Distributed Control Theory Research Articles

Partial component consensus in mean‐square for delayed nonlinear multi‐agent systems with uncertain nonhomogeneous Markov switching topologies and aperiodic denial‐of‐service cyber‐attacks

The partial component consensus in mean‐square for delayed nonlinear multi‐agent systems (NMASs) with uncertain nonhomogeneous Markov switching (UNMS) topologies subjected to aperiodic denial‐of‐service (DoS) cyber‐attacks is investigated. Firstly, the partial component ( ‐dimensional) consensus of the system ( ‐dimensional, ) is considered in this paper. When , the partial component consensus degrades into identical consensus. Secondly, the communication topologies governed by uncertain nonhomogeneous Markov chains are random. Thirdly, the communication topologies suffer from aperiodic DoS cyber‐attacks. Then, in view of stochastic analysis technology, distributed control theory and Lyapunov stability theory, the partial component consensus conditions are obtained using permutation matrix and inequality scaling skills. Eventually, the correctness of the results is verified through an example.

A Unified Distributed Digital Control Architecture for Secondary Control in Islanded Microgrids

Distributed control theory has gaining momentum for the global stability of modern Micro-grids (MGs), which can be hence regarded as a cyber-physical energy systems in a networked control systems perspective. Besides the benefits, the control over networks poses several issues and, among them, how to preserve stability despite the unavoidable sampling process is one of the most crucial to be addressed for the implementation in real-world scenarios. The paper addresses this concern by introducing a unified digital control architecture for secondary control of inverter-based MGs working in islanded mode, where both frequency and voltage regulations are ensured via fully-distributed sampled-data control protocols. By choosing a small-enough sampling period h > 0, the proposed strategies can ensure a reduction of the communication burden by avoiding a continuous interactions among electrical entities, thus enhancing the efficiency of the MG. Exponential stability of frequency and voltage closed-loop systems is analytically proven via Lyapunov-Krasovskii theory. The derived stability conditions are in the form of Linear Matrix Inequalities (LMIs) whose solution provides the maximum sampling period preserving the global stability. Simulation results, carried out in MATLAB/SimPower Systems, confirm the theoretical derivation.

Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control.

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments.

Optimal Distributed ADMM-Based Control for Frequency Synchronization in Isolated AC Microgrids

To restrict transient dynamics of each distributed generator (DG) in isolated AC micro-grids (MGs), an optimal distributed alternating direction method of multiplier (ADMM)-based control scheme, will be explored for achieving frequency synchronization within a given finite horizon. By introducing the consensus variable, we re-phrase the frequency synchronization problem as a linear quadratic tracking problem under the framework of multi-agent systems. The objective function of each individual DG is defined as the accumulation of quadratic frequency errors and quadratic inputs. Since the consensus variable is also part of the decision variable, more flexibility can be gained in developing the optimal distributed ADMM-based algorithm within a finite horizon. Since the direct extension of ADMM for static optimization can not be fully distributed, a fully distributed ADMM-based control is developed by exploring the optimal distributed control theory in each DG. To validate the performance of the proposed method, real-time simulations on OPAL-RT are conducted to validate the effectiveness of the proposed optimal distributed ADMM-based control strategy even under large load variations and plug-and-play operations of DGs.

Consensus of NMASs with MSTs subjected to DoS attacks under event-triggered control

The leader-following consensus (LFCs) of nonlinear multi-agent systems (NMASs) with Markov switching topologies (MSTs) subjected to denial-of-service (DoS) attacks under event-triggered (ET) control is studied. An ET strategy is applied to reduce unnecessary signal transmission among agents, save network resources, and ensure systems performance. As a result of the open communication network among agents, it is inevitably subjected to attacks that leads to changing in the communication topologies. The communication topologies among agents are modeled as MSTs, and the transfer rates (TRs) are assumed to be partially unknown. DoS attacks are the most common attacks due to their destructive, stealthy, and easy implementation, so the network attacks considered in this paper are DoS attacks. Based on the distributed control theory and Lyapunov stability theory, the Lyapunov direct method and stochastic analysis method are used to explore sufficient conditions for the systems to achieve LFCs. Finally, an example is provided to verify the effectiveness of the methods and the correctness of the results.

