Distributed Control Method Research Articles

PD distributed control and stability analysis for DC microgrids with multiple time delays

Distributed control is widely used for coordination of multiple distributed generations (DGs) in a microgrid (MG), but the information transmission in practice is inevitably suffered from time delays, which will deteriorate system performance and even cause system instability. To solve this problem, a proportional-differential (PD) distributed control method for DC MGs is proposed in this paper. Firstly, a small-signal system model considering multiple time delays including measurement delay and communication delay is established. The introduction of time-delayed differential terms results in the system being transformed into a neutral time-delayed system (NTDS). Then, the time delay margin (TDM) under different delayed scenarios is accurately determined by the frequency-domain stability analysis, and the relationship between the TDM and the differential gain is quantified. Compared with the conventional proportional (P-type) distributed control method, the TDM is enhanced effectively. In addition, an indicator is proposed to quantify the enhancement effect of introducing differential terms on TDM. Finally, the effectiveness of the proposed method and the accuracy of the stability analysis are verified by simulation and hardware-in-loop (HIL) tests.

A new data-driven distributed power grid load storage resource-wide area voltage self-optimization control method for distribution system

Abstract Different from the conventional energy distribution network, which solely relies on energy distribution, the novel distribution system gradually exhibits a complex and interconnected form of various resources, such as source network load storage. These resources possess characteristics of “multi-point, wide area, and small amount” distribution. Investigating the potential for wide-area voltage regulation by using distributed resources like renewable energy power generation, distributed energy storage, and flexible loads is crucial for constructing a highly integrated source network load storage in the new type of distribution system. This paper proposes a novel data-driven method for optimizing control of wide-area voltage through distributed power network load storage resources. By comprehensively and cooperatively controlling renewable energy power generation, energy storage systems, and flexible loads, this approach enhances the quality of wide-area voltage in the distribution network while ensuring optimal utilization of energy storage capacity and minimizing network losses. The effectiveness and advanced nature of this method are demonstrated by applying it to an urban distribution network example. Compared to traditional voltage regulation methods, this approach aligns better with the requirements for voltage regulation in the new type of distribution network.

Distributed control for DC microgrids with cyber attacks and constraints: A fault-tolerant model predictive controller

Communication security issues pose severe challenges to stability of microgrid system. This paper designs a cooperative control method based on fault-tolerant distributed model predictive control (DMPC) for DC microgrid against possible false information injection (FDI) attacks and communication constraints problems. First, a state variable is defined based on the voltage and current information, thereby deriving a system model of the microgrid under the cyber-physical framework. Then, since a combined uncertainty term consisting of attacks and disturbances exists in the constructed system model, an expanded state observer (ESO) is proposed to observe the uncertainty term. Further, based on the constructed system model and the designed ESO, a fault-tolerant distributed model predictive controller is designed. In the controller, DMPC is utilized to constrain the communication information and ensure the boundedness of the communication state and physical state. Simulation results indicate that the proposed DMPC-based distributed control method can achieve stable voltage regulation and accurate current sharing, and maintain smooth operation even under FDI attack, which verifies the feasibility for the proposed method.

Resilient distributed model predictive control of state‐delayed linear parameter varying systems with quantitative communication against denial of service attacks

AbstractThis work presents a resilient distributed model predictive control (MPC) method for linear parameter varying (LPV) systems with state delays and attacks in communication networks. Coordinations are required for distributed MPC (DMPC) to achieve the global performance of centralized MPC (CMPC). However, control performance can be severely degraded by unreliable communication networks, for example, with denial of service (DoS) attacks. A resilient control framework is derived to address the unreliable communications in DMPC. A global system is divided into subsystems for the distributed control purpose. To deal with the model uncertainties and state delays, a “min‐max” DMPC algorithm is presented with a buffer to ensure resilience against DoS attacks. A quantization scheme is introduced to quantize the control information exchanged between subsystems. An iterative interaction scheme is proposed to exchange feedback control laws among subsystems. The stability of the closed‐loop system under the proposed algorithm is ensured by using a Lyapunov function method. The effectiveness of the proposed DMPC is demonstrated through two simulation examples.

