Bounded synchronization of complex dynamical networks with prescribed performance via event‐based transmission strategy

This paper addresses a novel prescribed performance synchronization of complex dynamical networks by means of event‐triggered communication protocols. With the designed controller and proposed even‐triggered communication strategy, this paper have advantages of guaranteeing the transient and steady‐state performance of complex dynamic networks and avoiding the continuous communication of networks for reducing the number of communications and saving the network resources. In addition, the Zeno behavior is avoided in the networks. At last, the effectiveness of the theoretic results obtained is verified via the application in the complex dynamical network with Chua's circuit.     

Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

Hierarchical fusion robust Kalman filter for clustering sensor network time‐varying systems with uncertain noise variances

For the clustering time‐varying sensor network systems with uncertain noise variances, according to the minimax robust estimation principle, based on the worst‐case conservative system with conservative upper bounds of noise variances, applying the optimal Kalman filtering, the two‐level hierarchical fusion time‐varying robust Kalman filter is presented, where the first‐level fusers consist of the local decentralized robust fusers for the clusters, and the second‐level fuser is a global decentralized robust fuser for the cluster heads. It can reduce the communication load and save energy resources of sensors. Its robustness is proved by the proposed Lyapunov equation method. The concept of robust accuracy is presented, and the robust accuracy relations of the local, decentralized, and centralized fused robust Kalman filters are proved. Specially, the corresponding steady‐state robust local and fused Kalman filters are also presented, and the convergence in a realization between the time‐varying and steady‐state robust Kalman filters is proved by the dynamic error system analysis method. A simulation example shows correctness and effectiveness. Copyright © 2013 John Wiley & Sons, Ltd.     

A novel variable‐length sliding window blockwise least‐squares algorithm for on‐line estimation of time‐varying parameters

Motivated by the advances in computer technology and the fact that the batch/block least‐squares (LS) produces more accurate parameter estimates than its recursive counterparts, several important issues associated with the block LS have been re‐examined in the framework of on‐line identification of systems with abrupt/gradual change parameters in this paper. It is no surprise that the standard block LS performs unsatisfactorily in such a situation. To overcome this deficiency, a novel variable‐length sliding window‐based LS algorithm, known as variable‐length sliding window blockwise least squares, is developed. The algorithm consists of a change detection scheme and a data window with adjustable length. The window length adjustment is triggered by the change detection scheme. Whenever a change in system parameters is detected, the window is shortened to discount ‘old’ data and place more weight on the latest measurements. Several strategies for window length adjustment have been considered. The performance of the proposed algorithm has been evaluated through numerical studies. In comparison with the recursive least squares (RLS) with forgetting factors, superior results have been obtained consistently for the proposed algorithm. Robustness analysis of the algorithm to measurement noise have also been carried out. The significance of the work reported herein is that this algorithm offers a viable alternative to traditional RLS for on‐line parameter estimation by trading off the computational complexity of block LS for improved performance over RLS, because the computational complexity becomes less and less an issue with the rapid advance in computer technologies. Copyright © 2004 John Wiley & Sons, Ltd.     

A fully‐integrated CMOS UWB transceiver for ultra‐low‐power short‐range application

In short‐range UWB communication systems, the low‐power design is the most important issue to make UWB technology attractive. A novel trigger receiving algorithm for UWB signals is proposed, which can reduce the system power significantly at the cost of slight performance degrade. A UWB transceiver based on the trigger receiving algorithm is designed and fabricated in HJTC 0.18 µm CMOS process with a total size of 0.45 mm². The experimental results show that the total power consumption of the transceiver is only 12 mW at 100 Mb/s data rate from a 1.8 V supply, making it suitable for low‐power short‐range communication. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluation model for multi‐microgrid with autonomous DC energy exchange

This paper proposes an evaluation model to analyze the impact of microgrid topologies on self‐sufficiency for a given size of batteries and photovoltaic (PV) panels (resources). Three topologies are evaluated for a community of 19 houses: centralized resources (ideal case), stand‐alone resources, and a multi‐microgrid topology with autonomous exchange. Depending on the ratio of PV and battery size, the topology with stand‐alone resources has a clear disadvantage in terms of self‐sufficiency compared to the centralized, ideal topology. To counteract this, we propose a hybrid topology: households are interconnected so that they can exchange energy between each other based on an autonomous energy exchange algorithm we developed. We show that for a well‐chosen ratio of batteries and PV, the interconnected system can improve the stand‐alone design by up to 10% without requiring any additional resources. This topology can approach performance similar to that of a centralized microgrid but its design is more flexible and resilient to failures or accidents. The evaluation model computes the self‐sufficiency ratio (SSR) for the three topologies for 0–20 kWh batteries and 1–14 kWp PV sizes. Furthermore, seasonal differences in SSR per topology are analyzed for an actual community with real resources. We also calculate the savings in PV and battery due to the interconnected topology. Finally, the third topology's feasibility is demonstrated on a full‐scale platform in Okinawa on which the autonomous energy exchange software was tested for over a year in a community of 19 houses. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

