In this paper, a novel dual adaptive control strategy is proposed for LCL-filtered grid-connected inverters operating under different grid impedance conditions ranging from strong to weak grids. The proposed approach combines two adaptive mechanisms: a hybrid virtual impedance damping controller that adaptively adjusts the virtual resistance, inductance, and capacitance based on real-time grid impedance detection, and an event-triggered second-order phase lead compensator that is activated upon detection of a degraded signal quality. The DSOGI-FLL-based (double generalized second order integrator phase-locked loop) grid impedance estimator continuously monitors the grid condition, while the intelligent event-triggered switching mechanism seamlessly chooses between control strategies based on signal quality analysis. Results show that the methodology tests and realizes the stable operation of the system over the 0-20 mH equivalent inductive grid impedance range, which is much better than the conventional PI control and traditional phase lead compensation. It has excellent dynamic response when the grid impedance changes. Compared with the traditional method, the proposed dual adaptive strategy effectively extends the stable operation range while maintaining high power quality and fast transient response.

The multi-engine parallel inverter system in an is-landed microgrid faces complex and variable load perturbations, and the traditional control strategy is difficult to balance the steady-state accuracy and dynamic response performance of the system. In this paper, an enhanced fourth-order multi-extended state observer (ESO) intelligent regulation of the adaptive virtual impedance (AVI) system is proposed to realize the accurate identification and adaptive suppression of disturbances. The method innovatively constructs a multi-ESO structure acting on frequency, active power, and reactive power, and introduces a dedicated ESO coordinator to realize multi-objective co-optimization, designs a disturbance frequency analysis mechanism based on Fast Fourier Transform (FFT), and intelligently adjusts the ESO bandwidth and prediction model according to the perturbation characteristics, and develops a targeted anti-oscillation mechanism and an adaptive virtual impedance synergistic strategy to enhance the robustness of the system. The simulation results show that the proposed control strategy significantly reduces the voltage overshoot and recovery time during sudden load changes, improves the accuracy of the power distribution, effectively suppresses the circulating current, and especially shows obvious advantages under different disturbance conditions.

This paper presents a cockatiel-inspired hierarchical battery equalization strategy for multi-microgrid energy storage systems. The bio-inspired approach incorporates priority-based SOC processing, environmental perception mechanisms, and intelligent learning capabilities. Compared to traditional methods, the proposed strategy achieves 8.5% faster equalization completion (259.253s vs 283.023s), 55% better voltage regulation (1.6% vs 3.6% deviation), and 74% faster current response (0.931s vs 3.569s). Battery current stress is reduced by 50% (24.2A vs 47.8A), effectively extending battery lifetime while maintaining multi-microgrid stability. The biological coordination paradigm demonstrates superior adaptability and performance for distributed energy storage management.

In this paper, we propose an innovative multimicrogrid control method that organically combines a fast response consensus algorithm with a battery state-of-charge (SOC) equalization strategy. The proposed framework enables collaborative coordination across microgrid networks through strategically weighted communication structures. During scenarios involving rapid load variations and battery state transitions, the system achieves a remarkable response time of 197.22ms, demonstrating over 70% performance enhancement compared to conventional approaches. To mitigate control instabilities encountered during SOC balancing procedures, the methodology incorporates an intersection identification scheme coupled with hierarchical signal conditioning techniques, integrating linear SOC equalization logic and cyclic charging/discharging strategy, which ensures the healthy operation of the batteries while realizing fast response. This unified control approach successfully prolongs energy storage system operational life while preserving voltage regulation and rapid power distribution capabilities, providing a comprehensive solution for efficient cooperative control of distributed microgrid systems.

This paper proposes an active balancing architecture for parallel-connected battery modules in electric vehicle (EV) systems using dual active bridge (DAB) converters combined with MOSFET matrix switching networks to achieve state-of-charge (SOC) balancing without relying on a multi-transformer configuration. Meanwhile, an adaptive unscented Kalman filter (AUKF) is developed to solve the problem of inaccurate SOC estimation under nonlinear module dynamics. The effectiveness of the architecture is demonstrated by the MATLAB/Simulink simulation results: compared with the conventional passive balancing or multi-transformer-based solutions, the proposed DAB-MOSFET switch matrix topology utilizes a single DAB converter and reconfigurable MOSFET switching matrix to achieve a balancing efficiency of 96.4%, which greatly reduces the number of magnetic components and improves the system reliability, while the use of the AUKF estimation algorithm well reduces the impact of measurement noise on the SOC estimation. This work establishes a simulation-proven co-design framework that combines advanced power electronics with adaptive estimation algorithms to provide a scalable and cost-effective solution for large-scale EV battery systems.