Distributed optimal control of nonlinear multi‐agent systems based on integral reinforcement learning

AbstractIn this article, a distributed optimal control approach is proposed for a class of affine nonlinear multi‐agent systems (MASs) with unknown nonlinear dynamics. The game theory is used to formulate the distributed optimal control problem into a differential graphical game problem with synchronized updates of all nodes. A data‐based integral reinforcement learning (IRL) algorithm is used to learn the solution of the coupled Hamilton–Jacobi (HJ) equation without prior knowledge of the drift dynamics, and the actor‐critic neural networks (A‐C NNs) are used to approximate the control law and the cost function, respectively. To update the parameters synchronously, the gradient descent algorithm is used to design the weight update laws of the A‐C NNs. Combining the IRL and the A‐C NNs, a distributed consensus optimal control method is designed. By using the Lyapunov stability theory, the developed optimal control method can show that all signals in the considered system are uniformly ultimately bounded (UUB), and the systems can achieve Nash equilibrium when all agents update their controllers simultaneously. Finally, simulation results are given to illustrate the effectiveness of the developed optimal control approach.

Distributed model predictive control of fan coil system

To address the issues of large overshoot, long system stabilization time, and slow response associated with traditional PID-based and centralized model predictive control (MPC) of fan coil systems, this study proposes a distributed model predictive control (DMPC) method suitable for multi-zone building temperature regulation. Specifically, based on the thermal dynamic model of different zones, and aiming to constrain indoor temperature within set limits and minimize water system pressure drop and fan coil speed, a distributed MPC architecture for fan coil systems is developed. To solve the distributed optimization problem, the Nash equilibrium is introduced to find the global balance point within the distributed structure, and the Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) hybrid algorithm is used for online optimization of local MPCs. Experimental results show that compared to traditional PID control, the distributed MPC achieves coordinated multi-zone indoor temperature regulation with better dynamic performance, with the average absolute temperature error in each zone below 5 % and an energy savings rate of 16.23 %. Through semi-physical simulation, it is demonstrated that the proposed distributed MPC method has better applicability when the number of fan coils exceeds 10, compared to centralized MPC.

State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances

Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a linearized expression method of platoon error dynamics based on state measurement, the state feedback mechanism combined with a nonsmooth controller for a truck platoon is proposed in the development of the distributed control method. The state-feedback controller can drive the nominal platoon system to the state of second-order consensus, and the nonsmooth controller counterparts the uncertainties and disturbances. The convergence and string stability of the proposed control algorithm are demonstrated both theoretically and experimentally, and the effectiveness and robustness are also verified by simulation tests.

Heterogeneous unmanned swarm formation containment control based on reinforcement learning

In response to the problem of time-varying spherical formation control for a heterogeneous unmanned aerial vehicle (UAV) swarm system with dynamic uncertainty in the system model, this paper proposes an optimal distributed formation containment control method based on reinforcement learning. By combining the time-varying formation containment vector design with the distributed predefined time observer and constructing the augmented system of a multi-quadrotor UAV system and an observer, the problem of time-varying formation containment control in heterogeneous swarm systems is transformed into a stabilization problem. By introducing a value function with a discount factor, the stabilization problem of a heterogeneous UAV swarm system is transformed into an optimal control problem. Using the “actor-critic” neural network, combined with an off-policy reinforcement learning algorithm and distributed control methods, the solution to the formation containment controller is achieved in a data-driven manner. The stability of distributed observer and formation containment controller as well as the convergence of reinforcement learning algorithms are demonstrated using Lyapunov and related theory. The numerical simulation results demonstrate that the observation error of the designed distributed observer converges within a predefined time of 0.1 s, while the overall formation tracking error of the heterogeneous swarm converges within 2.74 s, thereby further validating the effectiveness and superiority of the designed control scheme.

Learning-based double layer control method of yaw stability simulation research for rear wheel independent drive electric tractor plowing operation

