Distributed Control Method Research Articles

Consensus-based distributed formation control for coordinated battle system of manned/unmanned aerial vehicles

In this paper, a consensus-based distributed formation control method is proposed for improving the coordinated battle efficiency of coordinated battle system for manned/unmanned aerial vehicles (MAV/UAVs). The method proposed is designed based on consensus protocol, which contains three parts. First, the distributed structure with limited centralized strategy for MAV/UAVs cooperative system is designed. Second, a second-order dynamic model is built up to describe the distributed formation system, which is further simplified using feedback linearization method. Third, the information exchange topology of MAV/UAV-coordinated battle system is established on the basis of graph theory and leader–follower mechanism. Finally, the effectiveness of the proposed formation strategy for MAV/UAVs cooperative system is proved asymptotic convergence by constructing a reasonable Lyapunov function which guarantees the accurate formation maintenance. Simulation results are presented to demonstrate the effectiveness of the proposed method. And comparative simulation results reveal that the method proposed in this paper is superior to traditional algorithms in the aspects of accuracy and stability.

Partially Decentralized Secondary Current Sharing and Voltage Restoration Controller Design for DC Microgrids With Event-Triggered Sampling

Decentralized control is a practical strategy to be employed for many reasons such as simplicity of design and easy implementation. In this paper, we design a partially decentralized secondary controller to achieve the objective of current sharing and DC bus voltage restoration in islanded direct-current (DC) microgrid (MG) systems. Compared to existing centralized or distributed control methods, only DC bus voltage information is required in local decentralized controller design, which greatly reduces the communication and implementation cost. On the other hand, such simplified design and implementation may limit the performances of the overall designed system. However, our rigorous theoretical analysis establishes that the proposed method is able to restore the DC bus voltage with bounded error by properly selecting control parameters. Furthermore, we also design an event-triggered sampling based decentralized controller and investigate its control performance, which indicates that the proposed event-triggered controller can ensure to achieve a uniformly bounded voltage restoration error but with much less signal communication cost. Finally, all these theoretical findings are supported by case studies conducted in a real-time DC MG test system built in OPAL RT.

Voltage and Frequency Regulation With WT-PV-BESS in Remote Weak Grids via Fixed-Time Containment Control

In this paper, a novel voltage and frequency regulation framework for the WT-PV-BESS in a remote weak grid is proposed. The BESSs are utilized to flexibly absorb or output power to address the issue of bus overvoltage caused by PV generation and system level frequency deviation caused by power output randomness. In this scheme, the BESSs are controlled by a kind of novel distributed control method called containment control. Its key feature is that it treats frequency and voltage as two independent containment dimensions, such that the BESSs will be controlled in a cooperative way to simultaneously restrict the system frequency and bus voltage within the allowable range. Meanwhile, the containment control can be achieved within fixed time to ensure the responsiveness of the BESSs. Simulation case studies are conducted to validate the performance and superiority of the method.

Distributed Secondary Control for Island Microgrids With Expected Dynamic Performance Under Communication Delays

In island microgrids, the increasing integration of inverter-interfaced distributed generators (DGs) degrades the dynamic performance of the control system. The operation voltage and frequency may appear unacceptable overshoots and fluctuations under disturbances. To achieve rapid convergence while efficiently suppressing overshoot, this paper develops a distributed control method for island microgrids with expected dynamic performance. In addition to the typical distributed consensus design, an auxiliary dynamic reshaping function is introduced into the algorithm to increase the control degree of freedom of the operation dynamics. Thus, the proposed method can realize independent and flexible configurations of the damping ratio and natural frequency of the control system, and obtain the expected state convergence and overshoot suppression performance according to the actual requirements of microgrids. Besides, different delays between DGs are also considered. Instead of simply adopting the conventional global fixed delay time, we preserve the difference of delays to the greatest extent. Such processing can effectively reduce the conservatism of control and improve the response rate. A nonlinear programming problem is also established for DGs to calculate the allowable delays under the specific performance. Finally, several case studies and an RT-Lab experiment evaluate the effectiveness of the method.