FPGA‐based implementation for improved control scheme of grid‐connected PV system with 3‐phase 3‐level NPC‐VSI

The large scale penetration of renewable energy resources has boosted the need of using improved control technique and modular power electronic converter structures for efficient and reliable operation of grid‐connected systems. This study investigates the performance of a grid‐connected 3‐phase 3‐level neutral‐point clamped voltage source inverter for renewable energy integration by using improved current control technique. For medium or high‐voltage grid interfacing, the multilevel inverter structure is generally used to reduce the voltage stress across the switching device as well as the harmonic distortion. The neutral‐point clamped voltage source inverter is controlled by using decoupling technique along with the proper grid synchronization via moving average filter–based phase‐locked loop. The moving average filter–based phase‐locked loop is used to reduce the delay in grid angle estimation under balanced as well as distorted grid conditions. A Lyapunov‐based approach for analysing the stability of the system has also been discussed. In this study, the hardware‐in‐loop (HIL) simulation of the control algorithm and the grid synchronization technique is realized using Virtex‐6 FPGA ML605 evaluation kit. The performance of the system is analyzed by conducting a time‐domain simulation in the Matlab/Simulink platform and its performance is examined in the HIL environment. The simulation and the hardware cosimulation results are presented to validate the effectiveness of the proposed control scheme.     

An indirect adaptive pole‐placement control for MIMO discrete‐time stochastic systems

In this paper, an indirect adaptive pole‐placement control scheme for multi‐input multi‐output (MIMO) discrete‐time stochastic systems is developed. This control scheme combines a recursive least squares (RLS) estimation algorithm with pole‐placement control design to produce a control law with self‐tuning capability. A parametric model with a priori prediction outputs is adopted for modelling the controlled system. Then, a RLS estimation algorithm which applies the a posteriori prediction errors is employed to identify the parameters of the model. It is shown that the implementation of the estimation algorithm including a time‐varying inverse logarithm step size mechanism has an almost sure convergence. Further, an equivalent stochastic closed‐loop system is used here for constructing near supermartingales, allowing that the proposed control scheme facilitates the establishment of the adaptive pole‐placement control and prevents the closed‐loop control system from occurring unstable pole‐zero cancellation. An analysis is provided that this control scheme guarantees parameter estimation convergence and system stability in the mean squares sense almost surely. Simulation studies are also presented to validate the theoretical findings. Copyright © 2005 John Wiley & Sons, Ltd.     

Null‐space‐based optimal control reallocation for spacecraft stabilization under input saturation

A novel saturated proportional‐derivative control incorporated with null‐space‐based optimal control reallocation is proposed for spacecraft attitude stabilization in the presence of disturbance and input saturation. More specifically, a saturated proportional‐derivative based baseline nonlinear controller is firstly developed to guarantee the globally asymptotic stability under input constraints and external disturbance. This is achieved with inexpensive online computations by dynamically adjusting a single parameter to ensure the desired performance. Then, a novel null‐space‐based optimal control reallocation method is employed to map the specified virtual control command to the redundant actuators. The optimal control solution is obtained by penalizing the control allocation errors at a lower power/energy cost using quadratic programming algorithm. The benefits of the proposed control method are analytically authenticated and also validated via simulation study. Copyright © 2014 John Wiley & Sons, Ltd.     

Output‐feedback model‐reference adaptive calibration for map‐based anti‐jerk control of electromechanical automotive clutches

It is well known that the map‐based control can reduce the computational burden of the automotive on‐board controller. This paper proposes an output‐feedback model‐reference adaptive control algorithm to calibrate the map‐based anti‐jerk controller for electromechanical clutch engagement. The algorithm can be used to adaptively construct a data‐driven fuzzy rule base without resorting to manual tuning, so that it can overcome the problem of conventional knowledge‐based fuzzy logic design, which involves strenuous parameter‐tuning work in the construction of calibration maps. To accurately define the consequent of each fuzzy rule for anti‐jerk control, an output feedback law for computing the reference trajectory of clutch engagement is developed to eliminate the discontinuous slip‐stick transition, whereas an adaptive controller is designed to track the reference trajectory and compensate the nonlinearity. The convergence of the proposed output‐feedback model‐reference adaptive control algorithm is analyzed. Simulation results indicate that the proposed method can successfully reduce the excessive vehicle jerk and frictional energy dissipation during clutch engagement as compared with the conventional knowledge‐based fuzzy logic controller without fine tuning.     

