Consensus Control With a Constant Gain for Discrete-time Binary-valued Multi-agent Systems Based on a Projected Empirical Measure Method

This paper studies the consensus control of multiagent systems with binary-valued observations.An algorithm alternating estimation and control is proposed.Each agent estimates the states of its neighbors based on a projected empirical measure method for a holding time.Based on the estimates,each agent designs the consensus control with a constant gain at some skipping time.The states of the system are updated by the designed control,and the estimation and control design will be repeated.For the estimation,the projected empirical measure method is proposed for the binary-valued observations.The algorithm can ensure the uniform boundedness of the estimates and the mean square error of the estimation is proved to be at the order of the reciprocal of the holding time(the same order as that in the case of accurate outputs).For the consensus control,a constant gain is designed instead of the stochastic approximation based gain in the existing literature for binary-valued observations.And,there is no need to make modification for control since the uniform boundedness of the estimates ensures the uniform boundedness of the agents’states.Finally,the systems updated by the designed control are proved to achieve consensus and the consensus speed is faster than that in the existing literature.Simulations are given to demonstrate the theoretical results.

Control of random Boolean networks via average sensitivity of Boolean functions

In this paper, we discuss how to transform the disordered phase into an ordered phase in random Boolean networks. To increase the effectiveness, a control scheme is proposed, which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes. Theoretical analysis is carried out to estimate the expected critical value of the fraction, and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes. Finally, the simulation is given for illustrating the effectiveness of the proposed method.

Estimation, Intervention and Interaction of Multi-agent Systems

在这篇论文，我们在关于多代理人系统的最近的研究上提供简短调查。也就是，我们在研究的三个区域集中于结果评价并且过滤，由外部工具的干预，和交互控制。

Observability conditions of switched linear singular systems

The observability problem of switched linear singular（SLS） systems is studied in this paper. Based on the observability definition, the unobservable subspaces of given switching laws are investigated under the condition that all subsystems are regular. A necessary condition and a sufficient condition for observability of SLS systems are given. It is shown that the observability and controllability are dual for some special SLS systems with circulatory switching laws. The method developed here is applicable to the observability analysis of normal switched linear systems.

On the stabilization of switched linear stochastic systems with unobservable switching laws

This paper is concerned with the stabilization problem of switched linear stochastic systems with unobservable switching laws. In this paper the system switches among a finite family of linear stochastic systems. Since there are noise perturbations, the switching laws can not be identified in any finite time horizon. We prove that if each individual subsystem is controllable and the switching duration uniformly has a strict positive lower bound, then the system can be stabilized by using a controller that uses online state estimation.

Twisting Sliding Mode Control of an Electrostatic MEMS Micromirror for a Laser Scanning System

In this paper,we present a twisting control scheme with proportional-integral-derivative(PID)sliding surface for a two-axis electrostatic torsional micromirror,and the utilization of the proposed scheme in a laser scanning system.The experimental results of set-point regulation verify that the proposed scheme provides enhanced transient response and positioning performance as compared to traditional sliding mode control.To evaluate the tracking performance of the closed-loop system,triangular waves with different frequencies are used as desired traces.With the proposed scheme the experimental results verified that the closed-loop controlled micromirror follows the given triangular trajectories precisely.A micromirror-based laser scanning system is developed to obtain images.When compared with open-loop control,the experimental results demonstrated that the proposed scheme is able to reduce the distortion of the raster scan,and improve the imaging performance in the presence of cross-coupling effect.

Stability and performance analysis of the compressed Kalman filter algorithm for sparse stochastic systems

This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)algorithm.Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space.Subsequently,the original high-dimensional sparse signals can be well recovered by a reconstruction technique.To ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback systems.We further present the performance of the compressed KF algorithm.Specifically,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and optimized.Finally,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.

The structure of canalizing functions

The structure of a canalizing function is discussed. Using a new matrix product, namely semitensor product, the logical function is expressed in its matrix form. From its matrix expression, a criterion is obtained to test whether a logical function is a canalizing function. Then a formula is obtained to calculate the number of canalizing functions. Moreover, an algorithm is presented to generate canalizing functions. Finally, some results obtained are extended to seminested canalizing functions.

Evolution of Two Non-identical Two-Level Atoms in Two-Mode Cavity Fields

在这篇论文，我们在二模式的洞地里调查二个非相同的二水平的原子的进化。我们在一些起始的条件下面为二个非相同的二水平的原子表明死亡和纠纷的再生效果。二个非相同的二水平的原子的纠缠的状态能由纠缠的二模式的洞地被产生，这也被例示。当第一走进一步调查在二模式的洞地里操作二个非相同的二水平的原子的问题，这研究能被考虑。

Parameterized Solution to a Class of Sylvester Matrix Equations

A class of formulas for converting linear matrix mappings into conventional linear mappings are presented. Using them, an easily computable numerical method for complete parameterized solutions of the Sylvester matrix equation AX - EXF = BY and its dual equation XA - FXE = YC are provided. It is also shown that the results obtained can be used easily for observer design. The method proposed in this paper is universally applicable to linear matrix equations.

