Order Dynamic Model Research Articles

An Online Simplified Nonlinear Controller for Transient Stabilization Enhancement of DFIG in Multi-Machine Power Systems

An adaptive nonlinear controller for transient stability and voltage regulation of power systems based DFIG in multimachine configuration is presented using a standard third order dynamical model of the DFIG. Finite time estimators for the unmeasurable time derivative of the quadrature component of the DFIG stator current, mechanical input, unknown direct axis transient open circuit time constant (function of the rotor resistance) are presented. The main feature of the proposed control scheme is its robustness with respect to large perturbations and parameter variations. Numerical results are presented to illustrate the performance of the proposed control scheme and its robustness properties.

Estimation of the shear force in transverse dynamic force microscopy using a sliding mode observer

In this paper, the problem of estimating the shear force affecting the tip of the cantilever in a Transverse Dynamic Force Microscope (TDFM) using a real-time implementable sliding mode observer is addressed. The behaviour of a vertically oriented oscillated cantilever, in close proximity to a specimen surface, facilitates the imaging of the specimen at nano-metre scale. Distance changes between the cantilever tip and the specimen can be inferred from the oscillation amplitudes, but also from the shear force acting at the tip. Thus, the problem of accurately estimating the shear force is of significance when specimen images and mechanical properties need to be obtained at submolecular precision. A low order dynamic model of the cantilever is derived using the method of lines, for the purpose of estimating the shear force. Based on this model, an estimator using sliding mode techniques is presented to reconstruct the unknown shear force, from only tip position measurements and knowledge of the excitation signal applied to the top of the cantilever. Comparisons to methods assuming a quasi-static harmonic balance are made.

Nonlinear Dynamic Analysis of Cantilever Beam Using POD Based Reduced Order Model

In this paper, a reduced order model is obtained for nonlinear dynamic analysis of a cantilever beam. Nonlinearity in the system is basically due to large deformation. A reduced order model is an efficient method to formulate low order dynamical model which can be obtained from data obtained from numerical technique such as finite element method (FEM). Nonlinear dynamical models are complex with large number of degrees of freedom and hence, are computationally intensive. With formulation of reduced order models (i.e. Macromodels) number of degrees of freedom are reduced to fewer degrees of freedom by using projection based method like Galerkin’s projection, so as to make system computationally faster and cost effective. These macromodels are obtained by extracting global basis functions from fully meshed model runs. Macromodels are generated using technique called proper orthogonal decomposition (POD) which gives good linear fit for the nonlinear systems. Using POD based macromodel, response of system can be computed using fewer modes instead of considering all modes of system. Macromodel is generated to obtain the response of cantilever beam with large deformation and hence, simulation time is reduced by factor of 90 approximately with error of order of 10-4. Further, method of POD based reduced order model is aplied to beam with different loading conditions to check the robustness of the macromodel. POD based macromodel response gives good agreement with FEA model response for a cantilever beam.

Integrated Pest Management in a Predator-Prey System with Allee Effects.

A commonly used biocontrol strategy to control invasive pests with Allee effects consists of the deliberate introduction of natural enemies. To enhance the effectiveness of this strategy, several tactics of control of invasive species (e.g., mass-trapping, manual removal of individuals, and pesticide spraying) are combined so as to impair pest outbreaks. This combination of strategies to control pest species dynamics are usually named integrated pest management (IPM). In this work, we devise a predator-prey dynamical model in order to assess the influence of the intensity of chemical killing on the success of an IPM. The biological and mathematical framework presented in this study can also be analyzed in the light of species conservation and food web dynamics theory.

Dynamic model based on Bayesian method for energy security assessment

The methodology for the dynamic indicator model construction and forecasting of indicators for the assessment of energy security level is presented in this article. An indicator is a special index, which provides numerical values to important factors for the investigated area. In real life, models of different processes take into account various factors that are time-dependent and dependent on each other. Thus, it is advisable to construct a dynamic model in order to describe these dependences. The energy security indicators are used as factors in the dynamic model. Usually, the values of indicators are obtained from statistical data. The developed dynamic model enables to forecast indicators’ variation taking into account changes in system configuration. The energy system development is usually based on a new object construction. Since the parameters of changes of the new system are not exactly known, information about their influences on indicators could not be involved in the model by deterministic methods. Thus, dynamic indicators’ model based on historical data is adjusted by probabilistic model with the influence of new factors on indicators using the Bayesian method.

A second order dynamic power flow model

In this paper a novel second order power flow solution paradigm based on artificial dynamic models is proposed. The idea is to derive the second order optimality conditions for the power flow problem by reformulating the system equations into a set of ordinary differential equations, whose equilibrium points represent the problem solutions. Starting from the Lyapunov Theory we will demonstrate that the structure of this artificial dynamic model is stable with an exponential asymptotic convergence to the equilibrium points. The application of this technique for solving both unconstrained and constrained power flow problems is explained in details, and several numerical results are presented and discussed, demonstrating the effectiveness of the proposed methodology.

