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Energy storage systems (ESSs)-based demand response (DR) is an appealing way to save electricity bills for consumers under demand charge and time-of-use (TOU) price. In order to counteract the high investment cost of ESS, a novel operator-enabled ESS sharing scheme, namely, the “operator-as-a-consumer (OaaC),” is proposed and investigated in this article. In this scheme, the users and the operator form a Stackelberg game. The users send ESS orders to the operator and apply their own ESS dispatching strategies for their own purposes. Meanwhile, the operator maximizes its profit through optimal ESS sizing and scheduling, as well as pricing for the users’ ESS orders. The feasibility and economic performance of OaaC are further analyzed by solving a bilevel joint optimization problem of ESS pricing, sizing, and scheduling. To make the analysis tractable, the bilevel model is first transformed into its single-level mathematical program with equilibrium constraints (MPEC) formulation and is then linearized into a mixed-integer linear programming (MILP) problem using multiple linearization methods. Case studies with actual data are utilized to demonstrate the profitability for the operator and simultaneously the ability of bill saving for the users under the proposed OaaC scheme.

This article is concerned with the challenge of guaranteeing output constraints for fault-tolerant control (FTC) of a class of unknown multi-input single-output (MISO) nonlinear systems in the presence of actuator faults. Most industrial systems are equipped with redundant actuators and a fault detection-isolation mechanism for accommodating unexpected actuator faults. To simplify the system design and reduce the risk of false alarm or missed detection brought by the detection unit, a learning-based switching function scheme is proposed to automatically activate different sets of actuators in a rotational manner without human intervention. By this means, no explicit fault detection mechanism is needed. An additional step has been made to guarantee that the system output remains in user-defined time-varying asymmetric output constraints all the time during the occurrence of failures by utilizing error transformation techniques. The stability of the transformed system can equivalently deliver the result that the original system output stays in the required bounds. Hence, system crash or further catastrophic outcomes can be avoided. A neural network is integrated to embody the adaptive FTC design for dealing with unknown system dynamics. The dynamic surface control (DSC) technique is also invoked to decrease complexity. Furthermore, the stability analysis is carried out by the standard Lyapunov approach to guarantee that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results are provided to verify the effectiveness of the proposed scheme.

Soft sensor techniques have been applied to predict the hard-to-measure quality variables based on the easy-to-measure process variables in industry scenarios. Since the products are usually produced with prearranged processing orders, the sequential dependence among different variables can be important for the process modeling. To use this property, a dual attention-based encoder–decoder is developed in this article, which presents a customized sequence-to-sequence learning for soft sensor. We reveal that different quality variables in the same process are sequentially dependent on each other and the process variables are natural time sequences. Hence, the encoder–decoder is constructed to explicitly exploit the sequential information of both the input, that is, the process variables, and the output, that is, the quality variables. The encoder and decoder modules are specified as the long short-term memory network. In addition, since different process variables and time points impose different effects on the quality variables, a dual attention mechanism is embedded into the encoder–decoder to concurrently search the quality-related process variables and time points for a fine-grained quality prediction. Comprehensive experiments are performed based on a real cigarette production process and a benchmark multiphase flow process, which illustrate the effectiveness of the proposed encoder–decoder and its sequence to sequence learning for soft sensor.

Industrial robots are becoming increasingly vulnerable to cyber incidents and attacks, particularly with the dawn of the Industrial Internet-of-Things (IIoT). To gain a comprehensive understanding of these cyber risks, vulnerabilities of industrial robots were analyzed empirically, using more than three million communication packets collected with testbeds of two ABB IRB120 robots and five other robots from various Original Equipment Manufacturers (OEMs). This analysis, guided by the confidentiality–integrity–availability (CIA) triad, uncovers robot vulnerabilities in three dimensions: confidentiality, integrity, and availability. These vulnerabilities were used to design Covering Robot Manipulation via Data Deception (CORMAND2), an automated cyber–physical attack against industrial robots. CORMAND2 manipulates robot operation while deceiving the Supervisory Control and Data Acquisition (SCADA) system that the robot is operating normally by modifying the robot's movement data and data deception. CORMAND2 and its capability of degrading the manufacturing was validated experimentally using the aforementioned seven robots from six different OEMs. CORMAND2 unveils the limitations of existing anomaly detection systems, more specifically the assumption of the authenticity of SCADA-received movement data, to which we propose mitigations for.

