A New Event-Triggered Consensus Control for Microgrids

With the rapid development of vehicular sensor networks (VSNs) technology, the potential use of networked real-time control and automation is enormous and appealing. However, closed-loop control VSNs via the Internet are very difficult to implement practically because of their stochastic nature. One of the biggest challenges in VSNs is how to deal with sensor networks time delay and data loss. The authors investigate the potential of using the predictive control scheme for VSNs based on round-trip time (RTT) delay prediction to overcome the VSNs transmission delay and data loss. The predicted RTT delay is taken as the sampling interval reference for variable-period sampling approach. Modelling the time delay of networked control systems as a non-linear time series, the least mean square (LMS) filter algorithm is adopted to predict online the time delay induced in VSNs. The simulation results show that the LMS algorithm can achieve ideal efficacy if time delay has no variety abnormally. The predictive compensation strategy is proposed to reduce the detrimental effect of stochastic time delays induced by communication networks on control performance. The results of offline simulations via the Internet illustrate that the predictive control scheme based on RTT delay prediction has the potential to overcome the VSNs time delay and data loss.

The role of network structure and time delay in a metapopulation Wilson--Cowan model

Interactive multiview video streaming (IMVS) is an application that streams to a client one out of N available video views for observation, but client can periodically request switches to neighboring views as the video is played back uninterrupted in time. Previous IMVS works focused on the design of a frame structure at encoding time, trading off expected transmission rate with storage, without knowing the exact view trajectory a client may select at stream time. None of the existing IMVS schemes, however, explicitly addressed the network delay problem, and so a client will suffer a round trip time (RTT) delay for each requested view-switch. In this pa- per, we optimize frame structure for a bounded RTT, so that a client can switch to neighboring views as the video is played back without view-switching delay. The key idea is to send additional views likely to be requested by a client within one RTT beyond the current requested view. Each required set of contiguous views (corresponding to a given current requested single view) are pre-encoded using frames of previously transmitted set of views as predictors to lower transmission rate. Using I-, P- and distributed source coding (DSC) frames, we first formulate the structure design problem as a Lagrangian minimization for a desired bandwidth/storage tradeoff. We then develop a low-complexity greedy algorithm to automatically generate a good structure. Experimental results show that for the same storage cost, the transmission rate of the proposed structure can be 42% lower than that of I-frame-only structure, and 8% lower than that of the structure without DSC frames.

In this article, a model-free-adaptive-control-based (MFAC-based) event-triggered (ET) consensus output tracking problem for multiagent systems (MASs) is investigated. The dynamic models of agents are unknown, and only a subset of agents can acquire the reference trajectory. The consensus tracking algorithm is designed by the real-time input/output data and pseudo-partial-derivative (PPD), which is an important parameter of MFAC approach. An output observer is built to design the centralized ET mechanism. Then, the boundedness analysis that the tracking error is uniformly ultimately bounded (UUB) is given. Finally, a simulation experiment is provided to verify the feasibility of the ET consensus output tracking strategy for MASs.

The problem of forward and backward time delays is significantly important for both control and feedback loop of networked control systems. These time delays give rise to latency in performance and thereby may destabilize the system. Therefore numerous methods have been proposed about time delay identification/estimation and compensation for networked control systems, especially for bilateral teleoperation systems. However, most compensation methods have been accomplished by considering offline time delay estimation for linear/nonlinear time delay control systems. In this work, we propose an observer based estimation algorithm for round trip delay which is the sum of forward and backward time delays for a 1 degree-of-freedom nonlinear bilateral teleoperation system. Via Lyapunov based stability analysis, global boundedness of the observer errors along with their ultimate convergence and the convergence of the round trip delay estimator to the vicinity of its real value can be guaranteed in the closed-loop system. Finally, simulation and experimental studies are carried out utilizing the last link of a PHANToM Omni Haptic device moving like a one-link robot in the vertical plane.

