Distributed Control Research Articles

Optimising torque for three-disc afpmsm in electric vehicles using bp_ann and anfis algorithms

Designing an optimal torque distribution controller for the three-disc axial flux permanent magnet synchronous (three-disc AFPMSM) optimises performance while ensuring robustness, stability, and adaptability in a real-world condition. This is crucial for maximising the potential of AFPMSM, particularly in a modern application like an electric vehicle and a renewable energy. Thus, a controller compatible with more complex systems in the future is essential. This paper presents a system that combines torque control algorithms based on a back-propagation neural network (BP-ANN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The BP-ANN uses a multi-layer structure where the input layer processes factors such as load torque, rotational speed, and stator current, hidden layers model complex nonlinear interactions, and the output layer predicts optimal torque for AFPMSM operation. Training involves minimising the error between predicted and actual torque through gradient descent and iterative adjustments of weights and biases. The ANFIS-based control enhances performance by integrating neural network learning with fuzzy logic to optimise torque output. By leveraging the strengths of both BP-ANN and ANFIS, the system offers a stable, efficient, and adaptable solution for three-disc AFPMSMs. The Matlab/Simulink simulations confirm its effectiveness, showing balanced torque distribution, reduced energy losses, improved drivetrain efficiency, and adaptability to sudden load or road changes, ensuring stability and enhanced dynamic response

Optimization of airflow distribution in mine ventilation networks considering ventilation energy consumption and number of regulators

In view of the problem of high energy consumption and high control costs caused by uneven airflow distribution and unreasonable control in complex mine ventilation networks, this study takes the minimum ventilation energy consumption and number of regulators as optimization objectives to establish a multi-objective optimization model for airflow distribution in mine ventilation networks. Based on the roadway adjustable attributes and the minimum spanning tree principle, the location of regulators was reasonably determined. Moreover, this article proposes an improved invasive weed optimization (IIWO) algorithm to solve the optimization model with high coupling and nonlinearity. Compared with other algorithms, IIWO showed excellent optimization performance. IIWO was applied to optimize the airflow distribution in the mine ventilation network. The results show that the algorithm can effectively reduce the energy consumption and number of regulators of the ventilation network, and the energy saving rate of the fan is 31.78%.

StegoEDCA: An Efficient Covert Channel for Smart Grids Based on IEEE 802.11e Standard

Smart grids are continuously evolving, incorporating modern technologies such as Wi-Fi, Zigbee, LoRaWAN or BLE. Wi-Fi are commonly used to transmit data from measurement systems, distribution control and monitoring systems, as well as network protection systems. However, since Wi-Fi networks primarily operate on unlicensed frequency bands, this introduces significant security risks for sensitive data transmission. In this paper, we propose a novel and highly efficient covert channels that utilize IEEE 802.11 Enhanced Distributed Channel Access (EDCA) for data transmission. It is also the first ever covert channel that employ three or four independent covert mechanisms to enhance operational efficiency. The proposed mechanism is also the first to exploit the Transmission Opportunity (TXOP) period and the access categories of the EDCA function. The protocol was developed and tested using the ns-3 simulator, achieving excellent performance results. Its efficiency remains consistent even under heavy network load with additional background traffic. These covert channels provide an innovative solution for securely transmitting large volumes of data within the smart grid.

Parallelized robust distributed model predictive control in the presence of coupled state constraints

In this paper, we present a robust distributed model predictive control (DMPC) scheme for dynamically decoupled nonlinear systems which are subject to state constraints, coupled state constraints and input constraints. In the proposed control scheme, all subsystems solve their local optimization problem in parallel and neighbor-to-neighbor communication suffices. The approach relies on consistency constraints which define a neighborhood around each subsystem’s reference trajectory where the state of the subsystem is guaranteed to stay in. Contrary to related approaches, the reference trajectories are improved consecutively. In order to ensure the controller’s robustness against bounded uncertainties, we employ tubes. The presented approach can be considered as a time-efficient alternative to the well-established sequential DMPC. In the end, we briefly comment on an iterative extension. The effectiveness of the proposed DMPC scheme is demonstrated with simulations, and its performance is compared to other DMPC schemes.

