Network Control Scheme Research Articles

An improved acoustic transponder system via LCCLC impedance matching network and equalization control strategy

AbstractThe underwater acoustic transducer (UAT) generates a large amount of reactive power during the operation, greatly limiting the output capability of the power amplifier. It is necessary to match the UAT and improve its load characteristics. The conventional design methods are not powerful enough to solve nonlinear or parameter‐sensitive problems, lacking enough convergence ability for the network design problem. This article proposes an optimization design method for LCCLC impedance matching network based on the improved chaos optimization algorithm and the corresponding equalization control strategy. First, the impedance model of the UAT is proposed, and then the matching principle of the LCCLC network is analyzed. Based on these, the optimization design for LCCLC network is described in detail. Then, to solve the problem of the calculation of the command signal, a control method based on the FIR filter principle is proposed. Finally, the proposed methods are verified by an experimental platform. The results show that the proposed design method and its corresponding control strategy successfully solve the matching and emission problems.

Encryption-based Coordinated Volt/Var Control for Distribution Networks With Multi-Microgrids

This paper proposes a data-driven coordinated volt/var control (VVC) strategy for active distribution networks (ADN) with multi-microgrids, which can achieve online economic and secure operations under false data injection attacks (FDIA). Based on voltage and power measurements, the microgrid central controller (MGCC) obtains optimal reactive power supports at the point of common coupling (PCC) and sends them to the distribution system operator (DSO). The MGCC is formulated with a convolution neural network (CNN) to emulate the optimal behaviors in microgrids (MGs), which can reduce computational burdens and facilitate its online application. The DSO then performs centralized optimization to dispatch VVC devices and update voltages at PCC. A voltage sensitivity-based reactive power adjustment method is also developed to simplify the iterative optimization process between ADN and MGs without deteriorating the VVC performance in each MG. Finally, data integrity and privacy are protected through an encrypted communication process against FDIA. The GGH (Goldreich-Goldwasser-Halevi) encryption algorithm directly prevents attackers from accessing the original transmitted data, while the RSA (Rivest-Shamir-Adleman) digital signature algorithm helps detect malicious tampering with the ciphertext during communication. Numerical simulations on a modified IEEE 33-bus ADN with three EU 16-bus MGs verify the effectiveness of the proposed method in mitigating voltage violations, reducing voltage regulation costs and protecting data security.

Adaptive and intelligent control of a dual-arm space robot for target manipulation during the post-capture phase

Dual-arm space robots provide a promising solution for efficiently and flexibly completing complex on-orbit servicing tasks. However, their effectiveness is often hindered by strong dynamic coupling between the robotic arms and the satellite base in the microgravity environment. In this paper, both robotic arms are used as task arms, and a system model that considers the closed-loop constraint formed after a dual-arm space robot captures a target is established. On this basis, two types of control strategies, namely self-tuning control strategy (STC) and radial basis function neural network control strategy (RBF) are proposed for trajectory tracking of the target transferred or detumbled by the robot arms in the presence of system uncertainty or external disturbances. The control methods are verified and compared against the traditional computed torque control strategy (CTC) in terms of control accuracy and system robustness through simulation. The results demonstrate the excellent robustness of both RBF and STC strategies. RBF outperforms the other strategies in terms of control accuracy, while STC proves to be the most energy-efficient.

A unified approach of detecting phase transition in time-varying complex networks

Deciphering the non-trivial interactions and mechanisms driving the evolution of time-varying complex networks (TVCNs) plays a crucial role in designing optimal control strategies for such networks or enhancing their causal predictive capabilities. In this paper, we advance the science of TVCNs by providing a mathematical framework through which we can gauge how local changes within a complex weighted network affect its global properties. More precisely, we focus on unraveling unknown geometric properties of a network and determine its implications on detecting phase transitions within the dynamics of a TVCN. In this vein, we aim at elaborating a novel and unified approach that can be used to depict the relationship between local interactions in a complex network and its global kinetics. We propose a geometric-inspired framework to characterize the network’s state and detect a phase transition between different states, to infer the TVCN’s dynamics. A phase of a TVCN is determined by its Forman–Ricci curvature property. Numerical experiments show the usefulness of the proposed curvature formalism to detect the transition between phases within artificially generated networks. Furthermore, we demonstrate the effectiveness of the proposed framework in identifying the phase transition phenomena governing the training and learning processes of artificial neural networks. Moreover, we exploit this approach to investigate the phase transition phenomena in cellular re-programming by interpreting the dynamics of Hi-C matrices as TVCNs and observing singularity trends in the curvature network entropy. Finally, we demonstrate that this curvature formalism can detect a political change. Specifically, our framework can be applied to the US Senate data to detect a political change in the United States of America after the 1994 election, as discussed by political scientists.