In a neuronal system, propagation speed of neuronal information is mainly determined by the length, the diameter, and the kind of the axons between the neurons. Thus, some communications between neurons are not instantaneous, and others are instantaneous or with some negligible delay. In the past years, effects of time delay on neuronal dynamics, such as synchronization, stochastic resonance, firing regularity, etc., have been investigated. For stochastic resonance, it has been reported recently that stochastic multi-resonance in a neuronal system can be induced by time delay. However, in these studies, time delay has been introduced to every connection of the neuronal system. As mentioned in the beginning, in a real neuronal system, communication between some neurons can be instantaneous or with some negligible delays. Thus, considering the effect of partial time delay (time delay is called as partial time delay if only part of connections are delayed) on neuronal dynamics could be more meaningful.In this paper, we focus on discussing effect of partial time delay on response amplitude of a Watts-Strogatz neuronal network which is locally modeled by Rulkov map. With the numerically obtained results, we can see that partial time delay can induce a stochastic multi-resonance which is indicated by the multi-peak characteristics in the variation of response amplitude with partial time delay. Namely, partial time delay could also induce stochastic multi-resonance in a neuronal system. Moreover, we also find that optimal response amplitude can be reached in much wider range of the partial time delay value when delayed connections are less (i.e., the partial time delay probability is small). This is different from the case in which all connections are delayed, where response amplitude become optimal only when time delay is nearly the multiples of external signal's period. But the range of the partial time delay value becomes narrower and narrower with the increasing of the partial time delay probability and when the partial time delay probability is large enough, response amplitude becomes optimal only when time delay is nearly the multiples of external signal period. It is similar to the case where all connections are delayed. Furthermore, effects of random rewiring probability and total link number in the neuronal network on partial time delay induced stochastic multi-resonance are also studied. It is found that partial time delay induced stochastic multi-resonance is robust to random rewiring probability but not robust to total link number. Stochastic resonance is a very important nonlinear phenomenon in neuroscience, thus, our obtained results could have some implications in this field.

Microgrids consist of multiple parallel-connected distributed generation (DG) units with coordinated control strategies, which are able to operate in both grid-connected and islanded modes. Microgrids are attracting considerable attention since they can alleviate the stress of main transmission systems, reduce feeder losses, and improve system power quality. When the islanded microgrids are concerned, it is important to maintain system stability and achieve load power sharing among the multiple parallel-connected DG units. However, the poor active and reactive power sharing problems due to the influence of impedance mismatch of the DG feeders and the different ratings of the DG units are inevitable when the conventional droop control scheme is adopted. Therefore, the adaptive/improved droop control, network-based control methods, and cost-based droop schemes are compared and summarized in this paper for active power sharing. Moreover, nonlinear and unbalanced loads could further affect the reactive power sharing when regulating the active power, and it is difficult to share the reactive power accurately only by using the enhanced virtual impedance method. Therefore, the hierarchical control strategies are utilized as supplements of the conventional droop controls and virtual impedance methods. The improved hierarchical control approaches such as the algorithms based on graph theory, multi-agent system, the gain scheduling method, and predictive control have been proposed to achieve proper reactive power sharing for islanded microgrids and eliminate the effect of the communication delays on hierarchical control. Finally, the future research trends on islanded microgrids are also discussed in this paper.

Markovian jumping systems have arisen naturally in the mathematical modeling of phenomena spanning disciplines in the social sciences, natural sciences, and engineering. This kind of stochastic dynamical systems can be employed to model the dynamics when parameters are subject to random abrupt changes due to sudden environment changes, subsystem switching, system noises, executor faults, and so forth. Much attention has been given to modeling, optimization, and real applications of such stochastic dynamical systems in the literature in recent years. As the advanced control and optimization will provide a basis for the design and application of such stochastic systems, these advanced techniques would result in substantial and sustainable benefits. The accepted papers in this special issue include stochastic stability, stabilization, stochastic control optimization, system modeling and identification methods, predictive control, signal processing, robust filtering, multiagent systems, networked control systems, time-delayed systems, neural networks, the Takagi-Sugeno fuzzy systems, simulated annealing, and fault detection methods.

The main causes of frequency instability or oscillations in islanded microgrids are unstable load and varying power output from distributed generating units (DGUs). An important challenge for islanded microgrid systems powered by renewable energy is maintaining frequency stability. To address this issue, a proportional integral derivative (PID) controller is designed in this article. Firstly, islanded microgrid model is constructed by incorporating various DGUs and flywheel energy storage system (FESS). Further, considering first order transfer function of FESS and DGUs, a linearized transfer function is obtained. This transfer function is further approximated into first order plus time delay (FOPTD) form to design PID control strategy, which is efficient and easy to analyze. PID parameters are evaluated using the Chien-Hrones-Reswick (CHR) method for set point tracking and load disturbance rejection for 0% and 20% overshoot. The CHR method for load disturbance rejection for 20% overshoot emerges as the preferred choice over other discussed tuning methods. The effectiveness of the discussed method is demonstrated through frequency analysis and transient responses and also validated through real time simulations. Moreover, tabulated data presenting tuning parameters, time domain specifications and comparative frequency plots, support the validity of the proposed tuning method for PID control design of the presented islanded model.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