Fixed-time event-triggered distributed formation control for underactuated USVs considering actuator saturation

This paper proposes an event-triggered fixed-time containment control strategy (FTETCCS) to address the distributed formation control (DFC) problem of multiple underactuated surface vessels (USVs). By integrating fixed-time control (FTC) with event-triggered control (ETC), this strategy achieves fixed-time stability while reducing the usage of communication resources within the system. Initially, the fixed-time containment control law is utilized to compute the desired virtual target points for the followers. Subsequently, to facilitate future real-world experiments, an event-triggered fixed-time containment control guidance law is developed. This law computes the desired heading angle and forward speed derived from the virtual target points. The resulting values are then accurately tracked using a fixed-time controller. Furthermore, this study tackles the challenges posed by lumped unknown terms and actuator saturation during USV navigation. To address these issues, a fixed-time disturbance observer and a fixed-time auxiliary saturation system are designed. The effectiveness and superiority of the proposed FTETCCS are validated through theoretical analysis and simulation studies, with an analysis of the Zeno phenomenon confirming that the event-triggered mechanism does not lead to Zeno phenomena. Additionally, real ship experiments demonstrate the practical applicability of the proposed event-triggered fixed-time containment control guidance law.

Quantitative Identification of Moisture Sources over the Tibetan Plateau: Spatial Distributions and Temporal Variabilities

Abstract The imbalanced hydrological cycles and water resource instability over the Tibetan Plateau (TP) are a topic of wide concern. Moisture sources affected by large-scale circulations are the main controls of precipitation and water resource distributions; as such, the quantitative identification of moisture sources is the key to the changes in precipitation or water-related environments, further aids efficient water management over the TP and in downstream regions of Asia. In this study, we primarily identified the spatial distributions and temporal variabilities of TP’s varied moisture sources, using HYSPLIT modeling and spatially dense precipitation isotopes (δ18O). Results showed that 1) moistures from the westerlies (West), the western arm of Indian summer monsoon (ISM) (ISM1), the eastern arm of ISM (ISM2), and the inner TP are the TP’s main moisture sources, with highest proportional contributions being >70%, ∼40%, >80%, and ∼10%–20% at northwest, southwest, and southeast sectors of the study area and the central TP region; 2) each moisture has its own region where it predominates and shows specific trends during 1951–2020 (West-northwest sector-increase, ISM1-southwest sector-increase, ISM2-southeast sector-decrease, and TP-central TP region-increase); 3) each of the moisture proportions and their temporal trends varied with different days of back trajectories (days 02–04–06–08–10), while their spatial patterns are similar; and 4) when verifying the modeled moisture proportions, precipitation δ18O is positively correlated or covaries with dry sources such as the West or TP moisture and inversely for humid ISM (ISM1 or ISM2) moisture. This work will improve our understanding of moisture-related hydrological, meteorological, and ecological studies in the “Asian Water Tower” region. Significance Statement The Tibetan Plateau (TP) is undergoing imbalanced hydrological cycles and water resource instability in response to global climate change. As the TP supplies vast quantities of water to Asia’s huge population, the socioeconomic impact caused by these changes is considerable. This study aims to quantitatively identify the spatial distributions and temporal variabilities of varied moisture sources over the TP in recent decades, using high-resolution modeling and ground-based precipitation isotopes. Results show that each moisture has its own region where it predominates and shows specific trends during 1951–2020. These findings can help to explain precipitation patterns or water resource distributions and provide insights into efficient water management over the TP and in downstream regions of Asia.

Multiphysics Ensemble Framework for Representing Western Himalayan Precipitation Climatology and Extremes: An Assessment from the WRF Model