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Survey/review study Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications Qichun Zhang 1, and Yuyang Zhou 2,* 1 Department of Computer Science, University of Bradford, Bradford BD7 1DP, United Kingdom 2 School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, United Kingdom * Correspondence: y.zhou@napier.ac.uk Received: 24 October 2022 Accepted: 18 November 2022 Published: 22 December 2022 Abstract: Non-Gaussian randomness widely exists in complex dynamical systems, in which the traditional mean-variance index cannot fully reflect the systematic characteristics. To improve the performance of control design subjected to non-Gaussian noises, stochastic distribution control (SDC) theory was proposed in the 1990s, where the output probability density function (PDF) has been investigated as an additional system variable. Following this framework, SDC has been extended to other research subjects in control systems such as filter design, fault diagnosis, and so on. It shows that SDC supplies an important solution to enhance the accuracy of system design, which is further beneficial to almost all the topics subject to non-Gaussian randomness. Meanwhile, the theoretical results of the SDC have been applied to several practical industrial applications. As data science raises based on the development of industrial artificial intelligence, SDC has been further developed recently focusing on data-driven design and multi-agent systems. To explore the new challenges with the evolution of SDC, e.g. unknown system models, unknown noise distributions, strong non-stationary transient dynamics, stability analysis and industrial applications, this survey summarises the most recent published results in the last 5 years of SDC work in terms of modelling, control, filtering, fault diagnosis, and industrial applications. Based on the technical analysis, potential future work is discussed in the end.

Distributed Predefined-Time Fractional-Order Sliding Mode Control for Power System With Prescribed Tracking Performance

In this paper, a distributed predefined-time fractional-order sliding mode controller (DPTFOSMC) is proposed for performance enhancement of power system. The proposed DPTFOSMC is designed based on the multi-agent framework and distributed control theory, which can reduce the pressure of central controller in communication and calculation, thus obtaining fast and coordinated response. The DPTFOSMC can achieve the advantages of SMC with fast response and strong robustness for the power system. In addition, the power system can be stabilized in stable range within a predefined time under the DPTFOSMC, and the upper limited convergence time is directly equal to the tunable parameter, thus meeting the high requirement for convergence time of the power system. The prescribed performance function (PPF) is carried out to design the DPTFOSMC, which can maintain that the power system can obtain the expected transient responses. Meanwhile, fractional calculus is applied to devise the DPTFOSMC to eliminate chattering phenomenon of conventional SMC. The Lyapunov function is adopted to analysis the predefined-time stability of the power system under the proposed DPTFOSMC. Simulation are taken to validate the effectiveness of the DPTFOSMC. The results indicate that the power system under the DPTFOSMC can achieve superior performances than the existing control methods.

Cooperative transportation: realizing the promises of robotic networks using a tailored software/hardware architecture

Abstract With cooperative transportation, the paper looks at a demanding problem from distributed robotics. At its heart, the proposed transportation scheme uses distributed model predictive control. Yet, distributed control alone does not suffice to solve the task. Thus, also distributed organization, a custom software architecture, simulation, and custom robotic hardware are dealt with, bridging the gap between distributed control theory and practical robotics. The robots are enabled to transport arbitrarily-shaped objects, automatically adapting to changing circumstances and numbers of robots.

Unified Distributed Control of Battery Storage With Various Primary Control in Power Systems

Distinct characteristics of transmission and distribution power grids typically necessitate different control algorithms, especially at the primary control level where quick responses are required. Accordingly, various droop control schemes have been developed due to specific types of power grid impedances. Therefore, controllers at higher levels often have to be redesigned whenever a different power grid is considered, hence, incurring higher cost and effort. As a way to overcome that problem, in this work, a unified secondary controller using distributed control theory is designed for different droop schemes associated with distributed battery storage in different grid conditions. The control design follows the consensus theory, originally designed for multi-agent systems, to control the frequency and voltage at the point of common coupling (PCC), synchronize energy levels, and proportionally share active and reactive powers of battery storage systems. A sufficient condition for the upper bound of communication delays between storage systems is derived to ensure system stability. Several scenarios are studied using a modified IEEE 118-bus benchmark to support the theoretical results of the proposed approach.