Complex field environments and variable soil conditions make tractor plowing operations susceptible to maneuvering instability, especially challenging in yaw stability. A learning-based double layer control (LDLC) method of yaw stability for rear wheel independent drive electric tractor (ET) plowing operation is proposed in this paper in order to solve this problem. Firstly, a sliding window self-learning traction resistance predictor is proposed based on Gaussian process regression (GPR) theory to predict the future traction resistance of plowing operation. Then, a discrete nonlinear model predictive control (NMPC) supervisor yaw stability controller is designed based on the dynamics model of the plowing unit and the tire-soil model to solve for the additional yaw torque required to maintain the yaw stability of the tractor. Finally, considering the additional yaw moment and driver demand torque, the subordinate controller composed of a particle swarm optimization algorithm optimally allocates the driving torque of the two driving wheels. Hardware-in-the-loop test results show that the maximum mean tracking errors of yaw rate and sideslip angle are 0.002 rad/s and 0.01 rad for the LDLC controlled ET during straight line and continuous steering under normal plowing operation, the maximum mean tracking errors of yaw rate and sideslip angle are 0.007 rad/s and 0.012 rad for straight line and continuous steering plowing operations under harsh plowing operation. The tracking accuracy is significantly higher than traditional cascade PID and equal distribution control methods. The proposed LDLC method effectively ensures the yaw stability of the ET during straight line and continuous steering under normal and harsh plowing operation conditions.

Low-voltage and high-penetration distributed photovoltaic access control technology based on intelligent measurement

Abstract This project takes high-penetration distributed photovoltaic access as the background and studies the control method for low voltage and high penetration distributed photovoltaic access. Firstly, by analyzing the measured data of the low-voltage distribution network, the daily load and measured values of the low-voltage distribution network were obtained. On this basis, the topology structure of a high-penetration distributed photovoltaic grid-connected system is studied, and the voltage, current, power, and other parameters of the grid-connected system are obtained. A low-voltage and high-penetration distributed photovoltaic grid-connected control method based on a genetic algorithm is adopted. Experiments have shown that the maximum allowable capacity of this scheme is smaller than the standard value and can effectively suppress voltage fluctuations. The experimental results show that the system has good grid connection performance, which is of great significance for improving the operational stability and efficiency of low-voltage distribution systems.

Update of high voltage isolation control and monitoring system for HVE-400 ion implanter

HVE-400 ion implanter is special ion implantation equipment for semiconductor materials boron and phosphorus doping. The ion source and extraction deflection system are at high voltage platform, while the corresponding control system is at ground voltage position. The control signals and measurement signals of various parameters at the high-voltage end need to be transmitted between ground voltage and high voltage through optical fibers to isolate high voltage. Upgrading is carried out due to the aging of the optical fiber transmission control and monitoring system, which cannot work stably. The transformation replaces the original distributed single-point control method with an advanced distributed centralized control method, and integrates all control and monitoring functions into an industrial control computer for digital operation and display. In the computer software, two kinds of automatic calculation of ion mass number are designed. After upgrading, the implanter high-voltage platform control and monitoring system features digitalization, centralized control, high reliability, strong anti-interference, fast communication speed, and easy operation.

Adaptive distributed MPC based load frequency control with dynamic virtual inertia of offshore wind farms

AbstractThe penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances.

Distributed control of energy storages for multi-time-step peak load shaving in a microgrid

Distributed energy storages (ESs) will be widely used in the future smart microgrids due to their continuous technology improvement and cost reduction. Meanwhile, the distributed control method also gains increasing attention for controlling distributed devices to protect user privacy and reduce computation burden compared with the centralized scheme. In this paper, a distributed control method of ESs is proposed for multi-time-step peak load shaving in a microgrid. Considering the ES efficiency is related to its power, an optimization is constructed to minimize the power loss during ES operations when performing peak load shaving function. By analyzing the net load curve and the ES available capacity, the multi-time-step peak load shaving problem is transformed into single-time-step ES operations at each time step. The Combine-Then-Adapt diffusion strategy is united with the consensus + innovation strategy to realize the peak load shaving in a fully distributed way. Case studies verify the efficiency of the proposed method.

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

AbstractIn this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

A Distributed Control Method With Seamless Hot Swap Capability for Generic DC Microgrids

A Distributed Control Method With Seamless Hot Swap Capability for Generic DC Microgrids

Distributed Model Predictive Control via Separable Optimization in Multiagent Networks

We present a distributed model predictive control method which enables a group of agents to compute their control inputs locally, while communicating with their neighbors over a communication network. While many distributed model predictive control methods require a central station for some coordination or computation of the optimization variables, our method does not require a central station, making our approach applicable to a variety of communication network topologies. With our method, each agent solves for its control inputs without solving for the control inputs of other agents, allowing for efficient optimization by each agent, unlike some other distributed methods. Further, our method attains linear convergence to the optimal control inputs in convex model predictive control problems, improving upon the sub-linear convergence rates provided by some other distributed methods such as dual decomposition methods. Moreover, our algorithm provides a closed-loop controller for convex model predictive control problems with affine constraints. We demonstrate our method in both convex and non-convex model predictive control problems in wireless transceiver alignment and satellite deployment, where we show robustness of our method to time delays.