Abstract Numerical model simulations are sensitive to the choice of physical parameterizations, particularly those related to convection processes, especially over complex terrains like the western Himalayan region. This study evaluates the sensitivity of the Weather Research and Forecasting (WRF) Model to three convective parameterization schemes [Betts–Miller–Janjić (BMJ), Kain–Fritsch (KF), and Grell–Freitas (GF)] and their multiphysics ensemble mean (ENSM) through long-term (2001–16) winter (December–March) simulations. Model outputs are validated against multisource datasets [India Meteorological Department (IMD), IMERG, ERA5, and Indian Monsoon Data Assimilation and Analysis (IMDAA)], with extreme precipitation events (EPEs) identified as those exceeding the 95th-percentile threshold. Climatology analysis reveals systematic precipitation biases across all single schemes, significantly reduced in the ENSM. Improvements in skill scores observed for ENSM highlight the reliability of the ensemble approach as an alternative to account for model errors. Underestimations of low-level clouds and hydrometeors improve in ENSM. BMJ and KF (GF) schemes exhibit overestimated (underestimated) magnitudes and location-wise discrepancies for baroclinic instability and moisture transport. These issues are mitigated in ENSM, resulting in better alignment with reference datasets. Additionally, ENSM and KF’s storm tracks align well with reanalyses, while individual schemes show a minor northward placement. For EPEs, ENSM highlights Jammu and Kashmir, Himachal, and Uttarakhand as zones of maximum heavy precipitation and frequency. ENSM realistically represents the western disturbance (WD)-induced circulation characteristics, southward-displaced subtropical jet, shifts in WD-associated vorticity maxima, and potential vorticity intrusions during extremes. Consistent with reference datasets, ENSM demonstrates the crucial role of vertical advection and dynamical controls in moisture distribution during EPEs. Our analysis indicates that a suitable WRF cumulus configuration includes either the multiphysics ENSM or the KF scheme, while the GF scheme yields the poorest results. The findings underscore the importance of evaluating both precipitation characteristics and underlying physical mechanisms to identify sources of systematic biases in model simulations.

Edges’ Riemannian energy analysis for synchronization of multi-agent nonlinear systems over undirected weighted graphs

In this note we investigate the problem of global exponential synchronization of multi-agent systems described by non-linear input affine dynamics. We consider the case of networks described by undirected connected graphs possibly without leader. We present a set of sufficient conditions based on a Riemannian metric approach in order to design a state-feedback distributed control law. Then, we study the convergence properties of the overall network. By exploiting the properties of the edge Laplacian we construct a Lyapunov function that allows to conclude global exponential synchronization of the overall network.

Intelligent vehicle trajectory tracking with an adaptive robust nonsingular fast terminal sliding mode control in complex scenarios

This study presents a strategy for an intelligent vehicle trajectory tracking system that employs an adaptive robust non-singular fast terminal sliding mode control (ARNFTSMC) approach to address the challenges of uncertain nonlinear dynamics. Initially, a path tracking error system based on mapping error is established, along with a speed tracking error system. Subsequently, a novel ARNFTSMC strategy is introduced to tackle the uncertainties and external perturbations encountered during actual vehicle operation. The adaptive laws established for the longitudinal demand force and the front-wheel steering angle do not require prior understanding of the upper limit of the lumped uncertainty, while successfully avoiding singularities and eliminating chattering. By applying Lyapunov’s stability theorem, it is shown that the control system for trajectory tracking can reach the equilibrium point within a finite time. Following this, a torque optimization distribution control strategy is developed. Ultimately, numerical simulations are used to validate both the effectiveness of the proposed approach and its robustness across different conditions.

Self‐Triggered Distributed Model Predictive Control via Path Parameter Synchronization

ABSTRACTThis paper investigates the formation tracking problem for multiple mobile robots via self‐triggered distributed model predictive control (DMPC) strategy and path‐parameter communication manner. To ensure the robots follow the desired formation structure along the predefined paths, we establish appropriate tracking error models that form a multi‐agent system. At triggered instants, each agent exchanges a sequence of path parameters representing the robot's position, resolves the optimal control problem (OCP) and subsequently determines the open‐loop phase. Different from existing coordination methodology, the proposed scheme exhibits two essential merits in environments where resources are particularly limited: (1) The tracking task of robots is achieved by designing an appropriate OCP under the DMPC scheme, and the formation task of robots is achieved through the synchronization of one‐dimensional path parameters instead of the multi‐dimensional state information, which demands less communication load; (2) The incorporation of the self‐triggered scheduler acquires the desired control performance with less calculation time, thereby relieving the computational and communication costs. Sufficient conditions are proposed to guarantee the recursive feasibility of the OCP and the closed‐loop stability. Simulation results illustrate the validity of the proposed control algorithm.