Adaptive inertia and damping of grid‐connected inverter with improved VSG control

AbstractThe control technology of virtual synchronization generator (VSG) based on energy storage system is proposed to compensate for the inertia and damping loss caused by the grid connection of distributed generators. However, with the addition of virtual inertia and damping, VSG is prone to generate frequency and power oscillation, and the regulating ability is weakened. For the above problems, this paper analyzes the inertia and damping characteristics of the synchronous motor in the rotation of synchronous motors, and applies artificial neural network control to VSG. In intelligent control strategy, a multi‐input‐output radial basis function (RBF) neural network is first proposed to adaptively adjust virtual inertia and damping. Finally, an experimental platform with a rated power of 1 kW and a variable load of 1 kW was built to validate the proposed control strategy. The experimental results show that the artificial neural network control strategy has a better regulation ability.

Cooperative Passivity-Based Control of Nonlinear Mechanical Systems

In this work, we propose two cooperative passivity-based control methods for networks of mechanical systems. By cooperatively synchronizing the end-effector coordinates of the individual agents, we achieve cooperation between systems of different types. The underlying passivity property of our control approaches ensures that cooperation is stable and robust. Neither of the two approaches rely on the modeling information of neighbors, locally, which simplifies the interconnection of applicable systems and makes the approaches modular in their use. Our first approach is a generalized cooperative Interconnection-and-Damping Assignment passivity-based control (IDA-PBC) scheme for networks of fully actuated and underactuated systems. Our approach leverages the definition of end-effector coordinates in existing single-agent IDA-PBC solutions for underactuated systems to satisfy the matching conditions, independently of the cooperative control input. Accordingly, our approach integrates a large set of existing single-agent solutions and facilitates cooperative control between these and fully actuated systems. Our second approach proposes agent outputs composed of their end-effector coordinates and velocities to guarantee cooperative stability for networks of fully actuated systems in the presence of communication delays. We validate both approaches in simulation and experiments.

Radial basis function neural network controller for power control of molten salt breeder reactor of nuclear power plant

The molten salt breeder reactor (MSBR) basically uses molten salt as fuel. Due to molten salt, it is difficult to control power of the MSBR core. To solve this problem, a neural network control scheme is adopted for the same system. Firstly, a nonlinear MSBR model is taken and after linearizing a single input single output (SISO) system is constructed out of it. Radial basis function neural network (RBFNN) has strong tolerance to input noise, online learning ability and good generalization. Therefore, RBFNN controller is used for controlling the core power of the MSBR. The conventional RBFNN dominates in the neural active region. Compact RBF networks has been designed by using newly developed algorithm. By using Lyapunov stability criterion, it has proved that the variation of core power is bounded and it may be comparable to any traditional neural networks or fuzzy interference system. Results of the study reflect the efficiency of RBFNN controller for controlling power of MSBR core.

Research on intelligent control theory and strategy of gas drainage pipe network based on graph theory

To address the issues related to the working conditions of the mine gas drainage system and achieve adaptive adjustment of negative pressure during gas drainage, this study proposes a gas drainage pipe network structure model based on graph theory principles. The model is built upon the air-gas mixed flow model of the pipe network. The boundary conditions of the pipe network are determined, and both the pipe network flow model and resistance model are constructed. An assignment iterative adjustment method is used to establish the pipe network solution model, enabling the solution of the gas drainage pipe network. The mathematical model for regulating and controlling the gas drainage pipe network is formulated, considering the objective function, decision variables, and constraint conditions based on the drainage parameters. The control model, along with the flow calculation model of the gas drainage pipe network system, is employed to calculate the optimal gas control scheme. This scheme enables remote intelligent control of the control valve opening on the ground, significantly improving the safety and efficiency of the gas drainage system. The engineering application of Liuzhuang Coal Mine demonstrates the successful realization of the rational distribution of negative pressure and intelligent dynamic control of the drainage pipe network. As a result, the total gas concentration in the drainage pipe network has increased by 12%, yielding favorable outcomes. The findings of this study have significant theoretical and practical implications for the design, development, and improvement of intelligent control systems for mine drainage pipe networks.

Adaptive neural network control for Markov jumping systems against deception attacks

This paper proposes an innovative approach for mitigating the effects of deception attacks in Markov jumping systems by developing an adaptive neural network control strategy. To address the challenge of dual-mode monitoring mechanisms, two independent Markov chains are used to describe the state changes of the system and the intermittent actuator. By employing a mapping technique, these individual chains are amalgamated into a unified joint Markov chain. Additionally, to effectively approximate the unbounded false signals injected by deception attacks, an adaptive neural network technique is skillfully built. A mode monitoring scheme is implemented to design an asynchronous control law that links the mode information between the joint Markov chain and controller with fewer modes. The paper derives sufficient criteria for the mean-square bounded stability of the resulting system based on Lyapunov theories. Finally, a numerical experiment is conducted to demonstrate the effectiveness of the proposed method.