This paper proposes a Vehicle-to-Grid (V2G) deployment strategy based on voltage sensitivity analysis to enhance voltage support and system stability in distribution networks with high electric vehicle (EV) integration. A stochastic model captures EV charging variability, and voltage sensitivity at each bus of the IEEE 33-node system guides the design of three spatial deployment strategies: centralized, random, and sensitivity-based distributed allocation. The simulation results show that the sensitivity-based strategy provides the most balanced voltage improvement, eliminating violations across the network. The centralized strategy offers strong but localized support, while the random strategy performs poorly overall. Further analysis under partial V2G node failure scenarios reveals that distributed deployment strategies retain significantly better voltage regulation compared to centralized deployment. These findings confirm that sensitivity-guided distributed V2G allocation offers a practical and robust solution for improving voltage stability and planning future EV integration.

The increasing penetration of electric vehicles (EVs) introduces new challenges to power distribution networks, particularly due to the stochastic and clustered nature of EV charging behaviors. Conventional scheduling approaches often neglect user-level heterogeneity, resulting in inefficiencies in both grid operations and user satisfaction. This paper proposes a user-centric multi-objective scheduling framework based on a novel charging Urgency Index (UI), which quantifies the flexibility of individual EVs. Flexible charging demands are identified via a self-adaptive threshold, upon which a multi-objective optimization model is developed to balance grid performance with user experience. The proposed framework utilizes the Multi-objective Grey Wolf Optimization (MOGWO) to explore Pareto-optimal solutions considering five conflicting objectives: peak-valley gap, load fluctuation, user delay, load correlation, and charging cost. Simulation results validate the effectiveness of the approach in improving grid load profiles and economic outcomes, while preserving fairness and flexibility in EV user scheduling.

The present study proposes two control methods in a multi-agent framework to realize the secondary voltage recovery for distributed heterogeneous energy storage systems (HESSs) in a DC microgrid. The first control method used the advanced secondary voltage recovery (SVR) control strategy. The SVR control employs a consensus-based strategy to balance the State of Charge (SoC), power, and energy across multiple HESSs to regulate the DC bus voltage despite load variations. The second control method uses an online deep reinforcement learning (DRL) algorithm called deep deterministic policy gradient (DDPG) to solve the DC bus voltage regulation by generating the control action. The simulation results show the effectiveness of these two methods and compare the advantages and disadvantages of each.

The imperative for global transformation towards energy sustainability leads to the increasing incorporation of photovoltaic generation into the power systems networks. The increase of photovoltaic (PV) capacity in power system, however, would reduce the system inertial response and may negatively affect the transient stability of the generators. This paper, therefore, proposes a deep reinforcement learning (DRL)-based controller to ensure the transient stability of the power grid with PV penetration. The agent of the proposed Deep Q-Network optimally learns the control actions and adjusts the excitation voltage to maintain the voltage and synchronism in the event of faults. The performance of the controller under different levels of PV penetration and LVRT capabilities are also investigated. The results are compared with that of power systems stabiliser (PSS) to verify the effectiveness. The results show that Deep Q-Network is superior to PSS in maintaining transient stability. Furthermore, the DQN is able to quickly damp out the transient oscillations at various levels of PV penetration and tripping conditions.

The present study proposes a new index to quantify the severity of nonstationary power quality (PQ) disturbance events. In particular, the severity of PQ events is estimated from their energy distribution in temporal-frequency space. The index essentially measures the$\ell _{p}$-norm between the energy distributions of an event and the nominal voltage signal. The efficacy of the new index is demonstrated considering a wide class of major nonstationary PQ events, including sag, swell, interruptions, oscillatory transients, and simultaneous events. The results of this investigation, with simulated, real and experimental data, convincingly demonstrate that the proposed index is generic, monotonic, easy to interpret, and can accurately quantify the severity of nonstationary events.

