Hierarchical Distributed Control Research Articles

A Distributed Control Architecture for Cascaded H-Bridge Converter With Integrated Battery Energy Storage

Cascaded H-bridge topology has been used in grid-tied converters for battery energy storage system due to its modular structure. To fully utilize the converter's modularity, this article proposes a hierarchical distributed control architecture that consists of primary control, secondary control, and battery state-of-charge (SOC) balancing control. Primary control ensures accurate current tracking, whereas a distributed secondary control based on consensus algorithm is presented to regulate power sharing among modules and is proved to be stable theoretically. A distributed SOC balancing control is further introduced to improve energy efficiency of battery energy storage system. Finally, the hierarchical distributed control strategy is implemented using hardware controllers and a software platform. Besides, a carrier phase shift control is also implemented to achieve multilevel output voltage and harmonic reduction. The experimental results demonstrate the performance of the proposed control scheme effectively.

Sensitivity-based hierarchical distributed model predictive control of nonlinear processes

Hierarchical distributed model predictive control (HDMPC) is a promising control framework for industrial processes. With the development of efficient solvers for large-scale nonlinear programming (NLP), the implementation of hierarchical distributed control with nonlinear models becomes achievable. However, there is a lack of systematic method that handles the online computational delay in HDMPC which may lead to deterioration of performance or stability. To speed up online computation, an NLP sensitivity-based HDMPC algorithm is proposed in this paper. The implementation strategy is divided into background and online stages. All the local MPC controllers solve their sub-optimization problems iteratively in background based on the predicted future state, and a sensitivity update step is performed online to correct the predicted optimal inputs. The system-wide sensitivity equation is formulated in the upper control level by combining the optimality information of local controllers. The optimality and stability analysis for the proposed method is given. Three case studies are presented to demonstrate the controller performance.

Coordinated Frequency Control Strategy with the Virtual Battery Model of Inverter Air Conditionings

Due to the potential of thermal storage being similar to that of the conventional battery, air conditioning (AC) has gained great popularity for its potential to provide ancillary services and emergency reserves. In order to integrate numerous inverter ACs into secondary frequency control, a hierarchical distributed control framework which incorporates a virtual battery model of inverter AC is developed. A comprehensive derivation of a second-order virtual battery model has been strictly posed to formulate the frequency response characteristics of inverter AC. In the hierarchical control scheme, a modified control performance index is utilized to evaluate the available capacity of traditional regulation generators. A coordinated frequency control strategy is derived to exploit the complementary and advantageous characteristics of regulation generators and aggregated AC. A distributed consensus control strategy is developed to guarantee the fair participation of heterogeneous AC in frequency regulation. The finite-time consensus protocol is introduced to ensure the fast convergence of power tracking and the state-of-charge (SOC) consistency of numerous ACs. The effectiveness of the proposed control strategy is validated by a variety of illustrative examples.

Distributed control for a multi-evaporator air conditioning system

An autonomous hierarchical distributed control (AHDC) strategy is proposed for a building multi-evaporator air conditioning (ME A/C) system in this paper. The objectives are to minimize peak demand and energy costs, and to reduce communication resources, computational complexity and conservativeness while maintaining both thermal comfort and indoor air quality (IAQ) in acceptable ranges. The building consists of multiple connected rooms and zones. The proposed control strategy consists of two layers. The upper layer is an open loop optimizer, which only collects local measurement information and solves a distributed steady state resource allocation problem to autonomously and adaptively generate reference points, for low layer controllers. This is achieved by optimizing the demand and energy costs of a multi-zone building ME A/C system under a time-of-use (TOU) rate structure, while meeting the requirements of each zone’s thermal comfort and IAQ within comfortable ranges. The lower layer also uses local information to track the trajectory references, which are calculated by the upper layer, via a distributed model predictive control (DMPC) algorithm. The control strategy is distributed at both layers because they use only local information from the working zone and its neighbors. Simulation results are provided to illustrate the advantages of the designed control schemes.

Multiple sampling PSC‐PWM with hierarchical control architecture for MMC‐DSTATCOM

The modular multilevel converter (MMC) is attractive for medium- and high-power applications because of its high modularity, availability and power quality. In this study, a quadruple sampling phase-shifted-carrier pulse-width modulation (PSC-PWM) strategy based on hierarchical distributed control architecture for MMC in medium-voltage power distribution static synchronous compensator (DSTATCOM) application is presented. Compared to centralised control architecture, the multi-digital signal processors master-slave hierarchical control architecture can enhance the scalability of MMC-DSTATCOM and the sub-module (SM) number can expand without limitation. To take advantage of the proposed hierarchical architecture, the multiple sampling PSC-PWM technique is employed to improve the converter performance. The spectrum of the electromotive force of MMC with quadruple sampling is almost the same as that of natural sampling, which leads to fewer harmonics in reactive and harmonic current compensating. Besides, it also raises the sampling frequency so that the MMC-DSTATCOM can compensate harmonic at the low SM switching frequency. The effectivenesses of the proposed architecture and modulation method are verified by experimental results obtained from a 60 V/2 kVA downscaled prototype.

