Connectivity-maintaining Consensus of Multi-agent Systems With Communication Management Based on Predictive Control Strategy

This paper studies the connectivity-maintaining consensus of multi-agent systems.Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved.In this paper,communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus.The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller,so there is more freedom to develop an appropriate control strategy for achieving consensus.For the multi-agent systems with this novel communication management,a predictive control based strategy is developed for achieving consensus.Simulation results indicate the effectiveness and advantages of our scheme.

Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization methods.However,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical foundations.This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used.In particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period problems.Lastly,we recommend some potential future directions for online optimization in the power systems domain.

An Optimal Control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

Output Feedback Based Admissible Control of Switched Linear Singular Systems

The admissibility analysis and robust admissible control problem of the uncertain discrete-time switched linear singular (SLS) systems for arbitrary switching laws are investigated. Based on linear matrix inequalities, some sufficient conditions are given for: A) the existence of generalized common Lyapunov solution and the admissibility of the SLS systems for arbitrary switching laws, B) the existence of static output feedback control laws ensuring the admissibility of the closed-loop SLS systems for arbitrary switching laws and norm-bounded uncertainties.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

In this paper,distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed,which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement.Based on the feedback of the bus voltage,the deviation of the current is dispatched to each DG according to cost over the prediction horizon.Moreover,to avoid the excessive fluctuation of the battery power,both the discharge-charge switching times and costs are considered in the model predictive control(MPC) optimization problems.A Lyapunov constraint with a time-varying steady-state is designed in each local MPC to guarantee the stabilization of the entire system.The voltage stabilization of the MG is achieved by this strategy with the cooperation of DGs.The numeric results of applying the proposed method to a MG of the Shanghai Power Supply Company shows the effectiveness of the distributed economic MPC.

3D Localization for Multiple AUVs in Anchor-Free Environments by Exploring the Use of Depth Information

Dear Editor,This letter investigates the cooperative localization problem for multiple autonomous underwater vehicles(AUVs)in underwater anchor-free environments,where AUV localization errors grow without bound due to the accumulated errors in inertial measurements(termed accumulated errors hereafter)and the lack of anchors(with known positions).

Effects of quantum noise on teleportation of arbitrary two-qubit state via five-particle Brown state

We propose a new protocol for quantum teleportation(QT)which adopts the Brown state as the quantum channel.This work focuses on the teleportation of a single unknown two-qubit state via a Brown state channel in an ideal environment.To validate the effectiveness of our proposed scheme,we conduct experiments by using the quantum circuit simulator Quirk.Furthermore,we investigate the effects of four noisy channels,namely,the phase damping noise,the bit-flip noise,the amplitude damping noise,and the phase-flip noise.Notably,we employ Monte Carlo simulation to elucidate the fidelity density under various noise parameters.Our analysis demonstrates that the fidelity of the protocol in a noisy environment is influenced significantly by the amplitude of the initial state and the noise factor.

Distributed Stretching Control of Web Production System

The stretching process,as a key phase of web production system,pursues the target velocities of rollers and the web tensions of spans between the successive rollers to guarantee proper stretching ratios. This requires the stable velocities and velocity ratios of large number rollers separated throughout the workshop. To this goal,a distributed cooperative controller is designed to coordinate the velocities of the rollers to the desired values as well as the target ratios between the upper and lower rollers. During the whole evolution,only the neighbor rollers can exchange the working information,and neither global information nor central controller is required. It is proven that all the rollers asymptotically achieve the desired velocity ratios via the proposed control law,which is also demonstrated by numerical simulation.

A Comprehensive Overview of Cyber-Physical Systems: From Perspective of Feedback System

Cyber-physical systems (CPS) are characterized by integrating cybernetic and physical processes. The theories and applications of CPS face the enormous challenges. The aim of this paper is to provide a latest understanding of this emerging multi-disciplinary methodology. First, the features of CPS are described, and the research progresses are summarized from different components in CPS, such as system modeling, information acquisition, communication, control and security. Each part is also followed by the future directions. Then some typical applications are given to show the prospects of CPS. © 2014 Chinese Association of Automation.