Guest Editorial for Special Issue on Cyber-Physical Systems

Ⅰ.Introduction CYBER-PHYSICAL system is a system of collaborating computational elements to control physical entities.The coordination and the tight link between computational,virtual and physical resources in cyber-physical system will have a pervasive effect on our everyday life.The development of cyber-physical system will create new opportunities for the introduction of services that will enhance the quality of life

Chaos Control in Random Boolean Networks by Reducing Mean Damage Percolation Rate

Chaos control in random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase.However,controlled nodes are only viewed as passive blocks to prevent perturbation spread.We propose a new control method in which controlled nodes can exert an active impact on the network.Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions.Simulation results show that the number of nodes needed to achieve control is largely reduced compared to the previous method.Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control.

Robust Set Stabilization and Its Instances for Open Quantum Systems

We propose and discuss a novel concept of robust set stabilization by permissible controls; this concept is helpful when dealing with both a priori information of model parameters and different permissible controls including quantum measurements. Both controllability and stabilization can be regarded as the special case of the novel concept. An instance is presented for a kind of uncertain open quantum systems to further justify this gen- eralized concept. It is underlined that a new type of hybrid control based on periodically perturbed projective measurements can be the permissible control of uncertain open quantum systems when perturbed projective measurements are available. The sufficient conditions are given for the robust set stabilization of uncertain quantum open systems by the hybrid control, and the design of the hybrid control is reduced to selecting the period of measurements.

On PID Control Theory for Nonaffine Uncertain Stochastic Systems

PID(proportional-integral-derivative)control is recognized to be the most widely and successfully employed control strategy by far.However,there are limited theoretical investigations explaining the rationale why PID can work so well when dealing with nonlinear uncertain systems.This paper continues the previous researches towards establishing a theoretical foundation of PID control,by studying the regulation problem of PID control for nonaffine uncertain nonlinear stochastic systems.To be specific,a three dimensional parameter set will be constructed explicitly based on some prior knowledge on bounds of partial derivatives of both the drift and diffusion terms.It will be shown that the closed-loop control system will achieve exponential stability in the mean square sense under PID control,whenever the controller parameters are chosen from the constructed parameter set.Moreover,similar results can also be obtained for PD(PI)control in some special cases.A numerical example will be provided to illustrate the theoretical results.

PERFORMANCE EVALUATION FOR DAMPING CONTROLLERS OF POWER SYSTEMS BASED ON MULTI-AGENT MODELS

This paper proposes a multi-layer multi-agent model for the performance evaluation of powersystems,which is different from the existing multi-agent ones.To describe the impact of the structureof the networked power system,the proposed model consists of three kinds of agents that form threelayers:control agents such as the generators and associated controllers,information agents to exchangethe information based on the wide area measurement system (WAMS) or transmit control signals tothe power system stabilizers (PSSs),and network-node agents such as the generation nodes and loadnodes connected with transmission lines.An optimal index is presented to evaluate the performance ofdamping controllers to the system's inter-area oscillation with respect to the information-layer topology.Then,the authors show that the inter-area information exchange is more powerful than the exchangewithin a given area to control the inter-area low frequency oscillation based on simulation analysis.

Estimation of IIR Systems with Binary-Valued Observations

Estimation and control problems with binary-valued observations exist widely in practical systems.However,most of the related works are devoted to finite impulse response(FIR for short)systems,and the theoretical problem of infinite impulse response(IIR for short)systems has been less explored.To study the estimation problems of IIR systems with binary-valued observations,the authors introduce a projected recursive estimation algorithm and analyse its global convergence properties,by using the stochastic Lyapunov function methods and the limit theory on double array martingales.It is shown that the estimation algorithm has similar convergence results as those for FIR systems under a weakest possible non-persistent excitation condition.Moreover,the upper bound for the accumulated regret of adaptive prediction is also established without resorting to any excitation condition.

A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems

We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example.

Distributed Least Squares Algorithm of Continuous-Time Stochastic Regression Model Based on Sampled Data

In this paper,the authors consider the distributed adaptive identification problem over sensor networks using sampled data,where the dynamics of each sensor is described by a stochastic differential equation.By minimizing a local objective function at sampling time instants,the authors propose an online distributed least squares algorithm based on sampled data.A cooperative non-persistent excitation condition is introduced,under which the convergence results of the proposed algorithm are established by properly choosing the sampling time interval.The upper bound on the accumulative regret of the adaptive predictor can also be provided.Finally,the authors demonstrate the cooperative effect of multiple sensors in the estimation of unknown parameters by computer simulations.

Compensated acceleration feedback based active disturbance rejection control for launch vehicles

In this paper, the attitude tracking and load relief control problems against wind disturbances and uncertain aerodynamics as well as the engine thrust of launch vehicles are studied.Firstly, a framework of Compensated Acceleration Feedback based Active Disturbance Rejection Control(CAF-ADRC) is established to achieve both desired attitude tracking and load relief performances. In particular, the total disturbance that includes the effects caused by both aerocoefficient perturbations and disturbances is estimated by constructing an Extended State Observer(ESO) to achieve attitude tracking. Furthermore, combined with the normal acceleration due to the engine thrust, the accelerometer measurement is also compensated to enhance the load relief effect.Secondly, the quantitative analysis of ESO and the entire closed-loop system are studied. It can be concluded that the desired attitude tracking and load relief performances can be achieved simultaneously under the proposed approach. Besides, tuning laws of the proposed approach are systematically given, which are divided into ESO, Proportional Derivative(PD) and Compensated Acceleration Feedback(CAF) modules. Moreover, the performances under CAF-ADRC approach can be better than those under CAF based PD(CAF-PD) approach by tuning load relief gain.Finally, the approach presented is applied to a typical control problem of launch vehicles with wind disturbances and parameter uncertainties.