Asynchronous distributed state estimation based on a continuous‐time stochastic model

In this paper, the problem of state estimation in an asynchronous distributed multi‐sensor estimation (ADE) system is considered. In such an ADE system, the state of a plant of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs fusion of data from its local sensor and other (remote) processors to compute possibly best state estimates. In performing data fusion, however, two important issues need to be addressed, namely, the problem of asynchronism of local processors and the one of unknown correlation between asynchronous data in local processors. Consequently, there are two main contributions proposed in this paper. The first is a method to deal with asynchronous discrete‐time data based on a continuous‐time stochastic plant model. The second contribution is an asynchronous distributed data‐fusion algorithm. Simulated experiments illustrate the effectiveness of the proposed ADE approach. Copyright © 2011 John Wiley & Sons, Ltd.     

拒绝服务攻击下基于分布式事件触发一致性预测补偿的微电网能量优化管理

针对微电网下电力系统通信易受到拒绝服务攻击(denial of service attack,DoS)、局部信息不开放以及通信带宽受限等问题,提出了一种基于DoS攻击的分布式事件触发的无模型预测补偿能量优化管理控制方法。首先,为了优化微电网能量供给并使得获利最大,给出考虑微电网功率损失的维持供需平衡的最小成本函数;其次,将微电网中的每个部分看作一个智能体并考虑了通信带宽受限问题,提出了一种分布式事件触发一致性算法;随后,提出一种基于输入输出数据的无模型预测控制算法,利用跟踪攻击前时刻的供需不匹配功率来预测补偿当前时刻及其后多个时刻的智能体功率数据缺失;最后,通过仿真实例验证所提出的基于分布式事件触发一致性预测补偿的微电网能量管理方法的有效性。     

Energy‐efficient cooperative localization in mobile WSN

Design and implementation of an energy‐efficient protocol is one of the main challenges in wireless sensor networks (WSNs). In terms of localization, both energy efficiency and accuracy must be addressed to achieve the final goals of localization. In mobile sensor nodes, where battery power is the most hardware resource limitation, accurate localization needs to be extremely energy efficient. In this work, a virtual multiple‐input multiple‐output (VMIMO) technique is deployed to tackle the problems of getting more energy efficiency and higher accuracy simultaneously.In this case, the optimum selection of the number of transceiver nodes can be obtained with the lowest possible total energy consumption, localization error, and speed of nodes. In addition, VMIMO decreases the power of transmitters, and therefore will lead to the reduction of destructive effects of electromagnetic sensitivity (EMS) on the body. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

含扩展状态观测器的双馈风机事件触发滑模控制（撤销发表）

提出一种基于事件触发滑模控制(SMC)的双馈风机(DFIG)直接功率控制方法。传统的控制方法需要控制信号执行元件不断更新由计算单元传输的控制信号,而这种控制信号执行方式较为冗余且需要额外的通信和执行成本。事件触发控制原理能够决策所执行控制信号的最佳更新时间节点,并在保证控制效果的同时减少计算单元与执行单元之间的通信。将事件触发控制的原理与SMC结合起来,并用于DFIG的直接功率控制中。同时,设计了一种让系统具有主动抗干扰能力的扩展状态观测器。采用Lyapunov函数法验证了该控制系统的渐近稳定性。最后通过试验算例,验证了所提方法的有效性。     

Controlling the renewable microgrid using semidefinite programming technique

Given the significant concerns regarding carbon emissions from fossil fuels, global warming and energy crisis, renewable distributed energy resources (DERs) are going to be integrated in smart grids, which will make the energy supply more reliable and decrease the costs and transmission losses. Unfortunately, one of the key technical challenges in power system planning, control and operation with DERs is the voltage regulation at the distribution level. This problem stimulates the deployment of smart sensors and actuators in smart grids so that the voltage can be stabilized. The observation from the microgrid incorporating DERs is transmitted to the control center via wireless communication systems. In other words, the proposed communication infrastructure provides an opportunity to address the voltage regulation challenge by offering the two-way communication links for microgrid state information collection, estimation and stabilization. Based on the communication infrastructure, we propose a least square based Kalman filter algorithm for state estimation and an optimal feedback control framework for stabilizing the microgrid states. Specifically, we propose to optimize the performance index by using semidefinite programming techniques in the context of smart grid applications. At the end, the efficacy of the developed approaches is demonstrated using a microgrid incorporating multiple DERs.     