Hierarchical Distributed Control for Global Network Integrity Preservation in Multirobot Systems.

In this paper, we address a novel hierarchical distributed control (HDC) strategy for networked multirobot systems (MRSs). This strategy is developed on a geometric approach without requiring estimation of algebraic connectivity. It is originally based upon behavioral control, but upgraded by distributed node control with a mobility constraint for global network integrity preservation and distributed connectivity control with a local connectivity minimization strategy for network coverage expansion. Thanks to properties of HDC, a networked MRS is capable of achieving high performance with cooperative tasks. We have examined and evaluated our proposed method in both simulations with up to 100 simulated robots and real-world experiments with up to 14 real robots.

Distributed Control of Nonlinear Multiagent Systems With Unknown and Nonidentical Control Directions via Event-Triggered Communication.

In this paper, the leader-following output consensus problem for a class of uncertain nonlinear multiagent systems with unknown control directions is investigated. Each agent system has nonidentical dynamics and is subject to external disturbances and uncertain parameters. The agents are connected through a directed and jointly connected switching network. A novel two-layer distributed hierarchical control scheme is proposed. In the upper layer, to save the communication resources and to handle the switching networks, an event-triggered communication scheme is proposed, and a Zeno-free event-triggered mechanism is designed for each agent to generate the asynchronous triggering time instants. Furthermore, to avoid the continuous monitoring of the system states, a Zeno-free self-triggering algorithm is proposed. In the lower layer, to handle the unknown control directions problem and to achieve the output tracking of the local references generated in the upper layer, the Nussbaum-type function-based technique is combined with internal model principle. With the proposed two-layer distributed hierarchical controller, the leader-following output consensus is achieved. The obtained result is further extended to the formation control problem. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

Hierarchical Distributed Control Strategy for Electric Vehicle Mobile Energy Storage Clusters

The stability problem of the power system becomes increasingly important for the penetration of renewable energy resources (RESs). The inclusion of electric vehicles (EVs) in a power system can not only promote the consumption of RESs, but also provide energy for the power grid if necessary. As a mobile energy storage unit (MESU), EVs should pay more attention to the service life of their batteries during operation. A hierarchical distributed control strategy was proposed in this paper for mobile energy storage clusters (MESCs) considering the life loss of each EV’s battery. This strategy was divided into a two-layer control structure. Firstly, numerous EVs were divided into different clusters according to their regional relationships. The lower layer adopted a distributed collaborative control approach for allocating energy among EVs in the cluster. Under this condition, an aggregate EVs response model was established and the characteristic of the MESC was analyzed. Secondly, the upper layer applied the multi-agent consensus algorithm to achieve the optimal allocation among different clusters. Therefore, the control strategy realized the two-way communication of energy between EVs and the power grid, and ensured the optimal economical dispatch for the mobile energy storage system (MESS). Finally, the simulation of testing examples verified the effectiveness of the proposed strategy.

Hierarchical Distributed Coordinated Control Strategy for Hybrid Energy Storage Array System

A hierarchical distributed control structure is proposed for the optimal operation of a hybrid energy storage array system (HESAS) composed of multiple battery units and supercapacitor units. A grouping control strategy is established for multiple battery units. The upper layer is the coordinated control layer. To realize the coordinated and safe operation of a battery energy storage system (BESS) and supercapacitor energy storage system (SCSS), a two time-scale optimization model based on model predictive control (MPC) is proposed. The long time-scale model provides reference values for the short time-scale model, whereas the short time-scale model optimizes the real-time power demand based on the reference values to ensure that the state of charge and power of the BESS and SCSS are within the safe operation region. The lower layer is the distributed control layer. To quickly decompose the power commands sent by the upper layer, the weighted discrete consensus algorithm based on MPC is used to realize the adaptive allocation of power between the units with the same energy storage. The convergence speed of the proposed algorithm is improved by shrinking the weighted matrix. The results show that the proposed control structure, model, and grouping strategy have certain advantages in terms of control performance, convergence speed, and in extending battery life.

