Decentralized Control Scheme Research Articles

Robust multiplexed piecewise affine MPC-based decentralized control of multi-satellite formations using aerodynamic forces

Aerodynamic forces are well suited to be exploited for Low Earth Orbit satellite formation control. However, some critical gaps still exist in control schemes suitable for multi-satellite formation applications, especially when facing atmospheric environmental uncertainties. This paper proposes a scalable and flexible decentralized control scheme for the multi-satellite formation using only aerodynamic forces. This scheme adopts a strategy of updating the control inputs alternatively and is applicable to various formation configurations with different architectures without increasing the computational complexity and communication pressure of each satellite. Aerodynamic forces are changed by reorienting the satellite, and the linear formation control model with pointing angles as inputs is obtained by the PWA (piecewise affine) approximation method. A decentralized control scheme is proposed in which each satellite only has to communicate with its neighboring satellites and update its own inputs. This control scheme allows various generalizations of formation architectures, such as one that includes local chief satellites. To deal with model uncertainties, an improved MPC algorithm named Robust PWA-MMPC is developed using the constraint tightening approach, and then its feasibility and stability issues are analyzed. Hard-in-the-loop simulations are carried out for formation maintenance and reconfiguration, in which aerodynamic force uncertainties are considered.

Harmonic Signature-Based One-Class Classifier for Islanding Detection in Microgrids

This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.

Feasibility Study of a Fully Decentralized Control Scheme for PV Cell-Level Cascaded H-Bridge Inverters

Feasibility Study of a Fully Decentralized Control Scheme for PV Cell-Level Cascaded H-Bridge Inverters

Decentralized Temperature and Storage Volume Control in Multiproducer District Heating

Modern district heating technologies have a great potential to make the energy sector more flexible and sustainable due to their capabilities to use energy sources of varied nature and to efficiently store energy for subsequent use. Central control tasks within these systems for the efficient and safe distribution of heat refer to the stabilization of overall system temperatures and the regulation of storage units state of charge. These are challenging goals when the networked and nonlinear nature of district heating system models is taken into consideration. In this letter, for district heating systems with multiple, distributed heat producers, we propose a decentralized control scheme to provably meet said tasks stably.

Mean Field Game based Energy Flow Management for Grid Integration of Large-scale Electric Vehicles*

To integrate the distributed energy of electric vehicles (EVs) into power grid, handling the collective energy demand of the EVs is a key issue due to the uncertainties in mobility behavior of the individual vehicle. This paper addresses this problem by using mean-field limit approach to challenge this issue. The system targeted in this paper consists of an electric energy supplier and a parking lot that hosts a electric vehicle (EV) fleet with sufficiently large population. The energy integration between the system and the power grid is formulated as two problems: a day-ahead electric trading planning problem of the supplier, where the benefit maximization is considered under the constraint of charging/discharging demand of the parking lot, and a decentralized charging control problem is solved for the individual EV with consideration of the collective behavior of the whole EV fleet. To decouple the two problems, the mean-field limit approach is introduced to predict the charging/discharging demand of the EV fleet, and this also enables one to design the decentralized control strategy by solving a mean-field game problem.

Distributed Active Noise Control Based on an Augmented Diffusion FxLMS Algorithm

Multichannel active noise control (ANC) systems have been widely investigated for low-frequency noise attenuation over a spatial region. Using a conventional centralized control strategy based on the multichannel filtered- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> least mean square (F <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> LMS) algorithm has been demonstrated to be effective for multichannel ANC systems, but the high computational burden restricts its practical applications. Meanwhile, a decentralized control strategy suffers from stability problems although it has been successful in reducing the computational load. Recently, distributed control strategies, such as the multitask diffusion adaptation scheme, have been introduced to ANC systems and shown to mitigate the stability problems in decentralized systems. However, distributed ANC systems using the diffusion F <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> LMS algorithm require strong symmetry of acoustic paths because of the dependence on node-based adaptation and neighborhood-based combination. To overcome this limitation, this paper proposes an Augmented Diffusion F <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> LMS algorithm with neighborhood-based adaptation and node-based combination. A theoretical formulation and convergence analysis are presented and simulations are performed to compare the proposed algorithm with existing ones under different system configurations for tonal, multi-tonal, narrowband and broadband signals. Simulation results demonstrate that the proposed algorithm has the same noise reduction performance as centralized method even if the acoustic paths are strongly asymmetrical, which is superior over existing distributed multitask diffusion strategy.