Ride Comfort Performance of Active Vehicle Suspension with Seat Actuator Based on Non-Fragile H∞ Controller

The purpose of this paper is to investigate an active seat suspension system with two active actuators using a non-fragile robust control strategy. A simple deterministic vibration model of human body is added to the seat suspension dynamical model in order to make the modeling more accurate. Desired controller is obtained by solving a linear matrix inequality formulation by considering the vertical body acceleration as measurement signal. Finally, the effect of the seat actuator and controller gain variations on the closed-loop system performance is investigated numerically for two deterministic external excitations: bump and a realization of Gaussian white noise. Simulations show that the seat actuator has a noticeable effect on the seat suspension performance especially under random disturbances.

Positive solutions of an abstract fractional semipositone differential system model for bioprocesses of HIV infection

Fractional order derivative is nonlocal which exhibits a long time memory behavior. With advantage of these, fractional order dynamic system models are more accurate than integer order ones in understanding the dynamic behavior of bioprocesses such as HIV infection. In this paper, we systematically study the existence of positive solutions of an abstract fractional semipositone differential system involving integral boundary conditions arising from the study of HIV infection models. By using the fixed point theorem in cone, some new results are established and an example is given to demonstrate the application of our main results.

Parametric reduced order dynamical model construction of a fluid flow control problem

The problem of approximating a given fluid flow control problem, governed by the Navier-Stokes equations at varying Reynolds number, with a low-order parametric linear dynamical model, is presented. To this aim, a three steps approach is proposed: first, (i) the original Reynolds number dependent fluid flow problem is spatially and parametrically discretized, then (ii) each resulting local very large-scale Linear Time Invariant (LTI) Differential Algebraic Equations (DAE) models are approximated using the IRKA approach proposed by Gugercin et al. (2008), and finally (iii), the reduced order models are interpolated and transformed into a low-complexity Linear Fractional Representation (LFR). The overall process is illustrated in a top down framework using a generic flow configuration, namely, an open cavity flow. Numerical simulations assess the validity of the approach.

The Production, Qualitative and Adsorbent Study of β-Cyclodextrin Molecular Engram

In the paper, the molecular engram polymer is produced by dithizone which is functional monomer,β-cyclodextrin which is pore-forming material and chloroepoxy propane which is crosslinking agent. Atomic absorption method is used to study its adsorption for lead. Thermodynamic model of adsorption and dynamic reaction order are discussed. The results show that adsorption is endoergic process and accords with Freundlich isothermal adsorption equation. Adsorption system conforms with pseudo-second order dynamic model, the static saturation adsorption amount is 45.55mg/g.

Design of Fuzzy-Neural-Network-Inherited Backstepping Control for Robot Manipulator Including Actuator Dynamics

This study presents the design and analysis of an intelligent control system that inherits the systematic and recursive design methodology for an n-link robot manipulator, including actuator dynamics, in order to achieve a high-precision position tracking with a firm stability and robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is introduced briefly. Then, a conventional backstepping control (BSC) scheme is developed for the joint position tracking of the robot manipulator. Moreover, a fuzzy-neural-network-inherited BSC (FNNIBSC) scheme is proposed to relax the requirement of detailed system information to improve the robustness of BSC and to deal with the serious chattering that is caused by the discontinuous function. In the FNNIBSC strategy, the FNN framework is designed to mimic the BSC law, and adaptive tuning algorithms for network parameters are derived in the sense of the projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator that are actuated by dc servomotors are provided to justify the claims of the proposed FNNIBSC system, and the superiority of the proposed FNNIBSC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.

Dynamic predictive modeling of extraction process for red ginseng using near-infrared spectroscopy

It is the objective of this study to develop dynamic predictive model for the extraction process of red Ginseng using NIR spectroscopy. NIR spectroscopy was collected online and PLSR models were developed for total quantity of ginsenosides. The performance of NIR prediction model achieved R, RMSEC, RMSEP of 0.996 09, 0.018 9, 0.016 8, respectively. A first order dynamic mass transfer model was combined with NIR prediction of the quality indicator to predict the trajectory of the extraction process based upon the initial 3 or 4 data points. The results showed good agreement with actual measurements indicating reasonable accuracy of the predictive model. It could potentially be used for advanced predictive control of the extraction process.

