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In this article, the issue of exponential stability (ES) is investigated for a class of switched stochastic neural networks (SSNNs) with proportional delay (PD). The key feature of PD is an unbounded time-varying delay. By considering the comparison principle and combining the extended formula for the variation of parameters, we conquer the difficulty in consideration of PD effects for such networks for the first time, where the subsystems addressed may be stable or unstable. New delay-dependent conditions with respect to the mean-square ES of systems are established by employing the average dwell-time (ADT) technique, stochastic analysis theory, and Lyapunov approach. It is shown that the acquired minimum average dwell time (MADT) is not only relevant to the stable subsystems (SSs) and unstable subsystems (USs) but also dependent on the decay ratio (DR), increasing ratio (IR), as well as PD. Finally, the availability of the derived results under an average dwell-time-switched regulation (ADTSR) is illustrated through two numerical simulation examples.

With the variation of parameters, the performance of the buck-boost converters may degrade due to the nonlinear phenomenon such as Neimark-Sacker bifurcation, period doubling bifurcation, even chaos. In this paper, a method to suppress the bifurcation and improve system stability through improving the control principle is indicated. By changing the control principle of the conventional OCC, the proposed one-cycle energy balance control method achieve the one-cycle control by keeping the energy balance of the circuit in each switching cycle. Then the poles of system transfer function are adjusted and the parameter stability range is extended, which achieve the objective of suppressing the bifurcation. Furthermore, such adjustments do not increase the order of the transfer function, thus there is no increase in the computational complexity. Compared with the conventional OCC method, the theoretical analysis based on the sampled-data model demonstrates the ability of the proposed method to suppress the bifurcation and extend the parameter range. Further simulation and experimental results also validate the theoretical analysis.

The steady-state error and torque ripple caused by the mismatch or variation of parameters in the deadbeat predictive current control of permanent magnet synchronous motor are presented in this paper. An improved adaptive deadbeat current predictive model only related to inductance parameters is proposed. The influence of inductance variation on the system stability margin is quantitatively analyzed. When the variation is less than 50%, the system performance can be improved obviously. When the variation reaches 50%, the system will oscillate and cannot operate stably. The feed forward control strategy is introduced to improve the stability margin of the system, and the inductance disturbance of the system oscillation is expanded to 67%. Simulations and experiments are carried out for traditional deadbeat current predictive model, adaptive deadbeat predictive model, and adaptive model with feed forward control. Results show that under various working conditions, the effectiveness of the proposed method in eliminating the steady-state error caused by parameter disturbances is verified. The current distortion and torque ripple are also restrained. Both robustness and steady-state performance of the system are improved.

In this paper, the impulsive effects on projective synchronization between the parameter mismatched neural networks with mixed time-varying delays have been analyzed. Since complete synchronization is not possible due to the existence of parameter mismatch and projective factor, a drive has been taken to achieve the weak projective synchronization of different neural networks under impulsive control strategies. Through the use of matrix measure technique and the extended comparison principle based on the formula of variation of parameters for mixed time-varying delayed impulsive systems, sufficient criteria have been derived for exponential convergence of the networks under the effects of extensive range of impulse. Instead of upper or lower bound of the impulsive interval, the concept of the average impulsive interval is applied to estimate the number of impulsive points in an interval. The concept of calculus is applied for optimizing the synchronization error bounds which are obtained because of different ranges of impulse. Finally, the numerical simulations for various impulsive ranges for different cases are presented graphically to validate the efficiency of the theoretical results.

This paper presents a novel power regulation controller for grid-connected voltage source converter (VSC). The proposed controller is usually applied to achieve smooth active and reactive power dynamics against the variation of parameters. The dynamic performance of grid-connected VSC is sensitive to parameter variation, threatening the safe operation especially when the relative stability is low. Moreover, there is no possibility to modify the dynamic response without the loss of power regulation precision or output voltage and frequency accuracy. In this paper, an improvement in the dynamic response is achieved by introducing feedforward branches into the VSC power regulation controller. Owing to the model reduction mechanism, the VSC provides smooth dynamic response and robustness against the variation of parameters. Simulation and experiment results are presented to prove the effectiveness of this approach, which shows excellent performance as opposed to the conventional controller paradigm.