The dust distribution law acting at the top of a blast furnace &#x0028 BF &#x0029 is of great significance for understanding gas flow distribution and mitigating the negative influence of dust particles on the accuracy and service life of detection equipment. The harsh environment inside a BF makes it difficult to describe the dust distribution. This paper addresses this problem by proposing a dust distribution k-S&#x03B5 -Up model based on interphase &#x0028 gas-powder &#x0029 coupling. The proposed model is coupled with a k-S&#x03B5 model &#x0028 which describes gas flow movement &#x0029 and a Up model &#x0028 which depicts dust movement &#x0029 . First, the kinetic energy equation and turbulent dissipation rate equation in the k-S&#x03B5 model are established based on the modeling theory and single-Green-function two-scale direct interaction approximation &#x0028 SGF-TSDIA &#x0029 theory. Second, a dust particle movement Up model is built based on a force analysis of the dust and Newton&#x02BC s laws of motion. Finally, a coupling factor that describes the interphase interaction is proposed, and the k-S&#x03B5 -Up model, with clear physical meaning, rigorous mathematical logic, and adequate generality, is developed. Simulation results and on-site verification show that the k-S&#x03B5 -Up model not only has high precision, but also reveals the aggregate distribution features of the dust, which are helpful in optimizing the installation position of the detection equipment and improving its accuracy and service life.

This paper deals with the synchronization control problem in the leader–follower format of a class of high-order nonaffine nonlinear multiagent systems under a directed communication protocol. A novel adaptive neural distributed synchronization scheme with guaranteed performance is proposed. The main contribution lies in the fact that both nonaffine agent dynamics, which basically makes most existing agent dynamics as special cases, and guaranteed synchronization performance are taken into account. The difficulty lies mainly in the nonaffine terms and coupling terms due to the interactions of agents. To overcome this challenge, an augmented quadratic Lyapunov function by incorporating the lower bounds of control gains is proposed. The problems resulting from the nonaffine dynamics and the coupling terms among agents are solved by incorporating the special property of radial basis function neural network into the derivative of the augmented quadratic Lyapunov function. The unknown nonaffine terms are addressed by using an indirected neural network approach. A nonlinear mapping is built to relate the local consensus error to a new one, which is subsequently stabilized via Lyapunov synthesis. As a result, the proposed approach can ensure the outputs of all follower agents to track the outputs of the leader, while the synchronization performance bounds can be quantified on both transient and steady-state stages. All other signals in the closed loop are ensured to be semiglobally, uniformly, and ultimately bounded. Finally, the effectiveness of the proposed controller is verified through a heterogeneous four-agent example.

Given the complexity and isolation of the blast furnace (BF), field engineers generally operate the system upon their former experience and the operating manual. Harsh environment and equipment shortage have made the testing of silicon content a prevailing method for the detection of temperature within BF. As the silicon content is a comprehensive performance of internal thermal state, knowing the exact value in advance can be very helpful for operators to keep the furnace temperature at a reasonable extent. Thus, an improved gated recurrent unit recurrent neural network (GRU-RNN) is proposed to predict the silicon content of hot metal, indicating a competitive performance at 92.4% hit rate among several deep learning methods.

The coaxial-rotor micro-aerial vehicles (CRMAVs) have been proven to be a powerful tool in forming small and agile manned-unmanned hybrid applications. However, the operation of them is usually subject to unpredictable time-varying aerodynamic disturbances and model uncertainties. In this paper, an adaptive robust controller based on a neural network (NN) approach is proposed to reject such perturbations and track both the desired position and orientation trajectories. A complete dynamic model of a CRMAV is first constructed. When all system states are assumed to be available, an NN-based state-feedback controller is proposed through feedback linearization and Lyapunov analysis. Furthermore, to overcome the practical challenge that certain states are not measurable, a high-gain observer is introduced to estimate the unavailable states, and then, an output-feedback controller is developed. Rigorous theoretical analysis verifies the stability of the entire closed-loop system. In addition, extensive simulation studies are conducted to validate the feasibility of the proposed scheme.

For the issues of the discontinuous measurement of the material level of the mechanical stock rod and the measurement of radar probe with weak anti-disturbance ability, high accuracy fluctuations and poor stability, combining with the semi-fuzzy and clustering characteristics of the radar gauge rod data, a new semi-fuzzy clustering algorithm based on membership degree classification is first proposed to achieve clustering analysis of radar gauge rod data. Then, to obtain the accurate material level information, a sliding window correction model based on mechanical stock rod data is built to correct the radar probe data, which can effectively get the continuous real time information of the material level in the blast furnace. Both the simulation results and industrial validation show that the proposed method can provide real-time and effective material level information, and it has a practical value for industrial production.

We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information.