Coordinated Charging of Spatially Distributed Electric Vehicles for Mitigating Voltage Rise and Voltage Unbalance in Modern Distribution Networks

This paper proposes a decentralised control strategy using droop-based control to vary the charging rates of electric vehicles (EVs) in real-time in response to nodal voltages, voltage unbalance and the state-of-charge (SOC). This strategy developed aims to mitigate excessive voltage rise caused by rooftop solar photovoltaic (PV) systems and voltage unbalance caused by unbalanced loads in a low-voltage (LV) distribution network. This will also smooth the voltage profile through peak reduction and valley filling by EV charging during times of low load, high generation and performing vehicle-to-grid operations during times of high load, low generation. Reference voltages are calculated and used to coordinate the control of the EV chargers by assigning each a target voltage based on their location and local conditions without the use for any common communication channel or a centralised controller. The local voltage, phase unbalance and current EV SOC are used to determine a suitable charging rate for the vehicle. A modified IEEE 13 node test feeder inclusive of an LV network based on a semi-rural Australian town was used to assess the impacts of PV and EV charging in the network and validate the proposed strategy as a means to reduce voltage rise and voltage unbalance in the network. Simulations were performed using OpenDSS and MATLAB in four scenarios, with and without charging control, to verify the effectiveness of the proposed strategy.

Circulating Current Control of LVDC Microgrid Based On Optimal Offset Voltage Decentralized Control Scheme with CPLs

Circulating Current Control of LVDC Microgrid Based On Optimal Offset Voltage Decentralized Control Scheme with CPLs

Design of Active Vibration Isolation Controller with Disturbance Observer-Based Linear Quadratic Regulator for Optical Reference Cavities

The optical reference cavity in an ultrastable laser is sensitive to vibrations; the microvibrations in a space platform affect the accuracy and stability of such lasers. In this study, an active vibration isolation controller is proposed to reduce the effect of vibrations on variations in the cavity length and improve the frequency stability of ultrastable lasers. Based on the decentralized control strategy, we designed a state-differential feedback controller with a linear quadratic regulator (LQR) and added a disturbance observer (DOB) to estimate the source noise. Experiments were conducted using an active vibration isolation system; the results verified the feasibility and performance of the designed controller. The accelerations along the axis (Z-, X-, Y-) directions were suppressed in the low-frequency band within 200 Hz, and the root-cumulative power spectral densities (PSDs) declined to 1.17 × 10-5, 7.16 × 10-6, and 8.76 × 10-6 g. This comprehensive vibration met the requirements of an ultrastable laser.

Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning

The real-time control (RTC) of urban drainage systems can make full use of the capabilities of existing infrastructures to mitigate combined sewer overflow (CSO) and urban flooding. Despite the benefits of RTC, it may encounter potential risks and failures, which need further consideration to enhance its robustness. Besides failures of hardware components such as sensors and actuators, the RTC performance is also sensitive to communication failures between the devices that are spatially distributed in a catchment-scale system. This paper proposes a decentralized control strategy based on multi-agent reinforcement learning to enhance communication robustness and coordinate the decentralized control agents through centralized training. To investigate different control structures, a centralized and a fully decentralized strategy are also developed based on reinforcement learning (RL) for comparison. A benchmark drainage model and a real-world drainage model are formulated as two cases, and the control agents are trained to control the orifices or pumps for CSO or flooding mitigation in each case. The three RL strategies reduce the CSO volume by 5.62-9.30% compared with a static baseline in historical rainfalls of the benchmark case and reduce the CSO and flooding volume by 14.39-21.36% compared with currently-used rule-based control in synthetic rainfalls of the real-world case. Benefitting from centralized training, the decentralized agents can achieve similar performance to the centralized agent. The decentralized control also enhances the communication robustness with smaller performance loss than the centralized control when observation communication fails, and provides a robust backup at the local level to limit the uncertainties when action commands from the centralized agent are lost. The results and findings indicate that multi-agent RL contributes to a coordinated and robust solution for RTC of urban drainage systems.

Fixed-time adaptive neural self-triggered decentralized control for stochastic nonlinear systems with strong interconnections

This article develops a fixed-time adaptive self-triggered decentralized control strategy based on the neural network for a class of stochastic nonlinear systems with strong interconnections. With the help of the special property of the Gaussian function, the strong interconnected functions in stochastic systems can be handled successfully without matching conditions assumptions, and the processing conditions of strong interconnected functions are relaxed. Moreover, a self-triggered mechanism is designed to decrease the waste of the system communication resources. On this basis, a fixed-time adaptive self-triggered decentralized control scheme is constructed by utilizing the fixed-time stability theory such that the stochastic nonlinear system with strong interconnections is fixed-time stable in probability. Finally, the feasibility of the developed control strategy can be ensured by the simulation result.