In this paper, an MPC-based faster joint control method is proposed for hybrid energy storage system (HESS), which consists of battery and supercapacitor in photovoltaic dc-microgrid. The proposed method utilizes the uncompensated power from the battery to improve the dc-link restoration and decrease overshoot. Simulations are conducted to validate the robustness and rapidity of the proposed method, and the results are compared with traditional double-loop PI controllers. The comparison results demonstrate that the new controller has higher dynamic performance and better robustness.

This article proposes a novel discrete event-triggered scheme (DETS) for the synchronization of delayed neural networks (NNs) using the dynamic output-feedback controller (DOFC). The proposed DETS uses both the current and past samples to determine the next trigger, unlike the traditional event-triggered scheme (ETS) that uses only the current sample. The proposed DETS is employed in a dual setup for two network channels to significantly reduce redundant data transmission. A DOFC is designed to achieve the synchronization of the NNs. Stability criteria of the synchronisation error system are derived based on the Lyapunov-Krasovskii functional method, and the co-design of the DOFC and DETS parameters are accomplished using the Cone-complementarity linearization (CCL) approach. The effectiveness and advantages of the proposed method are illustrated considering an example of the chaotic system.

This paper presents a controller, with heuristic intelligence, which focuses on optimizing the occupants’ thermal comfort in a building, by judiciously adjusting the temperature set-points during HVAC system operation. The selection of temperature set-points is formulated as an optimization problem, using suitable cost functions, wherein occupant specified comfort parameters are taken as constraints. The performance of the controller is examined by implementing the control algorithm on a thermal model of a real building, located in New Zealand, developed in EnergyPlus™. Simulation results are compared with that of a PI controller during two seasonal extremes of New Zealand, to further demonstrate the efficacy of proposed controller. The results of investigation show a consistent overall performance of the controller. An annual energy saving of 212.9MW h is achieved without compromising the occupants’ thermal comfort.

IEEE 1588 is a time synchronization protocol that is extensively used by many Cyber-Physical Systems (CPSs). However, this protocol is prone to various types of attacks. We focus on a specific type of Man-in-the-Middle (MITM) attack, where the attacker introduces random delays to the messages being exchanged between a master and a slave. Such attacks have been modelled previously and some mitigation strategies have also been developed. However, the proposed methods work only under constant delay attacks and the developed mitigation strategies are ad-hoc. We propose the first formal framework for modelling and mitigating time delay attacks in IEEE 1588. Initially, the master, the slave and the communication medium are modelled as Timed Automata (TA) assuming the absence of any attacks. Subsequently, a generic attacker is modelled as a TA, which can formally represent various attacks including constant delay, linear delay and exponential delay. Finally, system identification methods of control theory is used to design proportional controllers for mitigating the effects of time delay attacks. We use model checking to ensure the resilience of protocol to time delay attacks using the proposed mitigation strategy.

This article proposes an alternate approach following command-filtered backstepping (CFBS) principle to control the terminal voltage and stabilize the speed of a diesel generator through the excitation system. This controller eliminates the errors introduced due to differentiation of input signal and is, therefore, better compared to the conventional backstepping control technique. The global stability conditions of the overall closed-loop system are established using Lyapunov criteria. The parameters of the controller are optimized using a comprehensive learning particle swarm optimization technique. The performance of the proposed controller is compared with two other controllers, which include a classical proportional integral derivative (PID) and ΔΣ-based PID controllers. The performance comparison using percentage overshoot and settling time demonstrate that the proposed controller is superior and could effectively control the terminal voltage and stabilize the speed under various fault conditions.

