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This paper considers the containment control problem for uncertain QUAV (Quadrotor Unmanned Aerial Vehicle) multiagents with time-varying payloads under a fixed topology graph, and a distributed adaptive containment control protocol with multiple variable constraints is proposed. Generally, the control framework is classified into two layers. In the first layer, the desired trajectories are determined for followers by the communication topology and initial values of leaders. For the second layer, the ith QUAV follower is required to track the desired trajectory by employing the information of itself and neighbors. Under the second layer, the system of the ith agent is decoupled into two subsystems: the translational subsystem and the rotational subsystem. For the translational subsystem, the distributed adaptive containment controller is designed via dynamic surface control method to track the desired position trajectory. With such method, the information requirement of ith agent for its neighbors can be reduced significantly. For the rotational subsystem, the adaptive tracking controller is constructed to track the desired attitudes derived from translational subsystem through commonly used attitudes extraction algorithms. In the end, the resulting closed-loop system is proved to be stable in the sense of uniformly ultimate boundness, and the effectiveness of the proposed approach is illustrated by numerical simulations. • The containment control of QUAVs is studied firstly with time-varying payload. • The convergence processes of multiagents are confined to reasonable ranges. • Considering the limits on actuators, the saturated input are studied. • Simulations are given to validate the effectiveness of the designed strategy. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate the transmission scheduling problem for wireless control systems (WCSs) with limited power resources. Different from the existing works, for a discrete-time linear process, we consider a more practical WCS, where a relay is introduced into the framework for remote transmission and control. To achieve the best control performance of the system, we propose a global optimal offline scheduling algorithm. Then, based on ACK-feedback framework, two different online scheduling schemes are further designed respectively under the given power resources. Theoretically, we prove the superiority of online schedule to the offline one under the same energy budget. Simulations are conducted to demonstrate and verify the effectiveness of the proposed algorithms. Highlights • A novel framework of relay-assisted wireless control systems is proposed. • An optimal offline schedule is given under power constraints. • Two kinds of online schedules are given to further improve the performance. [ABSTRACT FROM AUTHOR]

Abstract: A fast development of high speed railways is witnessed in recent years as well as the performance of its wireless communication systems, GSM-R. In GSM-R systems, a large percentage of call outage is induced by handoff process. Thus, handover is a critical issue of reliability performance in GSM-R systems. Most of existing investigation and practical designs for handover in GSM-R systems are mainly based on single antenna without considering the specificity of the train as hard handover. In this paper, we take full use of the length of the train and propose a novel scheme of seamless fast handover based on Dual-Antenna. In this scheme, two antennas are respectively installed at the head and the end of the train and the proper coordination of the two antennas turn it to a soft handover. In the interlacing area of two antenna cells, the head antenna handover in advance while the end antenna connects to the current cell until the front antenna connect the new cell reliably. In this way, we can achieve a seamless and fast soft handover. This paper introduces the details of the scheme and analyzes the feasibility. Simulation results show that the proposed scheme is more efficient than existing schemes in terms of handover delay and reliability. [Copyright &y& Elsevier]

Abstract: We consider a distributed joint random access and power control scheme for interference management in wireless ad hoc networks. To derive decentralized solutions that do not require any cooperation among the users, we formulate this problem as noncooperative joint random access and power control game, in which each user minimizes its average transmission cost with a given rate constraint. Using supermodular game theory, the existence and uniqueness of Nash equilibrium are established. Furthermore, we present an asynchronous distributed algorithm to compute the solution of the game based on myopic best response updates, which converges to Nash equilibrium globally. Finally, a link admission algorithm is carried out to guarantee the reliability of the active users. Performance evaluations via simulations show that the game-theoretical based cross-layer design achieves high performance in terms of energy consumption and network stability. [ABSTRACT FROM AUTHOR]

DC microgrids (MGs) have gained significant attention due to their ability to complement renewable energy generation. In order to improve the operational stability of DC MGs, a predefined-time-based secondary controller is proposed to restore voltage drops caused by droop control and achieve current allocation among agents according to expected ratios. To conserve communication resources, the communication topology is established as a directed network. Each distributed generator (DG) receives voltage and current information from neighboring devices and uses its own current and voltage regulators to achieve control objectives. Compared to existing controllers, the convergence time upper bound of the proposed approach is independent of the initial value and can be directly adjusted as a parameter. The stability of the proposed control strategy is then verified using Lyapunov theory. Finally, hardware-in-loop test results are conducted to validate the performance of the control strategy, and its superiority is demonstrated through comparison with existing control methods. • The upper limit of the convergence time can be predefined as an input parameter. • A proportional consensus is achieved for the current with a directed topology. • The effectiveness is validated by using the RT-LAB experimental platform. [ABSTRACT FROM AUTHOR]

