Distributed Control Method Research Articles

Model Free Adaptive Iterative Learning Consensus Tracking Control for a Class of Nonlinear Multiagent Systems

This paper proposes a distributed model free adaptive iterative learning control (MFAILC) method for a class of unknown nonlinear multiagent systems to perform consensus tracking. Here, both fixed and iteration-varying topologies are considered and only a subset of followers can access the desired trajectory in each topology. To design the control protocol, the agent’s dynamic is first transformed into a dynamic linearization model along the iteration axis, and then a distributed MFAILC scheme is constructed to guarantee that all agents can track the desired trajectory. Through rigorous analysis, it is shown that under this novel distributed MFAILC scheme, the tracking errors of all agents are convergent along the iteration axis. The main merit of this design is that consensus tracking task can be achieved only utilizing the input/output data of the multiagent system. Three examples are given to validate the effectiveness of the proposed design.

Global Adaptive Fuzzy Distributed Tracking Control for Interconnected Nonlinear Systems With Communication Constraints

This paper investigates the adaptive distributed tracking control problem for a class of interconnected nonlinear systems with communication constraints where the subsystems are allowed to exchange output information only when the tracking error exceeds a given threshold. Considering the communication constraints problem, a novel adaptive distributed control method is developed. In this framework, a fuzzy adaptive controller is designed within a given compact set to compensate the unknown mismatching interconnections. Specially, to achieve the global tracking performance, a robust controller is activated outside the compact set to pull back the system states. By using the Lyapunov stability theory and the $\sigma$ -modification technique, it is proven that the tracking errors are globally asymptotically convergent and all the signals in the resulting closed-loop system are bounded. Finally, the effectiveness of the proposed method is demonstrated by two examples.

Combining Distributed Consensus with Robust H∞-Control for Satellite Formation Flying

Control methods that guarantee stability in the presence of uncertainties are mandatory in space applications. Further, distributed control approaches are beneficial in terms of scalability and to achieve common goals, especially in multi-agent setups like formation control. This paper presents a combination of robust H ∞ control and distributed control using the consensus approach by deriving a distributed consensus-based generalized plant description that can be used in H ∞ synthesis. Special focus was set towards space applications, namely satellite formation flying. The presented results show the applicability of the developed distributed robust control method to a simple, though realistic space scenario, namely a spaceborne distributed telescope. By using this approach, an arbitrary number of satellites/agents can be controlled towards an arbitrary formation geometry. Because of the combination with robust H ∞ control, the presented method satisfies the high stability and robustness demands as found e.g., in space applications.

Control Scheme to Achieve Multiple Objectives and Superior Reliability for Input-Series-Output-Parallel LCL-Type Grid-Connected Inverter System

Input-series-output-parallel (ISOP) inverter system is very suitable for high dc input voltage and large ac output current power conversion applications. If this topology is applied to the grid-connected occasion, a bulky-capacity grid-connected inverter can be built with multiple small-capacity standardized inverter modules, which can significantly improve the reliability of the system operation and facilitate system design. In this paper, we investigate an ISOP LCL -type grid-connected inverter system, and put forward an optimized way for the topology of combined LCL filters first. Then, the inverter-side inductor current is carefully chosen as the controlled variable and thus a concise distributed control method is proposed to achieve multiple control objectives. On the basis of the presented distributed control, the redundant control technique is also provided to heighten the reliability of the grid-tied ISOP inverter indeed. Finally, a three-module prototype is built and the experimental results validate the effectiveness of the raised strategy.

Distributed control for large-scale plug-in electric vehicle charging with a consensus algorithm

As the development of plug-in-electric vehicles (PEVs) accelerates, the effective integration of large-scale PEVs with power grid becomes an urgent problem for utilities and PEV users. Mainly considering grid side preference, this paper proposes a distributed control method with a consensus algorithm to deal with large-scale PEV charging coordination. The objectives of the proposed coordination strategy are: minimizing the charging power loss and maximizing the available PEV power for vehicle-to-grid (V2G) services. The incremental cost functions are formulated for these objectives. The graph theory is applied to specify the communication network for PEVs. An interaction rule, the consensus algorithm is applied to realize the local information updating and external information exchanging among neighboring PEVs. A variable-size stochastic matrix is defined to specify the communication topology among PEVs, as well as guarantee the convergence of the consensus targets. The simulation results show that, with limited communication, the large-scale PEV charging/discharging can be efficiently coordinated to reduce total charging power loss or provide V2G service with maximum available PEV power. Meanwhile the PEV charging requirements specified by drivers can be satisfied. In addition, the convergence of the proposed distributed consensus algorithm can always be guaranteed upon random PEV departures.

Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system

This work proposes an adaptive Multi-Input and Single-Output fuzzy controller which is designed in a supervisory manner for a multi-agent system. This paper mainly aims to control the frequency oscillation of each agent and minimize the production cost of the whole interconnected system. In the proposed controller, the load frequency control and management controller loops are integrated into the designing phase. In many parallel works, designing a load frequency controller and managing unit have been carried out separately which leads to disturbance in both outputs. In the present work, the disruption is eliminated by the implementation of the proposed controlling method. To improve the performance of the proposed controller, the key parameters of the controller are tuned using the modified particle swarm optimization algorithm. The designed control system namely “distributed control method” is applied for several independent units (agents). The controller parameters are tuned in a supervisory manner for the considered multi-agent system, where each agent is connected to the adjacent agents. A wide range of operation points is regarded in the designing phase to adjust the parameters in a way that the controller can effectively control the system in the whole of this range.

Battery Energy Storage System Based on Incremental Cost Consensus Algorithm for the Frequency Control

In the past few years, Battery Energy Storage System (BESS) has been found of great potential in supporting the frequency control. Increasing attentions have been given to the control strategy of BESS. In this paper, a distributed control method considering the life-loss cost is proposed for BESS. Based on the multi-agent system, the Incremental Cost Consensus (ICC) algorithm is applied to minimize the life-loss cost of BESS. In order to improve the control performance, parameters of the system are optimized by Genetic Algorithm (GA). The simulation results are provided to verify the effectiveness of the proposed approach.

Adaptive Distributed Control of a Flexible Manipulator Using an Iterative Learning Scheme

This paper presents an adaptive distributed control method based on an iterative learning scheme for a flexible manipulator to realize trajectory tracking and vibration reducing. The dynamical model of the flexible manipulator is captured by partial differential equations (PDEs). The control objective is to design a boundary controller and a distributed controller so that the motion of the flexible manipulator can track a desired position, and the deflection can be suppressed simultaneously subjecting to system parameters uncertainties and spatio-temporal distributed disturbances. A Lyapunov based stability analysis is carried out to achieve stability of the controlled system. Finally, simulation results evaluate the validity of the derived control scheme.

Implementation of Distributed Control of Hierarchical Assembly Systems via Extended Critical Places

Deadlock problem, a research area full of vitality, has been extensively investigated in the framework of Petri nets. Much of the work on deadlock problem mainly focuses on the models with flexible routes, while there are few theoretical results from the models with assembly operations, especially the models with hierarchical assembly operations. In this paper, an innovative distributed control method for assembly systems with complex structures is developed. Before advancing to the next step, a forward search procedure is conducted on the concerned process to check whether there exists a firing sequence from its current place to its nearest global critical place. The movement of its next step is allowed when the search result is positive; otherwise, our method prohibits its one-step movement and the same search method is imposed on the remaining processes until a positive result is returned. During the execution of our control method, because of no demanding of all the states and communication with other unchecked processes, a great reduction of computational complexity can be achieved with our on-line and real-time control strategy.

Consensus-Based Distributed Economic Dispatch Control Method in Power Systems

This paper introduces a new consensus-based control scheme for distributed power systems to solve the distributed economic dispatch (DED) problem. With the development of renewable energy, smart grid, and microgrid, more and more distributed generators will be connected to power systems. Thus the distributed control schemes become more and more important for power systems to control and manage the distributed equipment. Therefore, some consensus-based methods are published previously to solve the DED problem. But the challenge to apply the consensus-based method is to meet the power balance constraint without the centralized facility, since the value of power mismatch is hard to be obtained for a distributed control system. To solve the problem, this paper proposes the method to combine the frequency control methods with the consensus protocol from graph theory to develop the DED control method, so that the real power between the load and the generation can be balanced during the economic dispatch. With this method, a totally distributed optimal control scheme is obtained. The PI frequency controller and neural network frequency controller are employed to develop the DED control method. Then the communication failure problem is considered in this paper. The robust DED control method is proposed to improve the robustness of this distributed control method, allowing the system to stay at the optimal dispatching even if the communication is failed. In addition, the model of transmission line losses is incorporated in the DED problem to reduce the line losses. Finally, several case studies are given in this paper to verify the proposed method.