This paper proposed a two-stage objective function with genetic algorithm (GA) based tuning on two controller schema - a PD/cascaded P-PD control on a traditional ball and beam system, serving as a prototype for a friction fruit conveyor system. The ball and beam system governed under such controllers is excited with step input, the corresponding system performance factors are captured - rise time, settling time and overshoot. A probabilistic random search on optimum controller parameters is carried with GA method, multiple cost functions - ISE, IAE, ITSE and ITAE, with are evaluated to form a performance cost matrix, which is the first stage of the objective function. The optimum parameter search stops with two conditions; one is that the maximum number of chromosome generation is reached, and the other one is that the performance cost stops improving consecutively for ten generations. The second stage of the objective function is proceeded to decide the found optimum controller parameter solution. The result is decided by taking account of the previously captured performance factors. These factors are normalized and combined with a heuristic weight set to determine a minimum decision cost. The minimum cost chromosome, with ITSE, is the optimum from global found solution space. With two-stage objective function, the GA tuned cascaded P-PD control result on the ball and beam system, meets all system requirements and is satisfactory. Such method can be later implemented on the friction fruit conveyor system for fruit position control and sorting applications.

This paper proposed a two-stage objective function with genetic algorithm (GA) to refine an additional fuzziness layer on dynamic membership intervals of Type-I fuzzy logic control(FLC). The refined dynamic membership intervals Type-I FLC is applied on a traditional ball and beam system, serving as a prototype for a friction fruit conveyor system. The ball and beam system governed under Type-I FLC is excited with step input, the corresponding system performance factors are captured - rise time, settling time and overshoot. A probabilistic random search on optimum controller parameters is carried with GA method, multiple cost functions - ISE, IAE, ITSE and ITAE, with are evaluated to form a performance cost matrix, which is the first stage of the objective function. The optimum parameter search stops with two conditions; one is that the maximum number of chromosome generation is reached, and the other one is that performance cost stops improving consecutively for ten generations. The second stage of the objective function is proceeded to decide the found optimum controller parameter solution. The result is decided by taking account of the previously captured performance factors. These factors are normalized and combined with a heuristic weight set to determine a minimum decision cost. The minimum cost chromosome, with ITAE, is the optimum from global found solution space and sets fixed intervals on the dynamic membership range of Type-I FLC. With two-stage objective function, improved rise time and settling time performance are indicated on the GA tuned Type-I FLC dynamic membership intervals on the ball and beam system than the conventional Type-I FLC, and is satisfactory.

In this research paper, the Multi Agent System (MAS) is introduced to Multi Micro- Grid (MMG) mesh system with the consensus control to share the power between arbitrary inverters to meet the load demand. Each microgrid consists with the Hybrid Energy Storage (HESS) which include the battery and Supercapacitor (SC) to supply/absorb the energy according to the load demand. The consensus based droop characteristics are used with MAS topology to share the power between different microgrids. The overall system consists with five microgrids and they interconnected as meshed network. The implemented control architecture achieve the DC voltage stability among all the microgrids.The system's stability being analysed mathematically using graph theory. MATLAB/Simulink virtual environment is used to simulate the overall system. The Java Agent Development Framework (JADE) is used as the platform to see the status of the agents. The overall simulation results substantiate in different modes of operation and compared with the conventional control method.

This paper investigates some issues associated with the power sharing in the Hybrid Energy Storage System (HESS) in the Multi Micro- Grid System (MMGS) to meet the load demands. To address this problem in isolated microgrids, which often arises in emergency situations, the present study proposes an efficient energy management system (EMS) which operates the battery and supercapacitor (SC) based on their State of Charge (SOC) level using fuzzy logic based control algorithm. The optimal amount of charging/discharging of the battery and SC is decided by the fuzzy inference system (FIS) . Simulations are carried out by creating a microgrid test bench for fuzzy logic system (FLS) in MATLAB/Simulink environment. The performance of the proposed approach is validated considering different modes of operation and loading conditions and found to be satisfactory.

The present study proposes a multi-objective framework for structure selection of nonlinear systems which are represented by polynomial NARX models. This framework integrates the key components of Multi-Criteria Decision Making (MCDM) which include preference handling, Multi-Objective Evolutionary Algorithms (MOEAs) and a posteriori selection. To this end, three well-known MOEAs such as NSGA-II, SPEA-II and MOEA/D are thoroughly investigated to determine if there exists any significant difference in their search performance. The sensitivity of all these MOEAs to various qualitative and quantitative parameters, such as the choice of recombination mechanism , crossover and mutation probabilities , is also studied. These issues are critically analyzed considering seven discrete-time and a continuous-time benchmark nonlinear system as well as a practical case study of non-linear wave-force modeling. The results of this investigation demonstrate that MOEAs can be tailored to determine the correct structure of nonlinear systems . Further, it has been established through frequency domain analysis that it is possible to identify multiple valid discrete-time models for continuous-time systems. A rigorous statistical analysis of MOEAs via performance sweet spots in the parameter space convincingly demonstrates that these algorithms are robust over a wide range of control parameters .