Automatic supervisory control for the self‐healing of smart grids based on colored Petri nets

Power transmission lines of traditional power transmission systems (PTSs) may suffer from some faults that can lead to blackouts. This work proposes a methodology to construct hierarchical distributed control systems (DCSs) for the automatic fault detection and restoration of PTSs using colored Petri nets (CPNs). If faults occur in power transmission lines, they can be detected by supervisors, and local solutions can be computed to restore the faults. Each supervisor is coordinated by a coordination protocol to perform the computation of local solutions. The structures of traditional PTSs are simplified and defined by CPNs, and the derivation processes of fault detection and restoration based on the proposed coordination protocol are verified by the mathematical methods of CPNs. Finally, the complexity of the proposed hierarchical DCS and coordination protocol is discussed and an example is given to illustrate the proposed method. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Hierarchical distributed control for decentralized battery energy storage system based on consensus algorithm with pinning node

A decentralized battery energy storage system (DBESS) is used for stabilizing power fluctuation in DC microgrids. Different state of charge (SoC) among various battery energy storage units (BESU) during operation will reduce batteries’ service life. A hierarchical distributed control method is proposed in this paper for SoC balancing and power control according to dispatching center requirement in DBESS. A consensus algorithm with pinning node is employed to allocate power among BESUs in the secondary control whereas in the primary control, the local controller of BESU adjusts output power according to the reference power from secondary control. Part of BESUs are selected to be pinning node for accepting command from dispatching center while other BESUs as following nodes which exchange output power and SoC information with the adjacent nodes through communication network. After calculating reference power of each BESU by adopting consensus algorithm, the power sharing in DBESS is achieved according to their respective SoC of BESUs. Meanwhile, the total output power of DBESS follows the varying requirements of dispatching center. The stability of DBESS is also improved because of having no center controller. The feasibility of the proposed control strategy is validated by simulation results.

Distributed secondary and tertiary controls for I–V droop‐controlled‐paralleled DC–DC converters

A hierarchical distributed control method for I-V droop-controlled-paralleled DC-DC converters in DC microgrid is proposed. The control structure includes primary, secondary, and tertiary levels. The secondary control level is used to remove the DC voltage deviation and improve the current sharing accuracy. An improved dynamic consensus algorithm is used in the secondary control to calculate the average values of bus voltage and voltage restoration in distributed control. In the tertiary control level, as the main contribution in this study, the system conversion efficiency is enhanced by using the average restoration value obtained in the secondary control level, instead of using the total load current which needs more communication traffic. When the converters are connected to batteries, the method for the state of charge (SoC) management is proposed so that the SoC balance can be guaranteed. The effectiveness of the proposed method is verified by detailed experimental tests based on four 0.7 kW DC-DC converters.

Distributed Power Control Network and Green Building Test-Bed for Demand Response in Smart Grid

It is known that demand and supply power balancing is an essential method to operate power delivery system and prevent blackouts caused by power shortage. In this paper, we focus on the implementation of demand response strategy to save power during peak hours by using Smart Grid. It is obviously impractical with centralized power control network to realize the real-time control performance, where a single central controller measures the huge metering data and sends control command back to all customers. For that purpose, we propose a new architecture of hierarchical distributed power control network which is scalable regardless of the network size. The sub-controllers are introduced to partition the large system into smaller distributed clusters where low-latency local feedback power control loops are conducted to guarantee control stability. Furthermore, sub-controllers are stacked up in an hierarchical manner such that data are fed back layer-by-layer in the inbound while in the outbound control responses are decentralized in each local sub-controller for realizing the global objectives. Numerical simulations in a realistic scenario of up to 5000 consumers show the effectiveness of the proposed scheme to achieve a desired 10% peak power saving by using off-the-shelf wireless devices with IEEE802.15.4g standard. In addition, a small scale power control system for green building test-bed is implemented to demonstrate the potential use of the proposed scheme for power saving in real life.

Solution of Operation, Planning and Control Problems of Large Power Systems in Distributed Control System

In this paper the functional modeling (FM) method developed for efficient solution of simulation, planning and control problems in large power systems and interconnections is presented. According to the FM method a power system is represented by hierarchical model, consisting of systems of equations of subsystems (lower level) and system of equations of higher level, in which only boundary variables of subsystems are present. Combined with Subgramian calculation this approach forms the basis for hierarchical distributed control system.

An Open Architecture Control System for Parallel Kinematic Machines Based on Multi-DSP Parallel Procession

Because of the control complexity of the Parallel Kinematic Machines (PKMs), such mechanisms may suffer from lack of accuracy at high speed. Unfortunately, conventional motion controllers have very limited flexibility because they are designed for Cartesian coordinates. Therefore, the motion control system for PKMs with both high performance and open architecture is urgently demanded. In this paper, an open architecture control system for PKMs based on multi-DSP parallel procession is presented. Then, the hierarchical distributed control strategy is discussed in details. In addition, the realization of an interactive communication interface among DSP processors is presented. According to the testing results, the developed system is capable of obtaining an interpolation sampling period at least 3 times faster than that could be offered by most controllers based on single-DSP, thus it is available for high-speed and high-accuracy control of PKMs.