A genetic algorithm based flow control scheme for LTE-NR dual connectivity networks

A genetic algorithm based flow control scheme for LTE-NR dual connectivity networks

A collaborative control strategy for MV-LV distribution networks considering multi-level access of flexible resources

A collaborative control strategy for MV-LV distribution networks considering multi-level access of flexible resources

Dynamic Event-triggered Control for Nonlinear PDE System with Actuator Dead-zone

A dynamic event-triggered network control scheme is proposed for a class of distributed parameter systems with time delay and nonlinearity. The security control of the PDE system under DOS attack based on dynamic event-triggered is studied. A dynamic event-triggered scheme is adopted to reduce the communication burden, that is, the sampling data will be sent only when the original trigger condition is violated. At the same time, a new model including denial of service attack (DOS) is established to describe the random network attack. When a hacker launches a DoS attack, it prevents the sampling data from reaching the destination by interfering with the network channel. DOS attacks follow no specific pattern and exhibit non-periodic behavior or irregular probability distribution. A mathematical model of a state-dependent uncertain system with an event-triggered scheme was established to study denial of service attacks. By using the Lyapunov-Krasocskii stability theory and linear matrix inequality tools, sufficient conditions were derived to ensure the index mean square stability of the state-dependent uncertain system. Based on this, sufficient conditions for the asymptotic stability of the closed-loop network control system and the expected performance index of the system were obtained. Numerical examples verified the feasibility of the method, and controller design validated the applicability of the theoretical results.

A distributed routing-aware power control scheme for underwater wireless sensor networks

During the data routing process in underwater wireless sensor networks, the communication ranges of nodes profoundly affect the network performances, such as energy utilization and delay overhead. Thus, in this paper, we design a routing-aware power control scheme for underwater wireless sensor networks, whose primary purpose is to assign a differentiated optimal communication radius for each sensor node. Compared with the existing studies, this scheme pays more attention to the combination of power control and existing opportunity routing mechanisms, the distributed implementation of algorithms, and the specific characteristics of underwater sensor network topology. The scheme consists of two main algorithms, namely, the rough adjustment algorithm and the fine adjustment algorithm. These two algorithms employ the game theory and the flooding mechanism to achieve the trade-off among network connectivity maintenance, routing performance promotion, and energy utilization optimization during the power control process. Based on the theoretical analysis, with the proposed algorithms, each node can adjust its transmitted power in a distributed, convergent, and low-complexity manner. According to our numerical simulation experiment, the designed power control scheme could improve the diverse networking performances, including the network lifetime, the packet delivery ratio, the energy efficiency, the load balance, and the end-to-end delay.

Modeling and control for PEMFC hydrogen management subsystem based on neural network compensation and prescribed tracking accuracy

The hydrogen management subsystem plays an important role in fuel cell automobiles as it impacts the output performance of the polymer electrolyte membrane fuel cell (PEMFC). This paper investigates the design and implementation of the neural network-based control for anode pressure and hydrogen excess ratio (HER). Firstly, the coupled nonlinear multiple-input-multiple-output (MIMO) system is transformed into canonical form one by using input–output linearization. Secondly, the neural network control scheme is proposed to regulate the anode pressure and HER without using the exact prior knowledge of the system. Moreover, a prescribed performance function (PPF) is proposed to guarantee the overshoot and steady-state tracking errors within the quantitative boundaries. To avoid the laborious parameter selection, the restriction that the initial error is within the performance function bound is relaxed by proposing a tuning function. Finally, the Lyapunov stability theory, numerical simulations and hardware-in-loop (HIL) experiments show the effectiveness of the proposed controller. Simulation and HIL experiment results show that the proposed control strategy is effective in realizing quantitative adjustment of tracking error while showing the best performance indexes including the root mean square error (RMSE) and minimum deviation of tracking error (DTE) compared with the neural network controller (NNC) and traditional prescribed performance controller (TPPC).

Power Control and Interference Pricing Strategies for Heterogeneous Networks With Renewable Energy Sources

In this paper, the power control and interference pricing problems between the macro base station (MBS) and small cell base stations (SBSs) in Heterogeneous networks (HetNets) are investigated. To optimally allocate power resources and effectively manage the power resources to reduce interference in HetNets, an innovative Stackelberg differential game approach is proposed. In the proposed Stackelberg game, the SBSs control their transmit power to achieve cost minimization based on the interference price offered by the MBS. Given the optimal power control strategies of SBSs, the MBS can adjust the interference price to realize the objective optimization. Renewable energy sources are introduced into the proposed HetNets, and differential equations formulate the SBSs' dynamic energy. Meanwhile, the MBS's state is given by the dynamic spectrum resources. Then the differential game is imported to achieve optimal power control and interference pricing. The open-loop Nash equilibrium solutions are first analyzed for the MBS and SBSs. Then the feedback Nash equilibrium solutions are given. Under the feedback pattern, the proposed model's performances in the non-cooperative and cooperative situations are discussed. Numerical simulations are given to show the convergence and effectiveness of the proposed game approach.