MAS-based distributed control method for multi-microgrids with high-penetration renewable energy

The distributed control of a multi-microgrid (MMG) system composed of neighboring microgrids (MGs) is much more complex than that of an MG. In this paper, a fully distributed control method with fault tolerance control for MMG systems is proposed, which is a two-layer model, where a communication network composed of agents is the top layer, while an MMG is the bottom layer. Further, a systematic method is presented to obtain a set of distributed control laws for agents from any given communication network. The communication network consists of two types of subgraphs, a within-MG subgraph and a between-MG subgraph. Moreover, the control laws derived from the within-MG subgraph ensure the supply-demand balance and the proportional outputs of distributed generators (DGs) in each MG, while the control laws derived from the between-MG subgraph coordinate MGs to achieve the power balance of the MMG system. Furthermore, two theorems and a proposition are proved, which state the convergence of the derived control laws. Finally, simulations are carried out on the MMG model in MATLAB/Simulink. The results show that the frequencies and voltages in MGs stay at the prescribed values, and the proportional outputs are achieved, when both loads and environmental conditions fluctuate. Furthermore, our distributed method has higher tolerance to the failures of agents, when compared to a centralized method.

Distributed control strategy of hybrid energy storage system in the DC microgrid

In a DC microgrid, because the output of renewable energy such as photovoltaic is intermittent, hybrid energy storage system (HESS) combining ultracapacitors and batteries is usually used to solve this issue. According to the characteristics of ultracapacitors and batteries, the low-frequency power fluctuations are assigned to batteries, while the high-frequency power fluctuations are assigned to ultracapacitors. When considering an ultracapacitor group, state-of-charge (SoC) of the ultracapacitors will be unbalanced between each other, on account of the differences of initial states or attributes. A novel distributed control method is proposed in this study. In general, ultracapacitors control the DC bus voltage and batteries control SoC of the ultracapacitors. Additionally, the distributed control adds the difference between one ultracapacitor's SoC and the average SoC of the ultracapacitor group to its current control loop. As a result, the dynamic stability of SoC among the ultracapacitors can be achieved, and the issues of overcharging and/or over discharging of the ultracapacitors can be avoided. At last, the effectiveness of the proposed control strategy is validated by the simulation model built in MATLAB/Simulink.

Separable and Localized System-Level Synthesis for Large-Scale Systems

A major challenge faced in the design of large-scale cyber-physical systems, such as power systems, the Internet of Things or intelligent transportation systems, is that traditional distributed optimal control methods do not scale gracefully, neither in controller synthesis nor in controller implementation, to systems composed of a large number (e.g., on the order of billions) of interacting subsystems. This paper shows that this challenge can now be addressed by leveraging the recently introduced system-level approach (SLA) to controller synthesis. In particular, in the context of the SLA, we define suitable notions of separability for control objective functions and system constraints such that the global optimization problem (or iterate update problems of a distributed optimization algorithm) can be decomposed into parallel subproblems. We then further show that if additional locality (i.e., sparsity) constraints are imposed, then these subproblems can be solved using local models and local decision variables. The SLA is essential to maintain the convexity of the aforementioned problems under locality constraints. As a consequence, the resulting synthesis methods have $O(1)$ complexity relative to the size of the global system. We further show that many optimal control problems of interest, such as (localized) LQR and LQG, $\mathcal {H}_2$ optimal control with joint actuator and sensor regularization, and (localized) mixed $\mathcal {H}_2/\mathcal {L}_1$ optimal control problems, satisfy these notions of separability, and use these problems to explore tradeoffs in performance, actuator, and sensing density, and average versus worst-case performance for a large-scale power inspired system.

Event-Based Distributed Active Power Sharing Control for Interconnected AC and DC Microgrids

This paper investigates the issue of active power sharing among a cluster of microgrids formed by a set of ac and dc microgrids network-interconnected through a set of interlinking converters (ICs). An event-based distributed consensus control approach is proposed to address this issue, in which each IC is assigned to be an agent that needs only the information of its local and neighboring ICs. We first construct the agent system for each IC and design its consensus control protocol. This control mechanism uses a cooperative approach to indirectly adjust the active power load of individual microgrid and, accordingly, navigate the active power flow among them. Thus active power sharing among the interconnected microgrids can be achieved proportionally. Then, an event-based control scheme is utilized to design the consensus protocol, which can dramatically reduce the communication between ICs. Communication time delays and reactive power support are also considered. The proposed distributed control method allows a sparse communication structure and higher reliability and flexibility operation. Simulation results are presented to demonstrate the proposed control method.