Decentralized optimal management of a large-scale EV fleet: Optimality and computational complexity comparison between an adaptive MAS and MILP

Increasing the penetration of variable and uncertain renewables and electric vehicles in power systems may give rise to problems (such as network congestion and commitment mismatches) if not controlled strategically. This demands control solutions in the form of energy management strategies for active distribution networks which would control the connected distributed energy resources and storage units in real-time to address the mentioned challenges. Centralized strategies may fail to serve this purpose for large-scale distribution networks due to their inherent shortcomings like vulnerability to single point of failures and large computing times. Unlike centralized approaches, decentralized control strategies show more potential. This paper presents one such solution, based on an adaptive multi-agent system, to control a large-scale distribution network in real-time. Its performance is compared with the results obtained with the corresponding centralized optimization problem, modeled as a mixed integer linear programming problem. Both the centralized version and the decentralized multi-agent version of the problem under consideration are presented and a case study is designed for the comparison. The comparison shows that the designed multi-agent system produces a near-optimal solution in real-time while the centralized optimization strategy struggles in terms of computational complexities for larger distribution networks.© 2017 Elsevier Inc. All rights reserved.

Adaptive Fuzzy Output-Feedback Decentralized Control for Fractional-Order Nonlinear Large-Scale Systems

This article studies the adaptive fuzzy output-feedback decentralized control problem for the fractional-order nonlinear large-scale systems. Since the considered strict-feedback systems contain unknown nonlinear functions and unmeasurable states, the fuzzy-logic systems (FLSs) are used to model unknown fractional-order subsystems, and a fuzzy decentralized state observer is established to obtain the unavailable states. By introducing the dynamic surface control (DSC) design technique into the adaptive backstepping control algorithm and constructing the fractional-order Lyapunov functions, an adaptive fuzzy output-feedback decentralized control scheme is developed. It is proved that the decentralized controlled system is stable and that the tracking and observer errors are able to converge to a neighborhood of zero. A simulation example is given to confirm the validity of the proposed control scheme.

Enhancing dynamic stability performance of hybrid ac/dc power grids using decentralised model predictive control

High voltage direct current (HVDC) networks are transforming ac power grids to hybrid ac/dc power grids; hence hybrid ac/dc power grids are facing new stability challenges. In this paper, a decentralised control scheme based on model predictive control (MPC) is proposed to enhance the stability of hybrid ac/dc power grids under disturbances. The proposed scheme controls the dc voltage, frequency, active and reactive power simultaneously with minimum deviations from steady-state values. A communication network is used to share the sampled grid data with the local MPC of each voltage source converter (VSC). Hence, the central controller can be avoided. Moreover, the active power-sharing between converters can be pre-designed. Thus, fast and accurate active power control can be realised. Finally, the proposed control scheme is validated using a five-terminal VSCHVDC test system with MATLAB. The VSCHVDC network with the proposed control scheme shows a smaller deviation and a faster response compared with the conventional droop control scheme and centralised MPC scheme under small disturbances. Under large power variations, the proposed control scheme assists to reach the steady-state rapidly with a minimum impact on the entire power grid. The proposed control scheme has also proved its robustness under communication delays and outages.

COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning

Platooning and coordination are two implementation strategies that are frequently proposed for traffic control of connected and autonomous vehicles (CAVs) at signal-free intersections instead of using conventional traffic signals. However, few studies have attempted to integrate both strategies to better facilitate the CAV control at signal-free intersections. To this end, this study proposes a hierarchical control model, named COOR-PLT, to coordinate adaptive CAV platoons at a signal-free intersection based on deep reinforcement learning (DRL). COOR-PLT has a two-layer framework. The first layer uses a centralized control strategy to form adaptive platoons. The optimal size of each platoon is determined by considering multiple objectives (i.e., efficiency, fairness and energy saving). The second layer employs a decentralized control strategy to coordinate multiple platoons passing through the intersection. Each platoon is labeled with coordinated status or independent status, upon which its passing priority is determined. As an efficient DRL algorithm, Deep Q-network (DQN) is adopted to determine platoon sizes and passing priorities respectively in the two layers. The model is validated and examined on the simulator Simulation of Urban Mobility (SUMO). The simulation results demonstrate that the model is able to: (1) achieve satisfactory convergence performances; (2) adaptively determine platoon size in response to varying traffic conditions; and (3) completely avoid deadlocks at the intersection. By comparison with other control methods, the model manifests its superiority of adopting adaptive platooning and DRL-based coordination strategies. Also, the model outperforms several state-of-the-art methods on reducing travel time and fuel consumption in different traffic conditions.