Steel plate is one of the most valuable steel products which is highly customized in specification according to the demands of users. In this case, the outbound scheduling of steel plates is a challenging issue since its efficiency and complexity are impacted by both steel plate shuffling and truck loading sequencing. To overcome this challenge, we propose to jointly optimize steel plate shuffling and truck loading sequencing (SPS-TLS) by utilizing the data of steel plates and trucks collected by Industrial Internet of Things (IIoT). The SPS-TLS problem is firstly transformed as an orders scheduling problem which is formulated as a mixed-integer linear programming (MILP) model. Then an alternating iteration algorithm based on deep reinforcement learning (AltDRL) is proposed to solve the SPS-TLS problem. In AltDRL, the deep Q network (DQN) with prioritized experience replay (PER) and the heuristic algorithm are combined to iteratively obtain the near-optimal shuffling position of blocking plates and truck sequence. Experiments are executed based on data collected from a real steel logistics park. The results confirm that AltDRL can significantly reduce the number of plate shuffles and improve the outbound scheduling efficiency of steel plates. • Steel plate shuffling and truck loading sequencing are jointly optimized. • Proposing a mathematical model for the joint optimization problem. • An algorithm based on deep reinforcement learning is proposed. • Proposed algorithm evaluated using real steel logistics park data. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops a new approach to identify Wiener model. Firstly, a sequence of step signals with various amplitudes are supplied to the system. The structure of the static nonlinear function is obtained from the input step signals and their corresponding steady-state responses. Once the structure of the nonlinearity is determined, the parameters describing the nonlinearity are estimated through minimizing an objective function. Secondly, the parameters of the linear dynamic subsystem are estimated using random input from the view point of optimization. Particle swarm optimization is used to solve the two optimization problems involved in parameter estimation of static nonlinear function and linear dynamic subsystem. The proposed method makes the identification problem of nonlinearity separate from that of linear part and simplifies the identification procedure significantly. Also, it does not require any structure information about the static nonlinear function. Two examples are given to validate the effectiveness of the proposed method. [Copyright &y& Elsevier]

This paper developed a relay assignment and power allocation algorithm for data aggregator units (DAUs) in smart grid in order to minimize the cost to the utility company. A cooperative communication network with multiple DAUs assisted by multiple relays was deployed at the demand side in smart grid, and the relay assignment and power allocation problem was formulated as a nonlinear programming problem. Using the penalty function method, we transformed the constrained nonlinear programming problem into an unconstrained optimization problem. Then we developed an multi-swarm artificial bee colony (MS-ABC) algorithm with cooperative learning to search for the optimum. Simulation results indicate that the optimal relay assignment and power allocation can reduce the cost to the utility company. Moreover, the MS-ABC algorithm shows good performances and search ability. [ABSTRACT FROM AUTHOR]

Highlights • A credit rating based multi-leader multi-follower game is proposed. • A best response algorithm is designed to obtain the unique equilibrium strategy. • The impact level of transmission losses on trading behaviors is discussed. Abstract In this paper, we investigate the problem of credit rating management in energy trading among microgrids subject to transmission losses and wheeling cost. The main concern is how to constrain the default behaviors of retailers to enable all the consumers and retailers to be actively involved in the energy trading. By endowing retailers as leaders and consumers as followers, we establish a multi-leader multi-follower dynamic game model and propose a scorecard model based on logistic regression to evaluate retailers' credit ratings. The concept of trust degree is then introduced for all the retailers as a punitive measure to relate their credit ratings with the reduction in the profit. With such a strategy, we can theoretically show that a unique equilibrium exists for the dynamic game model. Moreover, a best response algorithm is proposed to make the consumers and retailers achieve the equilibrium iteratively. Numerical simulations are provided to demonstrate the effectiveness and efficiency of the proposed method. It is found that default behaviors of selfish retailers can be greatly constrained with only a slight degradation of the interests of other participants, thereby promoting the establishment of a trustworthy trading market. We also discuss the influence level of transmission losses on trading behaviors of retailers and consumers. [ABSTRACT FROM AUTHOR]