Manufacturing of composites using automated fiber placement (AFP) is a complex process that involves large number of processing conditions and variables. Improper selection of these parameters adversely affects the quality and integrity of the manufactured laminates. Thus, it is important to develop a predictive model that can assess how changes in critical process conditions alter the outputs of the manufacturing process. The goal of this investigation is to learn the complex behavior of composites by developing an intelligent model, which can subsequently be used for the prediction of various characteristics of the composites. However, manufacturing of AFP composites is both expensive and time-consuming and therefore the available data samples are less, from the prospective of machine learning, which leads to the small data learning problem. This article first solves this problem through virtual sample generation, and then a neural network based predictive model is developed to accurately learn the complex relationships between various processing parameters in AFP.

The present study applies reinforcement learning strategy for controller design to improve the low voltage ride through (LVRT) capability of a hybrid power system through convertible static compensator (CSC). This article considers different configurations of CSC such as static synchronous series compensator (SSSC), one static synchronous compensator (STATCOM), two STATCOMs, and unified power flow controller (UPFC). Both Q-learning and dynamic fuzzy Q-learning (DFQL)-based controllers are designed and their performances were compared with classical proportional-integral derivative (PID) controller considering a 3-machine system consisting of 2-synchronous and 1-wind energy systems. The results of simulation show that the performance of DFQL-based controller is better compared to other 2-controllers in improving the LVRT capability. Further, it is shown that the UPFC and two STATCOMs configurations of CSC provide higher voltage support compared to other configurations.

Many applications in control, signal processing and manufacturing require the inverse model of a system. Identification of the inverse of a system (inverse modelling) is often an ill-posed problem and therefore a challenging task. The learning capability of the artificial neural network (ANN) has been exploited in the past to identify the inverse of a system. However, in certain applications, such as manufacturing, where the available data samples are less, the complexity of fitting an inverse model increases significantly. This results in small data learning problem. The present study solves this small data learning problem from the perspective of the inverse system identification using neural networks. Initially, the effectiveness of different combinations of various virtual sample generation (VSG) methods and machine learning tools are investigated to determine the optimum combination which gives the highest learning accuracy. Simulation results are included to demonstrate the effectiveness of ANN in identifying the inverse of various systems from small data.

The present study applies a novel two-dimensional learning framework (2D-UPSO) based on particle swarms for structure selection of polynomial nonlinear auto-regressive with exogenous inputs (NARX) models. This learning approach explicitly incorporates the information about the cardinality (i.e., the number of terms) into the structure selection process. Initially, the effectiveness of the proposed approach was compared against the classical genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO is superior. Further, since the performance of any meta-heuristic search algorithm is critically dependent on the choice of the fitness function, the efficacy of the proposed approach was investigated using two distinct information theoretic criteria such as Akaike and Bayesian information criterion. The robustness of this approach against various levels of measurement noise is also studied. Simulation results on various nonlinear systems demonstrate that the proposed algorithm could accurately determine the structure of the polynomial NARX model even under the influence of measurement noise.

The present study investigates a friction fruit conveyor system development based on a traditional friction-less ball and beam system which share the commonalities of controlling object according to platform angle. Given that the ball and beam system is inherently open-loop unstable, a simple PID controller was designed to stabilize the ball to a predefined position on the beam. In most of the ball and beam literature, the system is assumed to be ideal, friction-free and usually linearized to a simplified model. The analytical model cannot be accurate in real life application. Subsequently, system identification is a standard procedure to estimate its corresponding model for optimal controller designs. With insight from an identified state-space model, parameters such as the number of tapped delay lines and hidden layers are designed. A grey-box neural network system identification with transfer learning is then proposed to identify a nonlinear friction ball and beam system. The identified model is adaptively based on a pre-trained neural network obtained from a linear friction-free BBS mathematical system. The performance of the grey-box identified neural network system model with transfer learning, is then compared with a model obtained from its black-box identified model with neural network structure. Subsequently, a similar procedure will be used to design a grey-box neural network model for fruit conveyor system based on this friction ball and beam. The results of simulation grey-box neural network with transfer learning based on the developed friction ball and beam model system model, is satisfactory.