A Prediction-Based Approach Realizing Finite-Time Convergence of Networked Control Systems

This paper presents a networked model predictive control (MPC) strategy for the finite-time convergence of networked control systems with two-channel random communication constraints. Network-induced delays and packet dropouts in the backward and forward channels are transformed into the equivalent delays in each channel, which are actively compensated in the controller and actuator, respectively. Future control inputs are obtained by a finite-time MPC strategy, and the upper bound of the convergence time is given by theoretical analysis. Finally, the proposed scheme is verified by the numerical simulation of a mass-spring-damper system.

Design of a BP neural network PID controller for an air suspension system by considering the stiffness of rubber bellows

A rubber bellows has certain creep characteristics, and its role has often been ignored in previous air suspension research. To describe the motion state of a vehicle more accurately during driving, this paper uses amplitude correlation and frequency correlation to describe the mechanical characteristics of the rubber bellows in air spring mechanical characteristics research, establishes an air suspension simulation, and explores the role of the rubber bellows in the suspension. In addition, an adaptive neural network controller is designed to simulate and analyze the Body Acceleration (BA), Sprung Mass Displacement (SMD), Unsprung Mass Displacement (UMD) and Dynamic Wheel Load (DWL) of the suspension. Simulation results show that the rubber bellows has a certain impact on suspension SMD and has a great impact on the performance of the active suspension. The root mean square (RMS) can be increased by 4.55 % but little impact is found on other performance indicators. In addition, compared with the conventional PID control strategy, the adaptive neural network control strategy designed in this paper has better control performance, reducing the RMS of BA and SMD by 27.58 % and 16.67 %, respectively, and improving the control effect by about 4.48 % and 4.17 %, respectively compared with PID. Thus, it can better maintain the stability of the car during driving.

Event-triggered nonfragile predictive control for networked control system with redundant channels

This paper is concerned with the nonfragile predictive control problem for the networked control system (NCS) with input delay and network-induced time delays (NITDs). First, the transmission strategy governed by redundant channels is introduced to reduce the occurrence of data packet dropout between the sensor and the observer. Moreover, the event-triggered mechanism (ETM) is employed to save the network resources, where the maximum triggered interval is given. Subsequently, a nonfragile networked predictive control scheme (NPCS) is designed to address the input delay and the NITDs. Accordingly, the sufficient criterion ensuring mean-square exponential stability (MSES) is derived by employing the Lyapunov–Krasovskii stability theorem. Both the observer gain matrix (OGM) and the controller gain matrix (CGM) are gained through solving the nonlinear minimization problem with convex constraints. Finally, a simulation example is utilized to demonstrate the usefulness of the proposed main results.

An output formation consensus control for leader–follower networked multi‐agent systems under communication constraints and switching topologies

AbstractIn this paper, the formation consensus problem for a class of leader–follower networked multi‐agent systems under communication constraints and switching topologies is investigated. A networked predictive control scheme is proposed to achieve stability and output formation consensus with the switching topology, capable of compensating for data loss and time delays in the network. By equating the whole closed‐loop networked multi‐agent system with the proposed control scheme to the corresponding switched system, the sufficient and necessary condition of output formation consensus and stability for agents is given. Finally, using three‐degree‐of‐freedom air‐bearing spacecraft simulators as the control objects, the proposed scheme is demonstrated to be able to actively compensate for the communication constraints through numerical simulations, and it is also verified to have a good control performance by further realizing the formation task of the simulators through practical experiments.

Event-triggered control for robust exponential synchronization of inertial memristive neural networks under parameter disturbance

Synchronization of memristive neural networks (MNNs) by using network control scheme has been widely and deeply studied. However, these researches are usually restricted to traditional continuous-time control methods for synchronization of the first-order MNNs. In this paper, we study the robust exponential synchronization of inertial memristive neural networks (IMNNs) with time-varying delays and parameter disturbance via event-triggered control (ETC) scheme. First, the delayed IMNNs with parameter disturbance are changed into first-order MNNs with parameter disturbance by constructing proper variable substitutions. Next, a kind of state feedback controller is designed to the response IMNN with parameter disturbance. Based on feedback controller, some ETC methods are provided to largely decrease the update times of controller. Then, some sufficient conditions are provided to realize robust exponential synchronization of delayed IMNNs with parameter disturbance via ETC scheme. Moreover, the Zeno behavior will not happen in all ETC conditions shown in this paper. Finally, numerical simulations are given to verify the advantages of the obtained results such as anti-interference performance and good reliability.