SCP model based on the new energy vehicle collaborative detection configuration method research

Abstract To address the problem of inaccurate positioning of new energy vehicles during cooperative detection, this paper investigates the positioning accuracy of different combinations of radar and infrared sensors. In order to meet the control requirements of the vehicle co-detection formation, a distributed control method considering the vehicle dynamics characteristics is adopted for this purpose. And considering the cost of sensors, the optimal parameters are calculated, and an optimal aircraft co-detection configuration design scheme based on the SCP model (structure-behavior-performance model) is proposed. At the working level of multi-craft cooperative detection guidance, a two-layer cooperative guidance structure is designed based on the basic framework. Research on vehicle configuration design, vehicle data transmission, processing technology, and cooperative detection and guidance is carried out. The basic principles of the particle swarm optimization algorithm and the algorithmic flow of the discrete particle swarm algorithm are applied to calculate the assignment of targets by the particle swarm optimization algorithm. The GDOP values are analyzed according to the simulation results, and it can be concluded that: the change rate of 2~5 sensors accuracy can reach 71.1%, 71.6%, and 71.5%, respectively, when the infrared sensor spacing is 1km~3km. 5~10 sensors accuracy change rate is only 53.3%, 52.2%, and 49.7%, respectively, which shows that the value is improved by 20%, indicating that the collaborative sensing accuracy of the aircraft has been effectively improved, which is of great help to the collaborative detection of new energy vehicles based on SCP model.

Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections

To improve the driving efficiency and energy-saving characteristics for large-scale mixed traffic flows under different market penetration rates (MPRs) of intelligent and connected vehicles (ICVs) at unsignalized intersections, considering the cooperative eco-driving performance between ICVs and human-driven vehicles (HDVs) with time-varying speed characteristics, the hierarchical and distributed cooperative eco-driving architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the multivehicle model-free adaptive predictive cooperative driving (MFAPCD) method is designed by using only the driving data of the HDVs and ICVs formation based on compact form dynamic linearization (CFDL) technique, thereby improving traffic efficiency. Furthermore, the CFDL integral terminal sliding mode predictive control (CFDL-ITSMPC) scheme is utilized to predict the time-varying driving speed of HDVs, and then, the CFDL predictive control (CFDL-PC) scheme is utilized to predict the expected control variables of ICVs formation. For the vehicle control layer, based on the anticipated driving speed obtained from the cloud decision layer, the nonlinear distributed model predictive control (NDMPC) method is utilized for distributed optimal control of each vehicle formation, to achieve optimization in terms of energy saving. Simulation results show that, compared with the signal time assignment strategy, the method can increase the average velocity by about 15.22% and decrease the average fuel consumption by about 36.43% under different MPRs and traffic volumes.

Fast Distributed Model Predictive Control Method for Active Suspension Systems.

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

An Observer-Driven Distributed Consensus Braking Control Method for Urban Railway Trains with Unknown Disturbances

An urban railway train is a braking power-distributed system consisting of multiple carriages, which is becoming a powerful transportation tool to alleviate traffic congestion within cities as well as across cities. It is critical to control an urban railway train synchronously for improving braking performances, but challenging to be achieved due to strong coupling, unknown dynamics, and disturbances. This paper proposes an observer-driven distributed consensus braking control method for an urban railway train. Specifically, according to the data intersection among carriages, a distributed consensus braking controller is designed to make the velocity of each carriage converge to the desired braking curve. A sliding mode disturbance observer is then developed to estimate the non-linear coupling force and disturbances. The estimation value is utilized to compensate for the distributed consensus braking control law. Moreover, the potential fields are introduced to guarantee that the distances between any two neighbouring carriages are stabilized in a safe range. The effectiveness of the developed control strategy is firstly authenticated via the Lyapunov stability theory and then validated via numerical comparative simulations.