This paper is concerned with the exponential synchronization issue of nonidentically coupled neural networks with time-varying delay. Due to the parameter mismatch phenomena existed in neural networks, the problem of quasi-synchronization is thus discussed by applying some impulsive control strategies. Based on the definition of average impulsive interval and the extended comparison principle for impulsive systems, some criteria for achieving the quasi-synchronization of neural networks are derived. More extensive ranges of impulsive effects are discussed so that impulse could either play an effective role or play an adverse role in the final network synchronization. In addition, according to the extended formula for the variation of parameters with time-varying delay, precisely exponential convergence rates and quasi-synchronization errors are obtained, respectively, in view of different types impulsive effects. Finally, some numerical simulations with different types of impulsive effects are presented to illustrate the effectiveness of theoretical analysis.

This paper presents an improved procedure in the modeling of photovoltaic (PV) modules based on the single-diode model. This improvement allows more accurate energy yield predictions and performance analysis. Variation of parameters of the PV module model is taken into account, since the output characteristics depend on the surrounding conditions. The analytical expressions of the single-diode model along with experimental data are utilized to support the modeling approach. Moreover, the spectral effects, which influence the output of PV modules, are included in the model. Hence, the PV characteristics in real outdoor operating conditions could be precisely predicted. The effectiveness of the proposed procedure was tested against experimental measurements taken in a PV installation with different commercially available PV modules. The PV model was also validated using real data collected by the SCADA system of a 12-MW PV farm. A comparison with previous methods was made to show the advantages of the proposed model. This model can provide a powerful tool for analysis and appropriate selection of PV systems under changing environmental conditions.

The processes based in microalgae imply new challenges on modelling as well as control, due to the complex nonlinear dynamics and variants during the course of time. These can be cultivated in closed systems called photobioreactors, through which it is possible to control the conditions of the crop, with the purpose of increasing the production of biomass or metabolites. The optimization of the conditions of growth in relation to light is essential for the increase of biomass. In this paper, a proposal is presented to control the intensity of light applied to a batch PBR, using the strategy of active disturbance rejection control (ADRC) with the goal of increasing biomass. Under this paradigm: 1) a substantial simplification of the design of the laws of control is obtained, 2) the complexity of the system is reduced to a linear disrupted system, 3) the high uncertainty of the internal dynamics are worked liked equivalent disturbances at the entrance, 4) the internal and external disturbances that affect the behavior of the system are worked in a unified manner and 5) the estimation of the unified disturbances are done in line by a linear observer of extended state in schemes of control based on the observer. The proposal of control is illustrated and evaluated on a simulation level. In the analysis, the performance of the strategy was contemplated against the variation of parameters. The simulations show the excellent performance against the different sensibility analysis considered, highlighting the strength of the strategy.

Control of an averaged mechanical system with lag where this one is replaced by an approximation of Pade is the central idea of this brief. The inclusion of a linear system does not affect the theory for the symmetric Lie brackets involved hence keeping the convergence of the series for the variation of constants which is used for minimizing the lag effects.

A uniform theory of diffraction (UTD)-physical optics (PO) formulation for multiple-cylinder diffraction analysis is compared with measurements performed at 60 GHz. Thus, the influence of the variation of parameters, such as: the number of cylinders considered, the radius of curvature, the distance from the transmitter to the array of rounded obstacles, and the polarization is analyzed both theoretically and experimentally. The good agreement shown between the predicted and measured results validates such a theoretical solution for the analysis of radiowave propagation when multiple-diffraction over cylindrical surfaces has to be considered.

This paper studies stability problems of a class of impulsive systems with time delay whose linear parts contain unstable system matrices. By using the method of variation of parameters, Lyapunov functions and inequalities, several stability criteria are established for both linear and nonlinear impulsive systems with time delay. It is shown that the time delay systems can be stabilized by impulses even if the system matrices are unstable. Several numerical examples are given to illustrate the results.

Based on investigating the spatially distributed input-output relationship of disinfectant residual in water distribution networks, this brief paper formulates the water quality control problem of multiple nodes in a decomposed adaptive control framework, with special consideration on the periodic variation of parameter uncertainty due to varying consumer demands. The water distribution network is decomposed to subnetworks based on the effect of the decay of chlorine concentration. The periodic parametric uncertainty is represented by a Fourier series with on-line parameter estimation of the unknown coefficients. A simulation example is provided to illustrate the performance of the algorithm in a real water distribution network.