LP-DMC CONTROL OF A CHEMICAL PLANT WITH INTEGRAL BEHAVIOUR

The purpose of this paper is to describe the implementation of a hierarchical distributed control system for a chemical pilot plant using industrial standard components. The control system consists of three components: regulatory control (mainly PID controllers), multivariable control (Dynamic Matrix Control controller) and the optimization and economic layer (Linear Programming). This work also includes details of the implementation tools: PROFIBUS, LabVIEW and data acquisition modules. The paper concludes with two possible solutions that deal with plants with integral behaviour.

ABLE: A toolkit for building multiagent autonomic systems

This paper describes a toolkit for building multiagent autonomic systems. The IBM Agent Building and Learning Environment (ABLE) provides a lightweight Java™ agent framework, a comprehensive JavaBeans™ library of intelligent software components, a set of development and test tools, and an agent platform. We describe a series of agents built using ABLE components and present three case studies of applications using the ABLE toolkit. The Autotune agent is a closed-loop controller agent that supports hierarchical distributed control. The Subsumption agent defines specific behaviors or strategies and can be plugged into a multiagent subsumption infrastructure. The Autonomic agent architecture features sensors and effectors for interacting with the external environment, layers of reflexive, reactive, and adaptive subsumption agents, components that dynamically model the autonomic system itself and its environment, and components for emotions, planning, and executive-level decision-making. By using the ABLE component library to build agents running on the ABLE distributed agent platform, we discuss how we can incrementally add new behaviors and capabilities to intelligent, autonomic systems.

By how much should a general purpose programming language be extended to become a multi-robot system programming language?

The paper gives the answer to the question formulated in the title for the case of manipulator-level languages [1]. Theoretical considerations show that regardless of the type and number of robots and cooperating devices used in the system as well as irrespective of the number and kind of sensors included in the system, a general purpose language has to be extended by a single instruction with a rather complex semantics. Due to this complexity it is more convenient to introduce two, much simpler instructions. The paper also presents the method of implementing those instructions in a hierarchical distributed control system. The presented approach has been used in the implementation of MRROC/MRROC++ robot programming libraries/languages. The programmer uses predefined library modules to construct the controller structure solving a specific multi-robot task. The structure is fine-tuned to the task at hand by supplying adequate motion generators in a plug-in fashion. The practical validity of the formal approach followed in the implementation of MRROC and MRROC++ has been positively verified on diverse robotic tasks.

The impact of job memory requirements on gang-scheduling performance

Almost all previous research on gang-scheduling has ignored the impact of real job memory requirements on the performance of the policy. This is despite the fact that on parallel supercomputers, because of the problems associated with demand paging, executing jobs are typically allocated enough memory so that their entire address space is memory-resident. In this paper, we examine the impact of job memory requirements on the performance of gang-scheduling policies. We first present an analysis of the memory-usage characteristics of jobs in the production workload on the Cray T3E at the San Diego Supercomputer Center. We also characterize the memory usage of some of the applications that form part of the workload on the LLNL ASCI supercomputer. Next, we examine the issue of long-term scheduling on MPPs, i.e., we study policies for deciding which jobs among a set of competing jobs should be allocated memory and thus should be allowed to execute on the processors of the system. Using trace-driven simulation, we evaluate the impact of using different long-term scheduling policies on the overall performance of Distributed Hierarchical Control (DHC), a gang-scheduling policy that has been studied extensively in the research literature.

An Object Oriented Modeling Approach to Intelligent Variable Geometry Trusses

Since an intelligent variable geometry truss (IVGT) is usually equipped with large numbers of redundant actuators for the sake of providing adaptivity, a distributed control is considered a promising approach to dealing with its motion control. In this case, a modeling approach and an integrated model is a key to constructing distributed control systems from both hardware and software. To this end, this paper proposes an object-oriented modeling approach. Through decomposing the control problem domain underlying VGTs into objects, an object model can be obtained to represent various object abstractions and the relationships among these objects. It is shown in this paper that an object model of VGTs is exactly the integrated model for their knowledge organizations as well as the control architectures. Two control schemes resulting from the object models-a hierarchical distributed control and an autonomous distributed control-are discussed in particular. To verify the concepts addressed in this paper, an experimental prototype of IVGTs was manufactured, with a two-level hierarchical distributed control system equipped. With this prototype, an experiment is carried out, simulating a self-construction of space structures with IVGTs in a null gravity space.