Sliding Mode Predictive Control of Main Steam Pressure in Coal-fired Power Plant Boiler

Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying, strongly coupled, and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportional integral derivative (PID) control scheme. For the characteristics of the main steam pressure in coal-fired power plant boiler, the sliding mode control system with Smith predictive structure is proposed to look for performance and robustness improvement. First, internal model control (IMC) and Smith predictor (SP) is used to deal with the time delay, and sliding mode controller (SMCr) is designed to overcome the model mismatch. Simulation results show the effectiveness of the proposed controller compared with conventional ones.

Trajectory Tracking of Vertical Take-off and Landing Unmanned Aerial Vehicles Based on Disturbance Rejection Control

We investigate the trajectory tracking problem of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV), and propose a practical disturbance rejection control strategy. Firstly, the nonlinear error model is established completely by the modified Rodrigues parameters, while considering dynamics of the servo actuators. Then, a hierarchical control scheme is applied to design the translational and rotational controllers based on the time-scale property of each subsystem, respectively. And the linear extended state observer and auxiliary observer are used to deal with the uncertainties and saturation. At last, global stability of the closed-loop system is analyzed based on the singular perturbation theory. Simulation results show the effectiveness of the proposed control strategy. © 2014 Chinese Association of Automation.

Water Supply Networks as Cyber-physical Systems and Controllability Analysis

Cyber-physical systems (CPS) is a system of systems which consists of many subsystems that can stand alone in an individual manner and can be taken as a typical complex network. CPS can be applied in the critical infrastructures such as water supply networks, energy supply systems, and so on. In this paper, we analyze the structure of modern city water supply networks from the view of CPS theory. we use complex network theory to build an undirected and unweighted complex network model for the water supply networks to investigate the structural properties, and present the structure of the water supply networks and detect communities by a spectral analysis of the Laplacian matrix. Then, we analyze the structure and controllability of water supply networks by the structural controllability method. The results show the feasibility and effectiveness of the proposed complex network model. © 2014 Chinese Association of Automation.

Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.

Energy Efficient Predictive Control for Vapor Compression Refrigeration Cycle Systems

Abstract--Vapor compression refrigeration cycle （VCC） system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly, according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control （MPC） based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance （COP） which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity. The effectiveness of the control performance is validated on the experimental rig. Index Terms--Cooling load, model predictive control （MPC）, superheat, vapor compression refrigeration cycle （VCC）.

An Improved Robust Model Predictive Control Approach to Systems with Linear Fractional Transformation Perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.

Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

In coking process, the production quality, equipment life, energy consumption, and process safety are all influenced by the pressure in gas collector pipe of coke oven, which is frequently influenced by disturbances.The main control objectives for the gas collector pressure system are keeping the pressures in collector pipes at appropriate operating point. In this paper, model predictive control(MPC) strategy is introduced to control the collector pressure system due to its ability to handle constraint and good control performance. Based on a method proposed to simplify the system model, an extended state space model predictive control is designed,which combines the feedforward strategy to eliminate the disturbance. The simulation results in a system with two coke ovens show the feasibility and effectiveness of the control scheme.

Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation

For large-scale networked plant-wide systems composed by physically(or geographically) divided subsystems, only limited information is available for local controllers on account of region and communication restrictions. Concerning the optimal control problem of such subsystems, a neighbor-based distributed model predictive control(NDMPC) strategy is presented to improve the global system performance. In this scheme, the performance index of local subsystems and that of its neighbors are minimized together in the determination of the optimal control input, which makes the local control decision also beneficial to its neighboring subsystems and further contributes to improving the convergence and control performance of overall system.The stability of the closed-loop system is proved. Moreover, the parameter designing method for distributed synthesis is provided.Finally, the simulation results illustrate the main characteristics and effectiveness of the proposed control scheme.