Distributed control of interconnected Chemical Reaction Networks with delay

This paper introduces a control approach for a class of Chemical Reaction Networks (CRNs) that are interconnected through a delayed convection network. First, a control-oriented model is proposed for interconnected CRNs. Second, based on this model, a distributed control method is introduced which assures that each CRN can be driven into a desired fixed point (setpoint) independently of the delay in the convection network. The proposed algorithm is also augmented with a disturbance attenuation term to compensate the effect of unknown input disturbances on setpoint tracking performance. The control design applies the theory of passive systems and methods developed for multi-agent systems. Simulation results are provided to show the applicability of the proposed control method.

Distributed model predictive consensus control for robotic swarm system

This paper proposes a distributed model predictive control method for a consensus of robotic swarm systems. The proposed method is free from any precomputed trajectories, contrast to previous studies. This is huge advantages in cases the system do not work as previously predicted, communication topology changes frequently, and the communication is unreliable. I introduce a concept of local subsystem regulator first, and then the distributed control method and its design concept are described. I also analyze stability of the proposed method and introduce how to handle constraints such as a mutual collision avoidance into the proposed method. Computer simulations are demonstrated to show effectivity of our method.

Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems

How to design a set of optimal distributed load frequency controllers for a multi-area interconnected power system is an important but still challenging issue in the field of modern electric power systems. This paper presents an adaptive population extremal optimization-based extended distributed model predictive load frequency control method called PEO-EDMPC for a multi-area interconnected power system. The key idea behind the proposed method is formulating the dynamic load frequency control issue of each area power system as an extended distributed discrete-time state-space model based on an extended state vector, obtaining a distributed dynamic extended predictive model, and rolling optimization of real-time control output signal by adopting an adaptive population extremal optimization algorithm, where the fitness is evaluated by the weighted sum of square predicted errors and square future control values. The superiority of the proposed PEO-EDMPC method to a traditional distributed model predictive control method, a population extremal optimization-based distributed proportional-integral control algorithm and a traditional distributed integral control method is demonstrated by the simulation studies on two-area and three-area interconnected power systems in cases of normal, perturbed system parameters and dynamical load disturbances.

Automatic train regulation of complex metro networks with transfer coordination constraints: A distributed optimal control framework

In designing the automatic train regulation strategy of metro networks subject to frequent disturbances, it is essential to coordinate the trains at the transfer stations among different lines to facilitate passengers transferring. In this paper, we systematically investigate the distributed optimal control method framework for automatic train regulation of large-scale complex urban metro networks with the transfer coordination constraints. A dynamic train traffic model of metro networks is elaborately developed in the form of the state-space equation. In case frequent disturbances happen, a dynamic optimization problem is developed to minimize the timetable and headway deviations for each line of the metro network under the interaction constraints of different lines on the transfer coordination. By regarding each line as a subsystem of the whole network, the optimization problem is formulated to coordinate a number of subsystems coupled by the state constraints. To satisfy the real-time control requirement, according to the dual decomposition technique, a new distributed optimal control method based on the distributed message passing mechanism is designed, which effectively decomposes the original large-scale problem spatially and temporally into multiple small-scale optimization subproblems that can be computed completely in parallel on a single computing platform to speed up the solution procedure, and thereby reduces the computational burden of centralized implementations for the large-scale urban metro networks. Numerical examples are given to illustrate the effectiveness of the proposed method.

Stochastic Distributed Predictive Tracking Control for Networks of Autonomous Systems With Coupling Constraints

In this paper, we propose a distributed model predictive control method with chance constraints for tracking reference signals. More specifically, considering a network of systems with independent dynamics, the proposed control scheme allows each subsystem to reach feasible setpoints, while respecting local and joint probabilistic constraints. Both a simulation example consisting of an academic temperature control problem and experimental results concerning the motion and coordination of moving agents are illustrated.

A Fully Distributed Approach for Economic Dispatch Problem of Smart Grid

The cooperative, reliable and responsive characteristics make smart grid more popular than traditional power grid. However, with the extensive employment of smart grid concepts, the traditional centralized control methods expose a lot of shortcomings, such as communication congestion, computing complexity in central management systems, and so on. The distributed control method with flexible characteristics can meet the timeliness and effectiveness of information management in smart grid and ensure the information collection timely and the power dispatch economically. This article presents a decentralized approach based on multi agent system (MAS) for solving data collection and economic dispatch problem of smart grid. First, considering the generators and loads are distributed on many nodes in the space, a flooding-based consensus algorithm is proposed to achieve generator and load information for each agent. Then, a suitable distributed algorithm called λ-consensus is used for solving the economic dispatch problem, eventually, all generators can automatically minimize the total cost in a collective sense. Simulation results in standard test cases are presented to demonstrate the effectiveness of the proposed control strategy.