GENERAL PRINCIPLES OF FORMALIZATION OF TECHNOLOGICAL PROCESS CONTROL OF MINING PRODUCTION IN A DYNAMIC DISTRIBUTED SYSTEM

Context. The problem of synthesis, modeling, and analysis of automated control of complex technological processes of mining production as a dynamic structure with distributed parameters. Objective. On the example of the technological line of ore beneficiation, the general principles of formalization of control of mining production processes as a dynamic system with distributed parameters are considered. Method. The modeling of interactions between individual components of the control system is carried out using the methods of coordinated distributed control. In accordance with this approach, the technological line is decomposed into a set of separate subsystems (technological units, enrichment cycles). Under these circumstances, the solution to the global optimization problem is also decomposed into a corresponding set of individual subproblems of optimizing the control of subsystems. To solve the global problem, this formulation uses a two-level structure with coordinating variables that are fed to the input of local control systems for technological units and cycles. At the lower level of control, sets of subtasks have independent solutions, coordinated by the coordinating variables formed at the upper level. Results. The paper proposes a method for forming control of a distributed system of technological units of an ore dressing line based on the decomposition of the dynamics of the distributed system into time and space components. In the spatial domain, the control synthesis problem is solved as a sequence of approximation problems of a set of spatial components of the dynamics of the controlled system. In the time domain, the solution of the control synthesis problem is based on the methods of synthesizing control systems with concentrated parameters. Conclusions. The use of the proposed approach to the formation of technological process management at mining enterprises of the Kryvyi Rih iron ore basin will improve the quality of iron ore concentrate supplied to metallurgical processing, increase the productivity of technological units and reduce energy consumption.

Distributed Consensus Control for Discrete-Time T-S Fuzzy Multiple-Agent Systems Based on an Unknown Input Observer.

This paper investigates a consensus problem for a class of T-S fuzzy multiple-agent systems (MASs) with unknown input (UI). To begin with, an unknown input observer (UIO) is able to asymptotically estimate the system state and the UI is designed for each agent. In order to construct the UIO, the state interval estimation is obtained by first using zonotope theory. Next, using the interval estimation of the state, a correlation of the state and the UI is built. Subsequently, a UIO is constructed, which is proposed by building upon the algebraic relationship. Moreover, by using the estimations of the state and the UI, a distributed control protocol is developed based on the proposed UIO. And, with the proposed distributed control protocol, the T-S fuzzy MAS can achieve consensus, in that all the states of the agents can converge to the leader's state asymptotically. Finally, the effectiveness of the proposed method is demonstrated through two simulation examples.

Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NOx emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.

ASYMPTOTICS OF THE SOLUTION TO A BISINGULAR PROBLEM OF OPTIMAL DISTRIBUTED CONTROL IN A CONVEX DOMAIN WITH A SMALL PARAMETER IN ONE OF THE HIGHER DERIVATIVES

The paper considers the problem of optimal distributed control in a strictly convex planar domain with a smooth boundary and a small parameter in one of the higher derivatives of the elliptic operator. In this problem, a zero Dirichlet boundary condition is imposed, and the control enters additively into the inhomogeneity. The set of admissible controls is the unit ball in the corresponding space of square-integrable functions. The solutions to the resulting boundary value problems are treated in the generalized sense as elements of a certain Hilbert space. The optimality criterion is the sum of the square of the norm of the state deviation from a given state and the square of the norm of the control, with a weighting coefficient. This structure of the optimality criterion allows either the first or the second term to be emphasized, depending on the need. In the first case, achieving the desired state is prioritized, while in the second case, minimizing resource costs becomes more important. The asymptotics of the problem are studied in detail, arising from a second-order differential operator with a small coefficient in one of the higher derivatives, to which a zero-order differential operator is added.

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach

Purpose This paper aims to investigate the attitude synchronization issue of multi-spacecraft formation flying systems under the limited communication resources. Design/methodology/approach The authors propose a distributed learning Chebyshev neural network controller (LCNNC) combining a dynamic event-triggered (DET) mechanism and a learning CNN model to achieve accurate multi-spacecraft attitude synchronization under communication constraints. Findings The proposed method can significantly reduce the internal communication frequency and improve the attitude synchronization accuracy. Practical implications This method requires the low communication resources, has a high control accuracy and is thus suitable for engineering applications. Originality/value A novel DET mechanism-based LCNNC is proposed to achieve the accurate multi-spacecraft attitude synchronization under communication constraints.