On‐line parameter estimation for a class of time‐varying continuous systems with bounded disturbances

In this paper, we proposed an on‐line parameter estimation algorithm for a class of time‐varying continuous systems with bounded disturbance. In this method, a novel polynomial approximator with a bounded regressor vector is constructed and utilized to approximate the time‐varying parameters. The direct least‐squares algorithm is employed to acquire the on‐line estimates, so that several useful properties of the direct estimation, such as fast convergence and robustness to the bounded disturbance, are reflected in our method. We have proved that the estimation error of this method is bounded. Furthermore, the bound on the Euclidean norm of the estimation error is derived. The simulation results demonstrate that this method can provide accurate estimates of time‐varying parameters even under the influence of bounded disturbance. Copyright © 2010 John Wiley & Sons, Ltd.     

Parallel FETI‐DP algorithm for efficient simulation of large‐scale EM problems

An efficient parallelization of the dual‐primal finite‐element tearing and interconnecting (FETI‐DP) algorithm is presented for large‐scale electromagnetic simulations. As a nonoverlapping domain decomposition method, the FETI‐DP algorithm formulates a global interface problem, whose iterative solution is accelerated with a solution of a global corner problem. To achieve a good load balance for parallel computation, the original computational domain is decomposed into subdomains with similar sizes and shapes. The subdomains are then distributed to processors based on their close proximity to minimize inter‐processor communication. The parallel generalized minimal residual method, enhanced with the iterative classical Gram‐Schmidt orthogonalization scheme to reduce global communication, is adopted to solve the global interface problem with a fast convergence rate. The global corner‐related coarse problem is solved iteratively with a parallel communication‐avoiding biconjugate gradient stabilized method to minimize global communication, and its convergence is accelerated by a diagonal preconditioner constructed from the coarse system matrix. To alleviate neighboring communication overhead, the non‐blocking communication approach is employed in both generalized minimal residual and communication‐avoiding biconjugate gradient stabilized iterative solutions. Three numerical examples are presented to demonstrate the accuracy, scalability, and capability of the proposed parallel FETI‐DP algorithm for electromagnetic modeling of general objects and antenna arrays. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi‐objective PMU placement optimization considering the placement cost including the current channel allocation and state estimation accuracy

The optimal phasor measurement unit (PMU) placement problem in power systems has been considered and investigated by many researchers for accurate and fast state estimation by PMUs. However, the current channel cost of the PMU affects the total placement cost. This paper proposes a novel formulation in the multi‐objective optimal PMU placement, which minimizes the PMU placement cost with the current channel selection and the state estimation error. The current channel selection is represented as a decision variable in the optimization. For trade‐off objective functions, the Pareto approach by nondominated sorting genetic algorithm II (NSGA‐II) is applied in the optimization. The result of the numerical experiment in this paper demonstrates the advantage of considering the appropriate PMU current channel allocation, compared with the conventional method that ignores it, in the modified IEEE New England 39‐bus test system. As a result, the proposed method obtained a better Pareto solution compared with the conventional one because of the consideration for the current channel selection. An advantage of the proposed PMU placement is that it is able to reduce the total PMU placement cost while maintaining the state estimation accuracy.     

An adaptive sliding‐mode observer for a class of uncertain nonlinear systems

In this paper, the problem of simultaneous state and parameter estimation is studied for a class of uncertain nonlinear systems. A nonlinear adaptive sliding‐mode observer is proposed based on a nonlinear parameter estimation algorithm. It is shown that such a nonlinear algorithm provides a rate of convergence faster than exponential, ie, faster than the classic linear algorithm. Then, the proposed parameter estimation algorithm is included in the structure of a sliding‐mode state observer, providing an ultimate bound for the full estimation error and attenuating the effects of the external disturbances. Moreover, the synthesis of the observer is given in terms of linear matrix inequalities. The corresponding proofs of convergence are developed based on the Lyapunov function approach and input‐to‐state stability theory. Some simulation results illustrate the efficiency of the proposed adaptive sliding‐mode observer.     

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

In this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.