In this paper, a new robust adaptive nonlinear teleoperation system using an improved extended active observer (IEAOB), adaptive Smith predictor (ASP) and sliding mode control is developed to address the time delay in the communication channels and the nonlinear robot model uncertainties. Firstly, an ASP based on Padé approximation and active observer is designed to compensate for the time delay effect. Specifically, the total network time delay is modelled by Padé approximation, and then an active observer is deployed to estimate the time delay. To ensure the time-varying delay effect is completely suppressed, a sliding mode control algorithm is further developed. The main added value of this teleoperation approach is that it requires neither specific mathematic delay-time model, nor strict assumptions on time delay from a practical point of view. Finally, the stability of the designed teleoperation system is theoretically studied and the system effectiveness is demonstrated by applying it to a pair of Phantom Omni haptic devices connected via a communication channel with time-varying delays.

Both time delay and coupling form are the most important factors in neural networks. The properties of firing rate oscillation and cluster synchronization induced by time delay are studied in random network of different coupling neurons. In previous work, the firing rate oscillation of cortical network was observed at the presence of three factors (time delay, weak sinusoidal signal, and noise). Here, we found that the firing rate oscillation can be induced only by the time delay, and the spike train can be propagated at a certain interval time, which is consistent with the value of delay time. Furthermore, the phenomenon of cluster synchronization occurs in random network, which may originates from network structure, and this connection between the neurons trigger spikes within a time-restricted window, resulting in cluster synchronization between corresponding neurons. These numerical results provide a potential theoretical basis for certain pathological brain rhythms associated with epileptic seizures.

Incorporation of wireless technology in Automation system has led to the development of remote monitoring and controlling of loads. It is essential to select and implement an appropriate technology according to the desired application area. This paper describes the various factors viz. network topology, network span and transmission time, which influence the performance of the system. It also illustrates ZigBee (over IEEE 802.15.4) based Home Automation system (HAS), for remote controlling and monitoring of loads/appliances. In order to estimate the total power consumption of the desired load, the designed HAS can be operated in either of the two modes viz. Energy measurement IC ‘CS5490’ based mode and Current sensor IC ‘WCS 2705’ based mode. It provides real time power monitoring with the help of a PC-based GUI application developed on Net Beans Java platform. Further, the paper emphasizes on performance evaluation of the system by analyzing the various performance metrics viz. the Latency, Round Trip Delay (RTD) time and Received Signal Strength Indicator (RSSI).

The uv-coverage of the current VLBI network between Australia northern Asia will be significantly enhanced with an existence of a middle baseline VLBI station located in Malaysia. This paper investigated the connecting route of the first half of the Asia-Oceania VLBI network i.e. from Malaysia to China. The investigation of transmission network characteristics between Malaysia and China was carried out in order to perform a real-time and reliable data transfer within the e-VLBI network for future eVLBI observations. MyREN (Malaysia) and CSTNET (China) high-speed research networks were utilized for this proposed e-VLBI connection. Preliminary network test was performed by ping, traceroute, and iperf prior to data transfer tests, which were evaluated with three types of protocols namely FTP, Tsunami-UDT and UDT. The results showed that, on average, there were eighteen hops between Malaysia and China networks with 98 ms round trip time (RTT) delay. Overall UDP protocol has a better throughput compared to TCP protocol. UDP can reach a maximum rate of 90 Mbps with 0% packet loss. In this feasibility test, the VLBI test data was successfully transferred between Malaysia and China by utilizing the three types of data transfer protocols.

This paper addresses the stabilization and optimization problem of networked control systems (NCSs) with long time delays. According to the actual network conditions, the network time delay is divided into the fixed time delay and the random time delay. Then, the stabilization controller is constructed, where the stabilizing control parameters are obtained with cone complementary linearization (CCL) approach and the optimizing control parameters are solved with estimation of distribution algorithm (EDA) algorithm. Simulation results demonstrate the effectiveness of the proposed methods.