme.

A compound control strategy is proposed for frequency regulation of source-grid-load systems in which power sources, power grids, and loads are all participating in the process. Here, power sources are conventional thermal generators, including new energy power generations, and loads are composed of energy storage units (ESUs) and grid-friendly appliances (GFAs). The proposed control scheme includes two levels of operations, with the upper level to be a model predictive control (MPC) for generators and the lower level to be a distributed leader-following consensus control strategy for multiple ESUs. For new energy power generations, the power outputs are restricted on a constant value during a sampling period based on a predicted generating curve. GFAs respond to the system frequency by regulating their active power consumption. Simulations on a single power system and three interconnected area power systems are provided to verify the effectiveness of the proposed compound control strategy.

This paper proposes a cooperative demand response (CDR) scheme for load management in smart grid. The CDR scheme is formulated as a constrained optimization problem that generates a Pareto-optimal response strategy profile for consumers. Comparing with the noncooperative response strategy (i.e., Nash equilibrium) obtained from the one-shot demand management game, the Pareto-optimal response strategy reduces the electricity costs to the consumers. We further develop an incentive-compatible trigger-and-punishment mechanism to avoid the noncooperative behaviors of the selfish consumers. Furthermore, the CDR scheme is applied to achieve load management of industrial refrigerated warehouses. To implement the CDR scheme in large-scale systems, we group the refrigerated warehouses into clusters and utilize the CDR scheme within each cluster. Numerical results demonstrate that the CDR scheme can reduce the electricity costs, drop the electricity prices, and curtail the total energy consumption in comparison with the noncooperative demand response scheme.

The papers in this special section focus on technological and system developments in designing power grids. The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. Now, traditional grids are facing kinds of challenges, and the world is proposing to modernize legacy and make strides toward smart grid. It is widely recognized that demand response is the core feature of smart grid, which can be formally defined as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. With the support of the advanced information and communication technologies, demand response is able to improve the efficiency, reliability, economics, and sustainability of power generation, distribution, and utilization.

This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M -matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topology-dependent multiple Lyapunov functions is proposed for analyzing the synchronization behavior of the whole network. It is theoretically shown that the global pinning synchronization in switched complex networks can be ensured if some nodes are appropriately pinned and the coupling is carefully selected. Interesting issues of how many and which nodes should be pinned for possibly realizing global synchronization are further addressed. Finally, some numerical simulations on coupled neural networks are provided to verify the theoretical results.

This paper proposes a cooperative demand response scheme for price-anticipating buildings in smart grid. The cooperative demand response scheme is formulated as a constrained social optimization problem. We develop a cooperative strategy and obtain a Pareto-optimal solution from the constrained social optimization problem. Comparing with the Nash equilibrium obtained from the one-stage demand management game, the Pareto-optimal solution reduces the electricity costs to all the building managers. We further align this Pareto-optimal solution with the subgame perfect Nash equilibrium of a repeated demand management game and develop an incentive-compatible trigger-and-punishment mechanism to avoid the noncooperative behavior of the building managers. Numerical results demonstrate that the cooperative demand response scheme can reduce the electricity costs, the electricity price, and the total energy consumption.

This paper presents a distributed formation control method for a group of robots. The global objective of achieving a desired formation is obtained by dividing it into a set of local objectives which are achieved in a distributed manner. A basic repetitive pattern in the desired formation is identified and a corresponding unique differentiable task function is defined based on the position coordinates of the robots forming the pattern. Neighbor selection rules are designed for the robots in such a way that each robot is part of one or more such patterns. A singularity-robust task-priority inverse kinematics method is used to design velocity controllers to achieve these patterns. Since a robot can receive multiple control actions being part of multiple task functions or patterns, a distributed null space behavioral (NSB) approach is designed to combine such multiple control actions in a prioritized way. A comprehensive stability analysis of the proposed approach based on Lyapunov methods is presented. Simulation results are provided to verify the effectiveness of the proposed approach.

This paper studies the passivity-based consensus analysis and the consensus synthesis problem (called passification) for a class of stochastic multi-agent systems subject to external disturbances. Based on Lyapunov methods, graph theory, and slack matrix methods such as the free-weighting matrix and Jensen's integral inequality, a new storage Lyapunov functional is proposed to derive delay-dependent sufficient conditions on mean-square exponential consensus and stochastic passivity for the stochastic multi-agent systems. By proposing passive time-varying stochastic consensus protocols, the solvability conditions for the passification problem are derived based on linearization techniques. A numerical example is provided to illustrate the effectiveness of the theoretical results.

This paper concerns the distributed containment problem of multi-agent systems with linear or linearized node dynamics under a directed communication topology. A new class of distributed control protocol based only on the relative outputs of neighboring agents is designed and utilized to achieve containment. Under the assumptions that each agent is stabilizable and detectable, and for each follower there exists at least one leader that has a directed path to that follower, it is theoretically proved that containment in the closed-loop multi-agent system can be guaranteed. A multi-step containment protocol design procedure is further provided. At last, the convergence rate of the containment in multi-agent systems is discussed. Finally, a simulation example is given to verify the effectiveness of the theoretical results.

This study introduces a novel visual servo controller that is designed to control the pose of the camera to keep multiple objects in the field of view (FOV) of a mobile camera. In contrast with other visual servo methods, the control objective is not formulated in terms of a goal pose or a goal image. Rather, a set of underdetermined task functions are developed to regulate the mean and variance of a set of image features. Regulating these task functions inhibits feature points from leaving the camera FOV. An additional task function is used to maintain a high level of motion perceptibility, which ensures that desired feature point velocities can be achieved. These task functions are mapped to camera velocity, which serves as the system input. A proof of stability is presented for tracking three or fewer targets. Experiments of tracking eight or more targets have verified the performance of the proposed method.

The control of dynamic systems that undergo an impact collision is both theoretically challenging and of practical importance. An appeal of studying systems that undergo an impact is that short-duration effects such as high stresses, rapid dissipation of energy, and fast acceleration and deceleration may be achieved from low-energy sources. However, colliding systems present a difficult control challenge because the equations of motion are different when the system suddenly transitions from a noncontact state to a contact state. In this paper, an adaptive nonlinear controller is designed to regulate the states of two dynamic systems that collide. The academic example of a planar robot colliding with an unactuated mass-spring system is used to represent a broader class of such systems. The control objective is defined as the desire to command a robot to collide with an unactuated system and regulate the mass to a desired compressed state while compensating for the unknown constant system parameters. Lyapunov-based methods are used to develop a continuous adaptive controller that yields asymptotic regulation of the mass and robot links. It is interesting to note that one controller is responsible for achieving the control objective when the robot is in free motion (i.e., decoupled from the mass-spring system), when the systems collide, and when the system dynamics are coupled.

Previous homography-based visual servo controllers have been developed using an error system that contains a singularity resulting from the representation of the rotation matrix. For some aerospace applications such as visual servo control of satellites or air vehicles, the singularity introduced by the rotation representation may be restrictive. To eliminate this singularity, a homography-based visual servo controller is developed in this paper based on an error system composed of the unit quaternion representation. The proposed adaptive controller regulates a camera to a desired position and orientation that is determined from a desired image. A quaternion-based Lyapunov function is developed to facilitate the control design and the stability analysis