The present study investigates the small data learning problem and proposes the optimum combination of Virtual Sample Generation (VSG) methods and various machine learning tools which would give highest learning accuracy. Although this small data problem has been studied by various researchers in the recent past, the selection of appropriate VSG methods and machine learning tools, which would yield better accuracy, have comparatively received less attention. This study bridges this gap by investigating the learning performance of three popular VSG methods and five well known machine learning tools. The results of the investigation shows that the learning accuracy is dependent jointly on the choice of the VSG method and the machine learning tool. It has been shown that among all the VSG methods, the Trend Similarity Assessment (TSA) method of VSG when combined with Back Propagation Neural Network (BPNN), gives highest learning accuracy.

This paper proposes an H∞robust state-feedback controller design for uncertain Takagi-Sugeno fuzzy systems using a nonmonotonic Lyapunov function. In the nonmonotonic approach, the monotonicity requirement of the Lyapunov function is relaxed by allowing it to increase locally. Based on the nonmonotonic Lyapunov function approach, sufficient conditions for the existence of a robust state-feedback H∞controller that guarantees stability and a prescribed H∞performance are given in terms of linear matrix inequalities. The proposed design technique is shown to be less conservative than the existing k-samples variations of the Lyapunov function. The effectiveness of the proposed approach is further illustrated via numerical examples.

This paper investigates the probability of a reference minutia repeating in another sample of the same person’s fingerprint, when that probability depends only on the consistency with which a user places their finger onto a fingerprint scanner. The investigation targets cooperative users in a civilian fingerprint recognition application. A database of 800 fingerprint samples from 100 participants was collected for the purpose of simulating such a scenario. Analysis of this database showed that such users are typically sufficiently consistent in the placement of their fingers onto the scanner to ensure that there is a 0.95 probability of a reference minutia repeating in another sample of the same fingerprint. Combining multiple samples of the same fingerprint during enrolment to filter out only the most reliable reference minutiae was shown to improve this probability even further. Additional analysis showed that, as the number of reference fingerprints used increases, the number of reference minutiae remaining for recognition purposes decreases. While this trend is expected, our results indicate that this loss in the number of reference minutiae is not very significant among cooperative users.

Inductive power transfer (IPT) systems are increasingly being used in numerous industrial applications, which essentially require power without any physical contacts. In contrast to unidirectional IPT systems, bidirectional IPT systems are inherently higher-order resonant networks and relatively complex in modeling, design, and control. The complexity further exacerbates with the increase in number of pickups or loads, making design and implementation of such systems a real challenge. This paper, therefore, develops a multivariable dynamic model for a multipickup bidirectional IPT system, which can provide an accurate insight into the behavior of this system and can be used for controller synthesis under variations of component values. The output sensitivity to various parameters and the interaction among various control variables and degree of controllability of the system are investigated using frequency domain analysis. The validity of the model is verified under various operating conditions by comparing the predicted behavior with a 1-kW prototype of a bidirectional IPT system with two pickups. Measured results convincingly demonstrate that the proposed model accurately predicts the dynamic behavior of bidirectional IPT systems with multiple pickups and can, therefore, be used as a valuable tool for both dynamic analysis and controller design.

Bi-directional Inductive Power Transfer (BDIPT) technique is suitable for renewable energy based applications such as electric vehicles (EVs), for the implementation of vehicle-to-grid (V2G) systems. Recently, more efforts have been made by researchers to improve both efficiency and reliability of renewable energy systems to further enhance their economical sustainability. This paper presents a comparative reliability study between a typical BDIPT system and an individually controlled segmented BDIPT system. Steady state thermal simulation results are provided for different output power levels for a 1.5 kW BDIPT system in a MATLAB/Simulink environment. Reliability parameters such as failure rate and mean time between failures (MTBF) are compared between the two systems. A nonlinear programming (NP) model is developed for optimizing charging schedule for a stationery EV. A case study of EV optimum charging is provided for a 24 hours period indicating minimum cost and higher reliability.