In Part I of this paper, we formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. We propose there a centralized solution based on second-order cone programming relaxation of optimal power flow and generalized Benders decomposition that is applicable when global information is available. In this paper, we propose two distributed solutions based on the alternating direction method of multipliers and dual decomposition, respectively, that are suitable for systems where the distribution grid, stations, and EVs are managed by separate entities. Our algorithms allow these entities to make individual decisions, but coordinate through privacy-preserving information exchanges to solve a convex relaxation of the global problem. We present simulation results to show that both algorithms converge quickly to a solution that is close to optimum after discretization.

In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

Predict the remaining useful life of devices in real time is important to maintenance management. This article addresses a remaining useful life predictive problem where the considered device suffers from non-stationary degradation with shocks. A novel degradation-shock system model subjected to a hidden degradation state is proposed to characterize the degradation process. To estimate the hidden system state, the Kalman filter is modified with optimal estimation. Then, based on Bayes’ theorem, a recursive algorithm is presented to estimate the unknown parameters of the model. With the proposed approach, the hidden state and unknown parameters can be updated at each sampling instant. Subsequently, the analytical solution for the remaining useful life is obtained, while the characteristics of the shocks as well as the uncertainties of the estimated system state are taken into account. Furthermore, the effectiveness of the proposed model is verified by a numerical simulation and a practical case study on a milling machine.

By integrating sensing and computing capabilities into the traditional radio frequency identification (RFID) tags, the wireless identification and sensing platform (WISP) opens up a new research area referred to as wireless rechargeable sensor networks (WRSNs). Since WISPs need to be fully charged and then can start communication with the reader, thus the energy charging and transmission collision of WISPs are different from those of RFID tags. If the reader power is large, WISPs will be charged fast and start data transmission almost at the same time, which results in heavy transmission collision and the communication delay would be extremely large. However, if the reader power is small, WISPs will be charged slow and start data transmission one by one even without collisionh between any two WISPs, but the charging period of the whole network would be extremely large. Therefore, this paper aims at determining an optimal reader power towards balanced energy charging and transmission collision, such that the total duration for all WISPs to be fully charged and completely communicated is minimized. Firstly, we investigate on dynamic reader power with transmission collision avoidance, and static reader power towards balanced energy charging and transmission collision. Then, we derive the optimal reader power in three cases: (i) One reader and ray-uniformly distributed WISPs; (ii) one reader and randomly distributed WISPs; and (iii) multiple readers and randomly distributed WISPs. Finally, the theoretical analysis is verified through extensive evaluations.

Understanding how human mobility pattern changes during the COVID-19 is of great importance in controlling the transmission of the pandemic. This pattern seems unpredictable due to the complex social contexts, individual behaviors, and limited data. We analyze the human mobility data of over 10 million smart devices in three major cities in China from January 2020 to March 2021. We find that the human mobility across multi-waves of epidemics presents a surprisingly similar pattern in these three cities, despite their significant gaps in geographic environments and epidemic intensities. In particular, we reveal that the COVID-19 policies and statistics (i.e., confirmed cases) dominate human mobility during the pandemic. Thus, we propose a universal conditional generative adversarial network based framework to estimate human mobility, integrating COVID-19 statistics and policies via a gating fusion module. Extensive numerical experiments demonstrate that our model is generalizable for estimating human mobility dynamics accurately across three cities with multi-waves of COVID-19. Beyond, our model also allows policymakers to better evaluate the potential influences of various policies on human mobility and mitigate the unprecedented and disruptive pandemic.

This paper studies the tracking control of general linear multi-agent systems with or without time delays. The observer-based event-triggered control schemes will be considered. For the conventional distributed tracking protocol, the authors will not update the relative state in continuous time, i.e., the relative state will be updated by some events which happened in discrete time. A completely decentralized event-trigger will be designed for leader-follower systems. It is shown that all followers can track the leader asymptotically by the proposed protocol, if the feedback gain matrices are designed appropriately. Numerical simulations are also provided and the results show highly consistent with the theoretical results.

This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced. The NN in the control law learns the system dynamics in an online manner, while the NN residual reconstruction errors and the bounded disturbances are overcome by the error sign signal. Since both of the NN output and the error sign signal are included in the integral, the continuity of the control input is ensured. The controller structure and the NN weight update law are novel in contrast with the previous effort, and the semiglobal asymptotic tracking performance is still guaranteed by using the Lyapunov analysis. In addition, the NN weights and all other signals are proved to be bounded simultaneously. The proposed approach also relaxes the need for the upper bounds of certain terms, which are usually required in the previous designs. Finally, the theoretical results are substantiated with simulations.

This paper formulates a multi-period optimal station assignment problem for electric vehicle (EV) battery swapping that takes into account both temporal and spatial couplings. The goal is to reduce the total EV cost and station congestion due to temporary shortage in supply of available batteries. We show that the problem is reducible to the minimum weight perfect bipartite matching problem. This leads to an efficient solution based on the Hungarian algorithm. Numerical results suggest that the proposed solution provides a significant improvement over a greedy heuristic that assigns EVs to nearest stations.