Multi-time scale optimization scheduling of microgrid considering source and load uncertainty

With the increase of renewable energy penetration in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power of contact lines and system economy when achieving optimal scheduling. In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed. In the day-ahead scheduling stage, a two-stage distributionally robust optimal scheduling model is established with the objective of minimizing the comprehensive day-ahead scheduling cost of the microgrid, and the optimal day-ahead scheduling solution is found under the probability distribution of the worst scenario. In the intra-day scheduling stage, based on the prediction value of source and load with higher accuracy, a distributed model predictive control method is used to build an intra-day rolling optimization and real-time adjustment optimal scheduling model, which ensures the effective implementation of the day-ahead plan and fully suppresses power fluctuation of the contact line. The simulation results show that the proposed multi-time scale optimal scheduling method can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid.

Distributed Event-Triggered Formation Control of Planar Vehicles Without Velocity Sensors

In this paper, an event-triggered distributed formation control method is proposed for multiple planar vehicles which are modeled as uncertain Euler-Lagrangian systems (ELSs). Firstly, a position and orientation (PAO)-based event-triggered mechanism is designed to decrease the frequency of data transmission through the wireless network. According to the PAO information at the event-triggered time, an event-triggered high-gain extended state observer (EHGESO) is developed to remove the demand of velocity information in conventional backstepping technique and eliminate the requirement of continuous PAO information in traditional HGESO design. Under the developed EHGESO and the PAO-based event-triggered mechanism, planar vehicles can cooperatively move along the parameterized path, and can transmit information with their neighbor vehicles only if the designed event-triggered mechanism is violated. The stability is proved by employing input-to-state stability (ISS) and semi-global uniformly ultimately bounded (SGUUB) stability theory. Eventually, a simulation of three-degree-of-freedom surface vehicle models is offered to illustrate the effectiveness of the developed control method.

A Distributed Multimode Control Strategy for the Cascaded DC–DC Converter Applied to MVAC Grid-Tied PV System

The cascaded dc–dc converter is a popular topology for large-scale medium-voltage photovoltaic (PV) system with the advantages of high voltage conversion ratio, high efficiency, great flexibility, and independent control capability. However, the absence of optimal string maximum power point tracking (MPPT) operation under power mismatch is a big issue in common centralized control. Furthermore, the cost and time delay are unacceptable resulting from the distance among PV strings in the large-scale PV station and the cascaded dc–dc converter needs to adopt the distributed control method without real-time communication. To this end, by introducing other necessary constraints including voltage constraints, power constraints, and gain constraints on the basis of the distributed MPPT control strategy, a distributed multimode control scheme is proposed in this article. Each submodule (SM) can be switched flexibly between multiple operation modes to maximize the extracted solar energy. Individual string MPPT and autonomous distribution of output voltage are achieved without communication under the proposed distributed control strategy. In this way, optimal operation range is wider, mode switching is simpler, and applicability and robustness are improved. A downscaled prototype and a 35-kV/6-MW simulation system are constructed to verify the effectiveness of the proposed control strategy.

Hybrid Electric Vehicle Torque Distribution Control Method and System with Environmental Temperature Protection Battery Integration

This research presents a hybrid electric vehicle (HEV) torque distribution control method and system that incorporates an environmental temperature protection battery. The study involves the collection of ambient temperature information to determine the vehicle's location and utilizes a battery equivalent circuit model to calculate the actual temperature of the battery. The state of charge (SoC) values of the battery are determined based on battery energy dump and maximum capacity. The research utilizes a neural network control method to optimize control parameters considering the actual temperature and SoC values of the battery. Furthermore, an online minimum equivalent oil consumption strategy is developed to model the target function and constraint formulas for fuel consumption, which enables the determination of optimal motor torque values during equivalent fuel consumption. The torque distribution and control commands for the vehicle's power train and dynamics module are established based on the calculated engine torque and motor torque values. By considering battery behavior and controlling the power-driven system, the research aims to protect against galvanic actions and demonstrates its practical significance.

Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization

Environmental and climate change concerns are pushing the rapid development of new energy resources (DERs). The Energy Internet (EI), with the power-sharing functionality introduced by energy routers (ERs), offers an appealing alternative for DER systems. However, previous centralized control schemes for EI systems that follow a top-down architecture are unreliable for future power systems. This study proposes a distributed control scheme for bottom-up EI architecture. Second, model-based distributed control methods are not sufficiently flexible to deal with the complex uncertainties associated with multi-energy demands and DERs. A novel model-free/data-driven multiagent deep reinforcement learning (MADRL) method is proposed to learn the optimal operation strategy for the bottom-layer microgrid (MG) cluster. Unlike existing single-agent deep reinforcement learning methods that rely on homogeneous MG settings, the proposed MADRL adopts a form of decentralized execution, in which agents operate independently to meet local customized energy demands while preserving privacy. Third, an attention mechanism is added to the centralized critic, which can effectively accelerate the learning speed. Considering the bottom-layer power exchange request and the predicted electricity price, model predictive control of the upper layer determines the optimal power dispatching between the ERs and main grid. Simulations with other alternatives demonstrate the effectiveness of the proposed control scheme.

Barrier-Certified Distributed Model Predictive Control of Under-Actuated Autonomous Surface Vehicles via Neurodynamic Optimization

This article addresses the distributed formation control of multiple under-actuated autonomous surface vehicles (ASVs) in a receding-horizon setting. The ASVs are subject to physical constraints, in addition to stationary and moving obstacles. A barrier-certified distributed model predictive control method is proposed with the capability of avoiding collision with stationary and moving obstacles and neighboring ASVs. Specifically, a data-driven neural predictor is used to learn unknown functions in ASV kinetics. A nominal distributed receding-horizon position control law is developed based on the learned unknown function to achieve the desired formation within physical constraints. To ensure the safety requirement, a barrier-certified control law is designed based on control barrier functions to generate the signals of optimal surge force and heading angle within the safety constraints. A receding-horizon heading control law is designed based on the data-driven neural predictor to track the desired heading signals. Constrained quadratic programming problems are formulated based on barrier functions for barrier-certified distributed formation control and solved via neurodynamic optimization using one-layer recurrent neural networks. Thus, the proposed control method is able to ensure obstacle avoidance in the formation control of multiple ASVs in the presence of stationary and moving obstacles. Simulation results are elaborated to validate the efficacy of the proposed barrier-certified distributed model predictive control method for ASV formation.

Adaptive Multigradient Recursive Reinforcement Learning Event-Triggered Tracking Control for Multiagent Systems.

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning (RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent systems. The multigradient recursive RL algorithm is used to avoid the local optimal problem that may exist in the gradient descent scheme. Different from the existing event-triggered control results, a new lemma about the relative threshold event-triggered control strategy is proposed to handle the compensation error, which can improve the utilization of communication resources and weaken the negative impact on tracking accuracy and closed-loop system stability. To overcome the difficulty caused by sensor fault, a distributed control method is introduced by adopting the adaptive compensation technique, which can effectively decrease the number of online estimation parameters. Furthermore, by using the multigradient recursive RL algorithm with less learning parameters, the online estimation time can be effectively reduced. The stability of closed-loop multiagent systems is proved by using the Lyapunov stability theorem, and it is verified that all signals are semiglobally uniformly ultimately bounded. Finally, two simulation examples are given to show the availability of the presented control scheme.

Voltage control and power sharing in DC Microgrids based on voltage-shifting and droop slope-adjusting strategy

• Designed a dynamic droop controller based on average of instantaneous power, droop control and voltage. • Proposed a new distributed control algorithm for DC microgrids. • Accurate power sharing and fast voltage recovery. To overcome the disadvantages of droop control, this paper proposes a novel method of hierarchical distributed secondary control for DC microgrids. With the load changes, distributed energy resources can achieve power sharing and restore the DC bus voltage. Based on the rated voltage and instantaneous power of the DC grid (DG), a hierarchical distributed control method including shift voltage and droop slope adjustment has been developed. By increasing the shifted voltage and adjusting the droop gain, it can effectively compensate the voltage drop caused by the droop controller. In this way, the DC bus voltage can always be adjusted automatically, regardless the load changes. In addition, this paper introduces the design process of the controller in detail and proves the stability of the system. Finally, simulations and experiments have verified the effectiveness and superior performance of the proposed control method.

