Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.

Indoor INS/UWB-based Human Localization With Missing Data Utilizing Predictive UFIR Filtering

A combined algorithm for the loosely fused ultra wide band(UWB)and inertial navigation system(INS)-based measurements is designed under the indoor human navigation conditions with missing data.The scheme proposed fuses the INS-and UWB-derived positions via a data fusion filter.Since the UWB signal is prone to drift in indoor environments and its outage highly affects the integrated scheme reliability,we also consider the missing data problem in UWB measurements.To overcome this problem,the loosely-coupled INS/UWB-integrated scheme is augmented with a prediction option based on the predictive unbiased finite impulse response(UFIR)fusion filter.We show experimentally that,the standard UFIR fusion filter has higher robustness than the Kalman filter.It is also shown that the predictive UFIR fusion filter is able to produce an acceptable navigation accuracy under temporary missing UWB-data.

Chaos Synchronization of Nonlinear Bloch Equations Based on Input-to-State Stable Control

In this paper, we propose a new input-to-state stable （ISS） synchronization method for chaotic behavior in nonlinear Bloch equations with external disturbance. Based on Lyapunov theory and linear matrix inequality （LMI） approach, for the first time, the ISS synchronization controller is presented to not only guarantee the asymptotic synchronization but also achieve the bounded synchronization error for any bounded disturbance. The proposed controller can be obtained by solving a convex optimization problem represented by the LMI. Simulation study is presented to demonstrate the effectiveness of the proposed synchronization scheme.

L_2-L_∞ learning of dynamic neural networks

This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to not only guarantee asymptotical stability of dynamic neural networks but also reduce the effect of external disturbance to an y2-y∞ induced norm constraint. It is shown that the design of the y2-y∞ learning law for such neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning law.

DC Motor Speed Regulator via Active Damping Injection and Angular Acceleration Estimation Techniques

This paper suggests a novel model-based nonlinear DC motor speed regulator without the use of a current sensor.The current dynamics,machine parameters and mismatched load variations are considered.The proposed controller is designed to include an active damping term that regulates the motor speed in accordance with the first-order low-pass filter dynamics through the pole-zero cancellation.Meanwhile,the angular acceleration and its reference are obtained from simple first-order estimators using only the speed information.The effectiveness is experimentally verified using hardware comprising the QUBEServo2,myRIO-1900,and LabVIEW.

The IOSS Chaos Synchronization Method

A new synchronization method, called the input/output-to-state stable synchronization (IOSSS) method, is proposed for a general class of chaotic systems with external disturbance. By introducing Lyapunov stability theory and linear matrix inequality (LMI) for the first time, the IOSSS controller is shown to not only guarantee the synchronization of the chaotic systems, but also reduce the effect of external disturbance. The proposed IOSSS controller can be obtained by solving the LMI, which can easily be done using standard numerical packages. A numerical example is given to demonstrate the availability of the proposed method.

AC Scheduling Based on Thermodynamics of Indoor for On-Campus Small Data

This paper proposes a new day-ahead control scheme of an air conditioning (AC) based on thermodynamic model of indoor-temperature. The thermodynamic model of indoor-temperature can be achieved by modified first-order thermal dynamic equation. For the practical verification of proposed model, we implemented the home energy management system (HEMS) in the laboratory and used real experiment data sets. The proposed model can be represented by a state-space model of indoor-temperature and its parameters are obtained by least square algorithm. Through the proposed thermodynamic model, indoor-temperature can be predicted closely, and a behavior pattern of AC can also be achieved. This research involves the experimental verification of the proposed approach and communication architecture between the aggregator and a system user in a laboratory environment.

Switched Synchronization with a Guaranteed H∞ Performance

A new H∞ synehronization method is proposed for switched systems. Based on the Lyapunov stability theory and linear matrix inequality （LMI） formulation, an existence condition of the H∞ synchronization controller for switched systems is proposed such that the resulting synchronization error system is asymptotically stable with a guaranteed H∞ performance. It is also shown that the design of the desired controller is achieved by solving a set of LMIs, which can be facilitated efficiently by resorting to standard numerical algorithms. A numerical example with simulation results is provided to illustrate the effectiveness and performance of the developed approach.

Adaptive H∞ Chaos Anti-synchronization

A new adaptive H∞ anti-synchronization （AHAS） method is proposed for chaotic systems in the presence of unknown parameters and external disturbances. Based on the Lyapunov theory and linear matrix inequality formulation, the AHAS controller with adaptive laws of unknown parameters is derived to not only guarantee adaptive anti-synchronization but also reduce the effect of external disturbances to an H∞ norm constraint. As an application of the proposed AHAS method, the H∞anti-synchronization problem for Genesio-Tesi chaotic systems is investigated.

仿生机器人系统:建模、设计、控制和感知专题简介

仿生机器人是指模仿生物特征的一类机器人,具有运动灵活性高、环境适应性强等特点,在救灾救援、公共安全、社会服务、国防军工等领域具有广泛的应用前景.当前仿生机器人在物理结构、机动性能方面与仿生对象相比仍然存有差距,亟待系统深入地对仿生机器人系统中的共性科学问题和关键技术开展研究,推动仿生机器人的应用与发展.

Stabilization of linear systems with distributed input delay using reduction transformation

We propose a new stabilization method for linear systems with distributed input delay via reduction transformation and Riccati equation approach.In the presented stabilization scheme,the gain matrix of controller is constructed by the well-known linear control technique for delay-free systems.The transformation kernel matrix can be determined by solving the non-symmetric matrix Riccati equation backward with the boundary condition.When point delay systems are considered,it will be shown that the proposed control law degenerates to the standard one for input delay systems.