In this paper, we study multi-zone air environment comfort and building energy conversation for the heating, ventilation, and air conditioning (HVAC) system. Each zone is equipped with the air-handling unit (AHU) and fan coil unit to regulate the supply air state for comfortable zone climate. It is known that the multi-zone air environment appears spatial and temporal variation due to the hygro-thermal interaction which is an extremely complex nonlinear dynamic process. In this study, hybrid models incorporating the first-principles model with full form dynamic linearization (FFDL)-based data-driven model have been proposed to precisely describe the multi-zone climate dynamics including unknown nonlinearity and uncertainty. Then model-free adaptive control (MFAC) scheme is designed for multi-zone climate control, which contains controller design, parameter estimation and reset mechanism to ensure system control performance. Finally, different case studies are conducted to test the performance of model prediction and system control for the multi-zone climate environment. Model validation shows that the zone climate prediction of the proposed hybrid model shows better agreement with actual measured data. The step response results display that the hybrid model-based MFAC can realize multi-zone climate control performance improvement in faster convergence without stable bias. By comparing with prior work, the hybrid model-based MFAC has the potential to get closer to actual situation in cost-effective multi-zone climate control. • The first principles and data-based hybrid model is built for multi-zone climate. • Full form dynamic linearization is used for unknown hygro-thermal dynamics. • An adaptive controller is designed for higher temperature and humidity accuracy. • The average energy saving can reach up to 4% by the hybrid model-based MFAC scheme. [ABSTRACT FROM AUTHOR]

With the integration of energy storage system (ESS), photovoltaic cell (PV) and generator, hybrid power ship system (HPSS), as one of promising technology, is regarded as an advanced method to improve energy efficiency and marine environment quality. However, the computational complexity and non-convexity of energy scheduling in hybrid power ship system make it challenging to obtain the feasible solution. To address this crucial issue, a heuristic optimization algorithm named multi-populations particle swarm optimization (MPPSO) is proposed for economic and feasible energy scheduling. Firstly, a hybrid power ship system, comprising generator, ESS, PV, service loads and propulsion system, is formulated. On this basis, a load shedding coefficient is given for the secure and stable operation of hybrid power ship system under fault model. Then, to achieve energy scheduling, several improvements are proposed to enhance PSO. Considering the problem of premature, a nonlinear adaptive inertial weight strategy is proposed to improve the searching ability. With the fitness value of population, learning coefficients are adjusted in nonlinear so that particle can accurately learn from individual or population position. Further, a modified velocity update formula with the information of historical experience and center particle is proposed to employ the particle information fully. Finally, the effectiveness of MPPSO is illustrated on simulation experiment by three cases. • The hybrid power ship system is formulated by taking into account the economic cost, GHG emissions, and so on. • An improved PSO named multi-populations particle swarm optimization (MPPSO) is proposed by improving several optimization strategies for PSO. • The MPPSO is proposed to solve hybrid power ship system energy scheduling by taking into account operation cost minimization, GHG emissions limitation and associated technical–operational constraints. [ABSTRACT FROM AUTHOR]

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid–ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically. • A systematic online optimization framework ensuring provable performance for multi-energy system management is presented. • A method is proposed for estimating users' willingness to shift inelastic loads via public data. • The energy storage balance and real-time supply–demand balance can be achieved by two-timescale optimization. • Fast distributed method is proposed to deal with temporally-coupled constraints. [ABSTRACT FROM AUTHOR]

The platoon control problem of interconnected vehicular systems is investigated in this paper studies. A finite-time adaptive sliding mode control (SMC) method based on acceleration feedback is developed to achieve platooning of interconnected vehicular systems subject to external disturbances and string stability of the closed-loop platoon system is analyzed. The string stability of the closed-loop system is achieved by applying the constant spacing (CS) strategy in the proposed adaptive sliding mode controller under zero initial steady-state error conditions. A novel error definition method is proposed to transform the non-zero initial steady-state error into the zero initial steady-state error so that the proposed algorithm can be independent of the initial steady-state errors and reduce the large transient response caused by non-zero initial steady-state errors. The salient feature of the proposed SMC law is that it does not need to know the disturbance boundary in advance. Finally, simulations and experiments are conducted to demonstrate the advantages and effectiveness of the proposed algorithm. [Display omitted] • The string stability can be guaranteed in any initial state. • Acceleration feedback-based controller shows better transient performance. • The finite-time control method improves the closed-loop system convergence rate. • The proposed platoon control method provides guidance for practical systems. [ABSTRACT FROM AUTHOR]