Efficient Algorithm for the Computation of the Solution to a Sparse Matrix Equation in Distributed Control Theory

In this short communication, an algorithm for efficiently solving a sparse matrix equation, which arises frequently in the field of distributed control and estimation theory, is proposed. The efficient algorithm stems from the fact that the sparse equation at hand can be reduced to a system of linear equations. The proposed algorithm is shown to require significantly fewer floating point operations than the state-of-the-art solution. The proposed solution is applied to a real-life example, which models a wide range of industrial processes. The experimental results show that the solution put forward allows for a significant increase in efficiency in relation to the state-of-the-art solution. The significant increase in efficiency of the presented algorithm allows for a valuable widening of the applications of distributed estimation and control.

A cooperative bat searching algorithm with application to model predictive control

In this paper, a cooperative bat searching algorithm (CBA) is proposed by using a communication topology to share information among all the bats in bat algorithm (BA). Inspired by the cooperation mechanism in the distributed control theory, a cooperative term is added to the original BA to accelerate the searching process. The convergence issue is rigorously studied for CBA by using the Jury’s test. Moreover, numerical evaluation is conducted to compare CBA with other variants of BA by solving fifteen benchmark functions from IEEE congress on evolutionary computation. The results are provided to demonstrate the effectiveness of the proposed CBA. As an application, CBA and binary CBA are equipped as the real-time optimizers in a networked model predictive control strategy to solve a balanced coordination problem. The proposed CBA showed competitive performance.

Research on Key Technologies of Multi-smart-agents Based Partially Distributed Control System for Aero Engine

Abstract In this paper, the research status of distributed control system of aero engine were briefly analyzed. It is reasonable to believe that the studies of distributed control system of aero engine are at the transition phase from theoretical research to engineering application with the development of related theories, yet there still exists problems such as the intelligence of the system still needs to be improved, and the lack of standardized interface which leads to diversification and complexity of equipment. Aiming at the problems mentioned above and taking a domestic turboprop engine as the research object, a multi-smart-agents based partially distributed control system architecture is proposed and then the function division and data transmission are described. The practical method of the concept of “smart” in the architecture of partial distributed control system is explored in order to give full play to the advantages of distributed control system. The results show that the structure of the partially distributed control system based on multi-smart-agents is feasible, which provides a reference for the further research of the distributed control theory of aero engine.

Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

This work investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.

Optimal topology for consensus using genetic algorithm

In the Multi-Agent Systems (MAS), graph network topologies play a crucial role in building consensus among the connected agents. Consensus may be achieved on many network graphs using distributed control theory. However, the optimal network topology is not addressed in most of the literature, which is an important part of building stable consensus among networked agents. In this paper, the optimal topology is obtained irrespective of the agent dynamics by using two-dimensional Genetic Algorithm (GA), which is a new approach in this context. Simulation result for agents with first, and second-order linear dynamic is obtained. These results show that the proposed method achieves consensus using the optimal network topology satisfactorily.

Distributed control architecture synthesis for integrated process networks through maximization of strength of input–output impact

Distributed control offers practical trade-off between globally (plant-wide) valid, computationally expensive centralized and locally effective decentralized architectures. While the development of distributed control theory (algorithms, stability, convergence properties, etc.) has made a significant progress in the last decade, decomposition of a system into an optimal distributed structure (classification of controller variables) has received considerably less attention. This paper focuses on achieving such a decomposition for integrated process systems with the objective of maximizing the strength of input–output impact and thus reducing the strength of inter-controller interactions. A graph-theoretic framework is used to abstract input-state-output dependence of a dynamical system and control-relevant interactions are quantified using weighted digraphs. It is shown that community detection in this weighted digraph is equivalent to distributed structure corresponding to strong input–output impact (quantified through the corresponding gain). Two relevant example systems are considered to illustrate the proposed framework and it is shown that the optimal distributed architectures adhere to the underlying structure of strong and weak inter-variable interactions.