Visually evoked blood flow responses and interaction with dynamic cerebral autoregulation: Correction for blood pressure variation

Visually evoked flow responses recorded using transcranial Doppler ultrasonography are often quantified using a dynamic model of neurovascular coupling. The evoked flow response is seen as the model's response to a visual step input stimulus. However, the continuously active process of dynamic cerebral autoregulation (dCA) compensating cerebral blood flow for blood pressure fluctuations may induce changes of cerebral blood flow velocity (CBFV) as well. The effect of blood pressure variability on the flow response is evaluated by separately modeling the dCA-induced effects of beat-to-beat measured blood pressure related CBFV changes.Parameters of 71 subjects are estimated using an existing, well-known second order dynamic neurovascular coupling model proposed by Rosengarten et al. [1], and a new model extending the existing model with a CBFV contributing component as the output of a dCA model driven by blood pressure as input.Both models were evaluated for mean and systolic CBFV responses. The model-to-data fit errors of mean and systolic blood pressure for the new model were significantly lower compared to the existing model: mean: 0.8%±0.6 vs. 2.4%±2.8, p<0.001; systolic: 1.5%±1.2 vs. 2.2%±2.6, p<0.001. The confidence bounds of all estimated neurovascular coupling model parameters were significantly (p<0.005) narrowed for the new model.In conclusion, blood pressure correction of visual evoked flow responses by including cerebral autoregulation in model fitting of averaged responses results in significantly lower fit errors and by that in more reliable model parameter estimation. Blood pressure correction is more effective when mean instead of systolic CBFV responses are used. Measurement and quantification of neurovascular coupling should include beat-to-beat blood pressure measurement.

Fractional Order Models of Industrial Pneumatic Controllers

This paper addresses a new approach for modeling of versatile controllers in industrial automation and process control systems such as pneumatic controllers. Some fractional order dynamical models are developed for pressure and pneumatic systems with bellows-nozzle-flapper configuration. In the light of fractional calculus, a fractional order derivative-derivative (FrDD) controller and integral-derivative (FrID) are remodeled. Numerical simulations illustrate the application of the obtained theoretical results in simple examples.

A high-order multidimensional gas-kinetic scheme for hydrodynamic equations

This paper concerns the development of high-order multidimensional gas kinetic schemes for the Navier-Stokes solutions. In the current approach, the state-of-the-art WENO-type initial reconstruction and the gas-kinetic evolution model are used in the construction of the scheme. In order to distinguish the physical and numerical requirements to recover a physical solution in a discretized space, two particle collision times will be used in the current high-order gas-kinetic scheme (GKS). Different from the low order gas dynamic model of the Riemann solution in the Godunov type schemes, the current method is based on a high-order multidimensional gas evolution model, where the space and time variation of a gas distribution function along a cell interface from an initial piecewise discontinuous polynomial is fully used in the flux evaluation. The high-order flux function becomes a unification of the upwind and central difference schemes. The current study demonstrates that both the high-order initial reconstruction and high-order gas evolution model are important in the design of a high-order numerical scheme. Especially, for a compact method, the use of a high-order local evolution solution in both space and time may become even more important, because a short stencil and local low order dynamic evolution model, i.e., the Riemann solution, are contradictory, where valid mechanism for the update of additional degrees of freedom becomes limited.

Modeling of a Building System and its Parameter Identification

This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink® and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics.

This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of robot manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.

Dynamical Balance Optimization and Control of Quadruped Robots Under Perturbing External Force

This paper investigates the optimal feet forces distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamic model of quadruped robots and derive a reduced order dynamical model of motion/force. Consider an external wrench on quadruped robots, the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to minimize the optimized objective function with linear equality and inequality constraints, and hybrid motion/force control based on adaptive neural network is used to compensate for the external perturbation in the environment and approximate feedforward force and impedance of the leg joints on line. The verification of the proposed control is conducted using the extensive simulations.

Suppression of the wing-rock phenomenon using nonlinear controllers

This paper deals with the design of nonlinear controllers for the wing-rock phenomenon of a delta wing aircraft. A fifth order dynamic model is used to describe this phenomenon. A state transformation is introduced such that the transformed dynamic model is in a form which is suitable for a variety of control designs. A feedback linearization control scheme and a sliding-mode control (SMC) scheme are then proposed to suppress wing rock oscillations. It is shown that the two controllers successfully suppress the undesired oscillations and guarantee the asymptotic convergence of all system trajectories to their desired values. The effectiveness of the proposed controllers is verified through simulation studies.

Entropy and Information in a Fractional Order Model of Anomalous Diffusion

Fractional order dynamic models (e.g., systems of ordinary and partial differential equations of non-integer order in time and space) are becoming more popular for characterizing the behavior of complex systems. Justification for such models is typically based on improved fits to experimental data or a reduced mean squared error for models with the same number of fitting parameters. This rationale, however, is relative to the form of the selected fitting function, and is dependent on the order of the derivatives. Nevertheless, there seems to be a recognition that fractional order models work better than integer order models in describing the electrical and mechanical properties of multi-scale, heterogeneous materials. In order to address this issue and to offer a new approach for establishing the utility of fractional order models, we calculate the total Shannon spectral entropy for the case of anomalous diffusion governed by a fractional order diffusion equation generalized in space and in time. This fractional order representation of the continuous time, random walk model of diffusion gives a spectral entropy minimum for normal (i.e., Gaussian) diffusion with surrounding values leading to greater values of spectral entropy.