ABSTRACT The paper characterizes the hello message exchange (HME) procedure for a sensor node to develop its neighborhood residual energy distribution in the initialization phase of a static wireless sensor network. Because of the lack of coordination on channel access in the initialization phase, hello messages from multiple nodes face a high risk of data collision in the exchange course. A discovery ratio is hereby defined to measure the sufficiency of the HME procedure. The discovery ratio is related to the precision of the parameter estimates for the probability density function of a node's neighborhood residual energy distribution. To achieve an arbitrarily high discovery ratio within a resolvable time interval, the HME procedure is implemented using Birthday protocol, which results in large node energy consumption. To overcome this flaw, a method termed carrier sensing mini‐slot algorithm is proposed to carry out the HME procedure. The time duration and the node energy consumption for the HME procedures based on the Birthday protocol and the carrier sensing mini‐slot algorithm, respectively, are theoretically analyzed and verified by simulations. Copyright © 2011 John Wiley & Sons, Ltd.

This paper investigates the practicability that a sensor node develops the probability density function (pdf) of its local network energy via exchanging hello messages with its neighboring nodes in the context of dense node deployment. The pdf is proven to approach Gaussian and can be used to decentralize a recent clustering algorithm. To alleviate the broadcast storm problem, a node is considered broadcasting hello messages to its neighboring nodes without immediate feedback from the receiving ones. Thus the broadcasting node cannot be guaranteed that its neighboring nodes have received its messages which are at high risk of channel collision. Characterizing hello message exchange becomes nontrivial, as the discovery ratio, which measures the effectiveness and the sufficiency of the message exchange, is identified having decisive effect on the precision of the developed pdf. A set of time asynchronous and slot-based channel access rules is presented for the sufficient and fast exchange of hello messages.

This paper analyses the duration, energy consumption and efficiency of two groups of neighbor discovery protocols dedicated for wireless sensor networks (WSN) with respect to various densities and discovery ratios. The derivation of the mathematical models regarding these parameters can be used for other WSN-oriented neighbor discovery schemes. Numerical results show that significant amounts of time and energy are consumed by the investigated protocols to achieve desirable discovery ratio that may have severe effects on the subsequent network organization, especially in dense scenarios.

An overhead-free, fully distributed clustering algorithm is proposed to decompose wireless sensor networks, where nodes are initialized with either equivalent or different energy capacities, into a two-tier clustered hierarchical structure. Energy-rich nodes are assured to act as cluster heads (CH), and CHs are dispersed evenly over the network. In the new algorithm, a converting function, a multiplicatively increasing CH selection probability, and two backoff strategies are interwoven over three phases during the CH selection and placement. Via simulations, the performance of the proposed algorithm has been demonstrated considering representative network scenarios. The results show that our algorithm outperforms some existing clustering methods in extending the system lifetime and enlarging the network data capacity.

The present study proposes an alternate method of structure selection or which terms to include into a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model, based on evolutionary programming (EP). The algorithm uses a strategy similar to elitism where the single best chromosome in a generation is retained and passed to the next. In addition to minimizing the mean square error (MSE), the method introduces an internal term penalty (ITP) function to reject spurious terms under the effects of significant noise. By following an adaptive mutation rate and restricting this to vary within 50%, faster convergence is achieved. To further improve the convergence, a pruning strategy is followed where any insignificant terms are removed from the model by assigning them with a time-to-live parameter. The performance of the proposed method is illustrated considering several examples of nonlinear systems and have been found to be satisfactory

The proposed study introduces a total least squares with structure selection (TLSS) algorithm to identify continuous time differential equation models from generalized frequency response function matrix (GFRFM) of multiple-input multiple-output (MIMO) nonlinear system. The estimation procedure is progressive where the parameters of each degree of nonlinearity of each subsystem is estimated beginning with the estimation of linear terms and then adding higher order nonlinear terms. The algorithm combines the advantages of both the total least squares and orthogonal least squares with structure selection (OLSSS). The error reduction ratio (ERR) feature of OLSSS are exploited to provide an effective way of detecting the correct model structure or which terms to include into the model and the total least squares algorithm provides accurate estimates of the parameters when the data is corrupted with noise. The performance of the algorithm has been compared with the weighted complex orthogonal estimator and has been shown to be superior