Recent advances in control algorithms for smart structures and machines

AbstractThis paper presents a state‐of‐the‐art review of recent control algorithms used primarily for vibration control of smart structures and machines. Control algorithms can be divided into open loop, feedback, feedforward, and combined feedback and feedforward control. Solutions proposed can be subdivided into (a) classical control, (b) optimal control, (c) robust control, (d) intelligent control, and (e) self‐sustaining solutions and energy harvesting used in centralized and decentralized configurations. The most recent research on novel autonomous control systems intersects distributed control theory, agent‐based modeling, bioinspired computational learning, and optimization algorithms.

Modeling for output fiber length distribution of refining process using wavelet neural networks trained by NSGA II and gradient based two-stage hybrid algorithm

The chemi-thermo-mechanical pulping (CTMP) is an important process that produces fibers for paper-making, whose output fiber length distribution (FLD) directly determines the energy consumption and product quality of the subsequent papermaking production. Therefore, study on modeling and control of the output FLD is essential for improving pulp quality and saving energy in refining process. However, the output FLD of refining process has non-Gaussian stochastic distribution property, making it difficult to use conventional methods to establish the output FLD model effectively. Under the framework of stochastic distribution control theory, this paper presents a novel modeling approach for output probability density function (PDF) of fiber length in CTMP by combining with the improved wavelet neural network (WNN). In this context, the square root B-spline approximation principle is firstly adopted to extract the B-spline weights of fiber length PDF shape as the target outputs for the WNN based B-spline weights model. Secondly, a novel two-stage hybrid learning algorithm is proposed to establish the parameters system for the WNN based weighs model by defining a multi-objective evaluation index for modeling accuracy in advance. This learning algorithm integrates multi-objective NSGA II algorithm in the first stage for better initial solutions at a global scope, and gradient descent method is employed in the second stage for accurate solutions of WNN model parameters inside a local range. As a result, the final output PDF of fiber length is reconstructed by the estimated B-spline weights using the B-spline approximation principle again. Experiments using actual industrial data have demonstrated the superiority and practicability of the proposed method.

Key issues and technical route of cyber physical distribution system

Relying on the National High Technology Research and Development Program, this paper introduced the key issues in Cyber Physical Distribution System (CPDS), mainly includes: composite modelling method and interaction mechanism, system planning method, security defence technology, distributed control theory. Then on this basis, the corresponding technical route is proposed, and a more detailed research framework along with main schemes to be adopted is also presented.

Modeling and Control of the Precision for the Building Energy Consumption Monitoring System

Building energy conservation has become to be one of the major tasks of promoting energy conservation and raise the efficiency of energy consumption. Meters are the source of data, it is important to study data precision model based on the accuracy of the error analysis method. This paper firstly introduces error analysis, error distribution and error control theory, then take electric and heat meters for example to describe the error analysis and error distribute methods. The results show that when the precision level of voltage is 0.2, current is 0.2 and power factor is 0.5, the maximum relative error of electrical quantity can be controlled less than 1%, meet the requirement of measurement. The total relative error of heat meter is inverse proportion with temperature difference of sensor and water flow, and when the temperature difference remains unchanged, the error of heat meter trends to main steady if the instant flow of water more than half of the normal flow value.

Distributed vibration control of tensegrity structure

Distributed control is a promising method for large-scale interconnected systems that are composed of several subsystems. This paper considers the design of distributed active vibration control for tensegrity structures. Based on the distributed control theory, the simplified subsystem model of the tandem interconnected system is proposed. A control strategy combining the distributed control with a μ synthesis method is developed for controlling the three-stage tensegrity structure. The simulation results show that the designed controller makes the closed-loop system stable and can suppress disturbance. The disturbance response and local control effect are improved.