Abstract: The parameter estimation problem is considered for a class Wiener systems. First, the effect of the forgetting factor on the stochastic gradient algorithm is analyzed. Then, a Wiener system stochastic gradient with a changing forgetting factor algorithm is presented which makes full use of the forgetting factor. Finally, an example is provided to test and verify the effectiveness of the proposed algorithms. [Copyright &y& Elsevier]

Traditional assembly line requires a significant amount of designs from engineers, especially in the case of multi-species and small-lot production. Recently, intelligent algorithms based on reinforcement learning are proposed to address this issue. However, the lower success rate and safety reasons limit their industrial applications. In this article, we proposed a systematic solution, including the automatic planning of assembly motions and the monitoring system of the production lines. In the planning stage, we built the digital twin model of the assembly line, then trained a deep reinforcement learning agent to assembly the workpieces. In the production stage, the digital twin model is used to monitor the assembly lines and predict failures. To validate the system we proposed, we conducted a peg-in-hole assembly experiment, and reached a 90% success rate for a single assembly attempt. During the whole experiment, no collision happens in the real world. [ABSTRACT FROM AUTHOR]

• A multi-energy management framework including fuel cell vehicles and energy storage is proposed. • The synergies between hydrogen and electricity further improve the absorption of the renewable energy. • A joint algorithm is designed to optimize the long-term energy cost. The emerging paradigm of interconnected microgrids advocates energy trading or sharing among multiple microgrids. It helps make full use of the temporal availability of energy and diversity in operational costs when meeting various energy loads. However, energy trading might not completely absorb excess renewable energy. A multi-energy management framework including fuel cell vehicles, energy storage, combined heat and power system, and renewable energy is proposed, and the characteristics and scheduling arrangements of fuel cell vehicles are considered to further improve the local absorption of the renewable energy and enhance the economic benefits of microgrids. While intensive research has been conducted on energy scheduling and trading problem, a fundamental question still remains unanswered on microgrid economics. Namely, due to multi-energy coupling, stochastic renewable energy generation and demands, when and how a microgrid should schedule and trade energy with others, which maximizes its long-term benefit. This paper designs a joint energy scheduling and trading algorithm based on Lyapunov optimization and a double-auction mechanism. Its purpose is to determine the valuations of energy in the auction, optimally schedule energy distribution, and strategically purchase and sell energy with the current electricity prices. Simulations based on real data show that each individual microgrid, under the management of the proposed algorithm, can achieve a time-averaged profit that is arbitrarily close to an optimum value, while avoiding compromising its own comfort. [ABSTRACT FROM AUTHOR]

• The emergency of malicious attack brings risk to the security of smart energy grid. • A novel detection and isolation scheme for protecting energy management system is introduced. • The proposed protection scheme is based on the characteristics of interval residual. • The proposed detection standard can address the limitation of the precomputed threshold. • The effectiveness of the proposed protection scheme is experimentally validated. With the integration in information and communication technologies, and advanced metering infrastructure, smart energy grid, as one of typical sustainable energy systems, addresses the energy and environment problems. However, the emergency of bias injection attack aiming at destroying the energy management center, brings great security threat to the security of smart energy grid. To address risks in energy-cyber-physical systems, this paper proposes a distributed detection and isolation scheme against the bias injection attack in smart energy grid. Considering the transmitted information of energy management centers in adjacent grid subareas, the proposed distributed detection and isolation scheme includes local and global steps. In the local-step, each local energy management center detects and isolates the possible sensor attack set, based on the constructed local attack signature judgment logic matrix. In the global-step, the subarea attack set is detected and isolated via the established global attack signature judgment logic matrix. Combining the above local and global detection and isolation framework, we can ensure the security of energy management center in smart energy system. This proposed distributed detection and isolation scheme examines some important practical aspects of deploying bias injection attack detection including: the limitation of the precomputed threshold; the detection delay; the accuracy in detecting bias injection attack. Finally, the effectiveness of the developed distributed detection and isolation scheme is demonstrated by using detailed studies on the IEEE 8-bus and IEEE 118-bus smart energy grid system. [ABSTRACT FROM AUTHOR]