This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the original nonaffine nonlinear system into an equivalent affine one. Then, to deal with the unknown dynamics and unknown control coefficient contained in the affine system, online approximator and Nussbaum gain techniques are utilized in the controller design. It is proven rigorously that asymptotic convergence of the tracking error and ultimate uniform boundedness of all the other signals can be guaranteed by the proposed control method. The control feasibility is further verified by numerical simulations.

In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is sufficient to solve the output constraint problem. It is shown that output tracking is achieved without violation of the output constraint. More specifically, we can shape the system performance arbitrarily on transient and steady-state stages with the output evolving in predefined time-varying boundaries all the time. A single neural network, whose weights are tuned online, is used in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control coefficient matrix is avoided without assumption on the prior knowledge of control input’s bound. All the signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded via Lyapunov synthesis. Finally, the merits of the proposed controller are verified in the simulation environment.

In this paper, asymmetric Gaussian weighting functions are introduced for the identification of linear parameter varying systems by utilizing an input–output multi-model structure. It is not required to select operating points with uniform spacing and more flexibility is achieved. To verify the effectiveness of the proposed approach, several weighting functions, including linear, Gaussian and asymmetric Gaussian weighting functions, are evaluated and compared. It is demonstrated through simulations with a continuous stirred tank reactor model that the proposed approach provides more satisfactory approximation.

Backup path is an important mechanism to sustain the reliability of a multi-state network. As a popular backup path method, the double minimal path based backup path algorithm can improve the multi-state network’s reliability when the main paths fail. However, this algorithm cannot work efficiently when a single main minimal path fails. To improve the reliability in the first main minimal path failure case, we propose a single minimal path based backup path algorithm. In the single minimal path based backup path algorithm, two disjoint minimal paths are used as the main routing pair to transmit the data, and one single minimal path, which is disjoint with the main minimal paths, acts as the backup path. In the second main minimal path failure case, we propose a double–single minimal path based backup path algorithm to improve the multi-state network reliability. To develop the single minimal path based backup path and the double–single minimal path based backup path algorithms, this article first formulates the multi-state network reliability analysis problem. Then, a solution procedure is proposed to calculate the multi-state network reliability. Furthermore, numerical examples are given to validate the effectiveness of the algorithms. Finally, some comparisons are made between the single minimal path based backup path/double–single minimal path based backup path and the double minimal path based backup path/double minimal path based backup path algorithms. The comparison results indicate that the single minimal path based backup path and the double–single minimal path based backup path algorithms lead to considerable improvement in terms of the multi-state network reliability in the first and the second main minimal path failure cases, respectively, which are verified by both the mathematical analysis and numerical experiments.

Wireless sensor and actuator networks (WSANs) facilitate close interactions between human and the environment. Efficient coordination among the sensors and actuators plays a vital role in carrying out sensing and acting in WSANs. In this paper, we develop a collaborative estimation and actuation mechanism, which consists of a sensor-actuator coordination phase and an actuator-actuator coordination phase. The first phase is based on distributed Kalman filter in federated configuration, which is able to provide reliable and precise sensing data. On this basis, the second phase allocates proper tasks based on system requirements and coordinates actuators to accomplish the tasks. Particularly, the actuator-actuator coordination is formulated as an optimization problem and an effective method is proposed to search the solution based on sequential unconstrained minimization technique. Simulation results demonstrate the effectiveness of our proposed mechanism.

In present computer networks, cyber attacks, such as compromising and energy depleting attacks, can cause malfunction or even failure of nodes, and can be significantly harmful to the convergence property of the average consensus algorithms. In this paper, we aim to distributively find the most critical nodes in the sense that removing it causes the largest destruction to the converging speed of an average consensus algorithm among all the nodes. The algebraic connectivity is used to assess the destruction and thus the importance of a node. We design two methods to estimate the algebraic connectivity and analyze their bounds for the estimation error. Based on this, we propose a fully distributed algorithm for the nodes to iteratively find the most critical one. Simulation results demonstrate the effectiveness of our algorithm.

In this brief, a continuous tracking control law is proposed for a class of high-order multi-input-multi-output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix. The proposed controller consists of high-gain feedback, Nussbaum gain matrix selector, online approximator (OLA) model and a robust term. The OLA model is represented by a two-layer neural network. The continuousness of the control signal is guaranteed to relax the requirement for the actuator bandwidth and avoid the incurred chattering effect. Asymptotic tracking performance is achieved theoretically by standard Lyapunov analysis. The control feasibility is also verified in simulation environment.