PWM Based Fixed Frequency Equivalent SM Controller for Stability of DC Microgrid System

DC microgrids are localized and independent power distribution networks which show high efficiency when batteries and renewable sources are interconnected with the system. This paper addresses the stability of the dc microgrid through a decentralized control scheme. A centralized control architecture can improve the stability but reliability is compromised if the central controller fails. Droop control is commonly used to address the stability problem based on techniques through linear controllers. However, the Droop controller requires a tradeoff between voltage regulation and droop gain. Further, the global stability of the systems cannot be ensured through linear control techniques. Additionally, for different operating requirements and load conditions, it is difficult to optimize the parameters of these controllers. To address limitations, a PWM Based fixed frequency equivalent sliding mode (SM) control technique is proposed for dc microgrid stability. SM controllers show high robust performance. To formulate the problem, system equations are modeled and the operation of the system under SM is verified for existence and stability conditions. To examine the transient performance, the responses for critically damped and underdamped are investigated and presented. The results of detailed experiments simulations are presented which show the efficiency of the proposed control method.

Research on subsystem division scheme of overlapping decentralized control strategy

The overlapping decentralized control strategy is a solution to the decentralized control of large-scale systems based on the inclusion principle. Its design process can be summarized as three stages of expansion-decoupling-contraction. In this paper, the subsystem division scheme of the overlapping decentralized control strategy is studied for building structure systems under earthquake. Using the energy-to-peak algorithm, we mainly analyse the influences of the location of the overlapping layer, the number of overlapping layers and the number of subsystems on the control effect of the structural system, and compare them with the traditional energy-to-peak centralized control method. The numerical results indicate that the dynamic response of the structure can be effectively suppressed by raising the location of the overlapping layer, increasing the number of overlapping layers, and increasing the number of subsystems. Compared with the centralized control strategy, the control effect is equivalent, or even better, verifying the feasibility of this research scheme.

Active-sensing-based decentralized control of autonomous mobile agents for quick and smooth collision avoidance.

There is an increasing demand for multi-agent systems in which each mobile agent, such as a robot in a warehouse or a flying drone, moves toward its destination while avoiding other agents. Although several control schemes for collision avoidance have been proposed, they cannot achieve quick and safe movement with minimal acceleration and deceleration. To address this, we developed a decentralized control scheme that involves modifying the social force model, a model of pedestrian dynamics, and successfully realized quick, smooth, and safe movement. However, each agent had to observe many nearby agents and predict their future motion; that is, unnecessary sensing and calculations were required for each agent. In this study, we addressed this issue by introducing active sensing. In this control scheme, an index referred to as the "collision risk level" is defined, and the observation range of each agent is actively controlled on this basis. Through simulations, we demonstrated that the proposed control scheme works reasonably while reducing unnecessary sensing and calculations.

Observer-based adaptive ISMC for connected vehicles against cyber-attacks

This paper concentrates on decentralized variable structure adaptive integral sliding mode control (ISMC) for connected vehicles (CV) against cyber-attacks. First of all, a novel vehicle leader-follower model is established by jointly considering the cyber-attacks which involve replay attack and DoS attack simultaneously. Secondly, a decentralized control scheme is designed on the basis of the variable structure observer and adaptive integral sliding mode technique. The Lyapunov-Razumikhin method is adopted such that the state estimation error of CV system is asymptotically converged to zero, which can overcome the replay attack, DoS attack, and measurement delays, then a corrective signal is designed for the deception attack to eliminate the influence of deception attack on the CV system. Furthermore, the robust asymptotic stability of CV system is guaranteed by exploiting the emerging technique of robust control for time-delay systems. Meanwhile, the string stability of CV is guaranteed, even under the condition of heterogeneous vehicles. Finally, extensive simulations are conducted to verify the theoretical analysis and demonstrate the effectiveness and superiority of the proposed method.

A decentralized feedback approach for flow control in highway traffic networks

Finite-time optimal feedback control for highway traffic networks under information constraints is studied. The primary objective is to design a fully decentralized state-feedback controller with ramp metering and variable speed limit control actions, with no information exchange between local controllers, that ensures feasibility and improves a global performance index. The design of a decentralized feedback controller with a one-hop information structure is examined, wherein the optimum outflow rate from each segment of the network depends only on the state of that segment and the state of all other segments directly connected to the downstream junction of that segment. It is demonstrated that when cost/constraints can be modeled or approximated by piecewise-affine functions, the control law has a closed-form state-feedback realization. The decentralization is based on the truncation of non-local variables in local optimization problems. The resulting decentralized control scheme has a simple closed-form representation and is scalable to arbitrary large networks; moreover, we demonstrate that, under certain conditions, with respect to certain meaningful performance indexes, the performance loss due to decentralization is zero; namely, the centralized optimal controller has a decentralized realization with a one-hop information structure.