This paper describes the use of a system identification technique to estimate the parameters of a low-order, dynamic model for a power system. The basic technique is to obtain an ARIMAX model by applying a prediction error method to data consisting of 1-s samples of the system frequency and the power output of the Dinorwig fast-response pumped-storage station. From this model the natural frequency, damping factor and stiffness (or "beta") of the power system are obtained. The paper first establishes an appropriate order for the ARIMAX model. Simulation is used to validate the model and ensure that the results are not an artefact of either the input, the identification method or the model structure. It is shown that the precision of the parameter estimates is limited by unknown load disturbances. An example of the use of the technique to analyze the daily variation of parameters is given.

Many practical systems in physics, chemistry, biology, engineering, and information science have impulsive dynamical behaviors due to abrupt changes at certain instants during the dynamical processes. These complex dynamical behaviors can be modeled by impulsive differential systems. This paper studies the controllability and observability issues for a general time-varying impulsive control systems. Sufficient and necessary conditions for state controllability and state observability of the impulsive control systems are established and their applications to time-invariant impulsive control systems are also discussed. Furthermore, several new results associated with variation of parameters for time-varying impulsive control systems are derived.

When eliciting a probability model from experts, knowledge engineers may compare the results of the model with expert judgment on test scenarios, then adjust model parameters to bring the behavior of the model more in line with the experts intuition. This paper presents a methodology for analytic computation of sensitivity values in Bayesian network models. Sensitivity values are partial derivatives of output probabilities with respect to parameters being varied in the sensitivity analysis. They measure the impact of small changes in a network parameter on a target probability value or distribution. Sensitivity values can be used to focus knowledge elicitation effort on those parameters having the most impact on outputs of concern. Analytic sensitivity values are computed for an example and compared to sensitivity analysis by direct variation of parameters. >

A method for the induction heating problem solution in three dimensions is presented. This method couples the transient heat conduction equation, associated to convective and radiative boundary conditions with the electromagnetic system modeled by the A/sup */- Psi - phi method. The nonlinearities associated to the variation of parameters with the temperature as well as the modeling of the Curie point are included. Particular attention is given to the time stepping scheme which allows a time-step variation based on the estimation of the error due to time discretization and to the incomplete solution of the problem's nonlinearity. This generates a method which avoids the need of iterative calculations in each time step of the nonlinear coupled problem. >

The purpose of this note is to study the robustness properties of a specific adaptive pole placement scheme in the presence of bounded disturbances and slow time variation of parameters. We show that the least squares (with block processing) algorithm used in this scheme has the remarkable property of retaining the global stability of the overall system in the presence of bounded disturbances of small mag­nitude and slow time variation of system parameters (within a compact set.)

The present analysis is concerned with the determination of the electromagnetic field and losses in a cylinder of ferromagnetic material. The method consists of finding a linear solution for the diffusion equation governing the H field and then applying the method of variation of parameters to take the nonlinearity into account. The application of the method of averaging reduces the problem to the solution of two first-order ordinary differential equations governing the amplitude and phase variations with depth. The equation governing the averaged phase is readily integrable, while that governing the averaged amplitude is integrated numerically. The results are in excellent agreement with experimental data and with finite-difference numerical solutions.

A procedure has been developed to illustrate the effect of parameter variation on the design of transformers in order to achieve minimum cost of production. It is shown that for a specified value of reactance there is a unique value of coil height for the three-in-line, three-phase transformer studied. With the choice of coil height established, there is also a unique value of primary turns yielding a minimum cost configuration for a specified flux density in the core leg, and a specified current density in the windings. The procedure illustrates also that there are many possible designs within a very small increment of cost. Hence standard wire sizes, steel gauges, and punch limitations can be accommodated. The process can be programmed on a microcomputer to illustrate the design procedure. In this form it is readily taught in option courses on power principles to senior undergraduates.

The system of nonlinear differential equations that model a simple exploding conductor circuit in its initial stages up to the melting point of the conductor is analyzed. Using the method of variation of parameters, approximate analytical solutions are derived that reflect the role of the circuit parameters and initial conditions in shaping the dynamics. A comparison with solutions obtained by numerical integration shows that the approximate solutions are quite accurate over the entire domain of validity of the mathematical model. >