Alternating direction method of multipliers-based distributed control for distributed manipulation by shaping physical force fields

This paper proposes an algorithm for decomposing and possibly distributing an optimization problem that naturally emerges in distributed manipulation by shaping physical force fields through actuators distributed in space (arrays of actuators). One or several manipulated objects located in this field can “feel the force” and move simultaneously and independently. The control system has to produce commands for all actuators so that desired forces are developed at several prescribed places. This can be formulated as an optimization problem that has to be solved in every sampling period. Exploiting the structure of the optimization problem is crucial for platforms with many actuators and many manipulated objects, hence the goal of decomposing the huge optimization problem into several subproblems. Furthermore, if the platform is composed of interconnected actuator modules with computational capabilities, the decomposition can give guidance for the distribution of the computation to the modules. We propose an algorithm for decomposing/distributing the optimization problem using Alternating Direction Method of Multipliers (ADMM). The proposed algorithm is shown to converge to modest accuracy for various distributed platforms in a few iterations. We demonstrate our algorithm through numerical experiments corresponding to three physical experimental platforms for distributed manipulation using electric, magnetic, and pressure fields. Furthermore, we deploy and test it on real experimental platforms for distributed manipulation using an array of solenoids and ultrasonic transducers.

A Dual Distribution Control Method for Multi-Power Components Energy Output of 4WD Electric Vehicles.

Energy management strategies are vitally important to give full play to the energy-saving of the four-wheel drive electric vehicle (4WD EV). The cooperative output of multi-power components is involved in the process of driving and braking energy recovery of 4WD EV. This paper proposes a novel energy management strategy of dual equivalent consumption minimization strategy (D-ECMS) to improve the economy of the vehicle. According to the different driving and braking states of the vehicle, D-ECMS can realize the proportional control of the energy cooperative output among the multi-power components. Under the premise of satisfying the dynamic performance of the vehicle, the operating points of the power components are distributed more in the high-efficiency range, and the economy and driving range of the vehicle are optimized. In order to achieve the effectiveness of D-ECMS, MATLAB/Simulink is used to realize the simulation of the vehicle. Compared with the rule-based strategy, the economy of D-ECMS increased by 4.35%.

Active power oscillation suppression and dynamic performance improvement for multi-VSG grids based on consensus control via COI frequency

Virtual synchronous generator (VSG) is deemed to be the key foundation for the future low inertia power system for its superiority to provide flexible inertia support. However, complicated dynamic frequency interaction, active power oscillation, and inappropriate transient active power-sharing will occur when VSGs are widely applied. Center of Inertia (COI) is introduced for solving these problems. But the calculation of COI frequency relies on global communication and a centralized controller, which results in high communication costs and complexity, and reduces the system reliability. This paper proposes a distributed control method via COI frequency for multi-VSG grids. The COI frequency estimator is designed based on dynamic consensus control. Communication between adjacent neighbors is required, which improves reliability and reduces communication costs. The stability of the proposed control method is proved and the feasible range of the control parameter is presented in this paper. Simulations and experiments are carried out to verify the effectiveness and feasibility.

Distributed Control for an Urban Traffic Network

In this paper, we develop a distributed control method for an urban traffic network in order to improve traffic conditions and guarantee a smooth operation for all roads and intersections. An optimal coordination among traffic flows is determined by a model predictive control idea. The control objective is formulated as a constrained optimization problem in which the cost function to be minimized is a combination of some traffic network-related performance indexes and the constraints are derived from capacities of roads and intersections. The green time assigned to each road link is computed from its optimal downstream traffic flow. In the proposed algorithms, every road link uses only its local information to determine its control decision which corresponds to a global optimal solution. The algorithms are developed by using a gradient projection-based method and properties of the minimal polynomial of a matrix pair. The effectiveness of the proposed algorithms is validated via numerical simulations with MATLAB and VISSIM.