Asynchronous Event-Triggered Distributed Predictive Control for Multiagent Systems With Parameterized Synchronization Constraints

This paper proposes an asynchronous event-triggered distributed predictive control method for the cooperation of multi-agent systems with parameterized synchronization constraints. A novel event-triggering condition related to the parameterized information received from neighboring agents is derived for each agent based on the input-to-state stability (ISS) condition. In such a framework, the distributed optimization problem is solved and the parameterized information is exchanged between neighboring agents, only when the triggering condition is satisfied. In addition, the dual mode control is adopted in this work and no information will be exchanged between the agent and its neighbors when the agent enters the invariant set, reducing the computation and communication load. Moreover, the recursive feasibility of the proposed algorithm is proved, and a sufficient condition is developed to ensure the stability of the closed-loop system. Finally, numerical simulation is given to verify the effectiveness of the proposed method.

A Guidance Module Based Formation Control Scheme for Multi-Mobile Robot Systems With Collision Avoidance

This paper investigates distributed formation control of multi-mobile robot systems with collision avoidance. A novel guidance module based formation control scheme is established, which consists of two main parts. In the first part, on the communication network of the multi-mobile robot system, a distributed guidance module is constructed from some predesigned virtual dynamics scattered in the robots’ feedback loops. By using some proper distributed formation control methods and distributed observer techniques, some formation references are generated by the guidance module. In the second part, by taking these references as the tracking references, some tracking controllers are designed and assigned to the robots such that the robots’ positions track their respective references asymptotically. A “two-layer constraint mechanism” is presented in the above controller design to limit both the formation references and the robots’ tracking errors such that robots’ collision avoidance is guaranteed. In this way, the multi-mobile robot system completes the desired collision-free formation control task. Compared with existing results, the method proposed in this paper has a better plug-and-play function and wider application scope, especially when the practical robots have tight encapsulations such that their pre-equipped tracking controllers cannot be arbitrarily redesigned. Moreover, under the proposed scheme, the asymptotic formation convergence is achieved by the robots without extra limitations on robots’ initial states and the utilization of global information. Some simulations and an experiment are given to validate the effects of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by controller encapsulation characteristics of practical mobile robot products. To complete various formation tasks, most of the related existing formation control methods require robots’ controllers to be continually updated and redesigned. However, this purpose is sometimes unachievable because actual mature robot products may have tight controller packages for trade secrets and intellectual property, or engineers want to retain robots’ well-adjusted default performances. To overcome this practical limitation, this paper provides a novel “guidance module based formation control scheme”. Under this scheme, engineers only need to put most efforts on a guidance module design to generate desired real-time desired tracking references for robots. A new “two-layer constraint mechanism” is proposed and applied here to guarantee collision avoidance among the robots when using the proposed scheme. With this scheme, engineers can establish required formation control strategies without robots’ pre-equipped controllers changed and the original control performances abandoned, as long as the robots have some basic capability of tracking given trajectories. Theoretical analysis and experiments on this scheme are all shown. In future research, based on the proposed scheme, we will continue to develop application-oriented formation control methods to improve the anti-disturbance performance and robustness of the multi-mobile robot systems.

Noncooperative Target Finite-Time Surrounding Control of Spacecraft Formation

This paper proposes a novel distributed control method for surrounding a noncooperative target that has maneuverability by spacecraft formation. A relative orbit error dynamic model between the target and the formation is established dependent on a reference spacecraft under the 2-body assumption. To estimate and compensate for the target’s control input rapidly, a novel finite-time extended state observer is developed. It is stable in the sense of fast finite-time uniformly ultimately bounded stability. A fast terminal sliding mode controller is proposed for finite-time convergence of the system. Simulation examples are implemented to show the effectiveness of proposed algorithm.

Power Distribution Control Method in Communication–Free MIMO–WPT Through Load Impedance and Mutual Inductance Estimation

Power Distribution Control Method in Communication–Free MIMO–WPT Through Load Impedance and Mutual Inductance Estimation