Online monitoring and fault early warning prediction method for the operational status of steam turbine sliding pin systems

Abstract In modern power systems, ensuring the safe and reliable operation of the sliding pin system in large steam turbine generator sets is crucial. However, the measurable parameters in the current distributed control system are insufficient for fault early detection of the sliding pin system’s operational state. Additionally, there is a lack of relevant research in this area at present. This paper utilizes a typical 300 MW-class unit as a case study. By analyzing the operational mechanism and fault modes of the sliding pin system, a method for online monitoring of its operational status based on cylinder expansion measurement parameters is proposed. Based on this foundation, taking the advantage of long short-term memory (LSTM) network to effectively extract features from univariate time series, and integrating improved particle swarm optimization (IPSO) for automatic hyperparameter optimization, a multi-step prediction model and fault prediction method for the operational status of sliding pin systems based on IPSO-LSTM is designed. Test results based on various performance evaluation metrics indicate that the IPSO-LSTM algorithm significantly enhances the prediction model’s accuracy. Specifically, the TVIWAC-PSO model, which varies the inertia weight (TVIW) and acceleration coefficients simultaneously in the PSO algorithm, optimizes by enhancing global search in the early stages and emphasizing local search in the later stages of iteration. Furthermore, TVIWAC-PSO demonstrates superior performance in optimizing the hyperparameters of the LSTM algorithm. Finally, based on the gap standard between sliding pins and keyway in the actual operating procedures of the unit, combined with the low-pressure cylinder and rotor expansion difference operation standard, thresholds for anomaly detection and early fault prediction of the sliding pin system’s operational status are provided. This study holds significant engineering application value.

Linear quadratic control and estimation synthesis for multi‐agent systems with application to formation flight

AbstractThis paper concerns the optimality problem of distributed linear quadratic control in a linear stochastic multi‐agent system (MAS). The main challenge stems from MAS network topology that limits access to information from non‐neighbouring agents, imposing structural constraints on the control input space. A distributed control‐estimation synthesis is proposed which circumvents this issue by integrating distributed estimation for each agent into distributed control law. Based on the agents' state estimate information, the distributed control law allows each agent to interact with non‐neighbouring agents, thereby relaxing the structural constraint. Then, the primal optimal distributed control problem is recast to the joint distributed control‐estimation problem whose solution can be obtained through the iterative optimization procedure. The stability of the proposed method is verified and the practical effectiveness is supported by numerical simulations and real‐world experiments with multi‐quadrotor formation flight.

Flexible linear clock–based distributed self-triggered active power-sharing secondary control of AC microgrids

Flexible linear clock–based distributed self-triggered active power-sharing secondary control of AC microgrids

Dimensionality reduction of high-solid anaerobic digestion flow pattern: Flow velocity distribution model and control strategy

Dimensionality reduction of high-solid anaerobic digestion flow pattern: Flow velocity distribution model and control strategy

Adaptive bipartite time-varying formation tracking control for heterogeneous multi-agent systems with DoS attacks.

Denial-of-service (DoS) attacks and antagonistic interactions may exist in complex networks, which will destroy cooperative communication between agents and thus cannot realize collaborative tasks. Therefore, this paper studies time-varying formation tracking (TVFT) of heterogeneous multi-agent systems (HMASs) with DoS attacks and cooperative-antagonistic interactions. It aims to ensure system communication connectivity and allow followers to achieve distributed secure bipartite TVFT. To enable followers to successfully obtain unknown state of the leader, this paper designs a composite adaptive dynamic event-triggered (DET) bipartite compensator. Compared with existing compensators, it can resist DoS attacks; obtain information of a non-autonomous leader; increase the event triggering interval; and be independent of global information of signed digraph. Based on proposed compensator, a new distributed formation controller is designed for achieving TVFT. The results show that sufficient conditions for realizing secure bipartite TVFT of HMASs under DoS attacks and feasibility conditions for realizing time-varying formations are obtained. Meanwhile, simulations are performed to test validity of the compensator and controller.