A Control Simulation Tool for Online Demand Calibration

Using specially designed broadband periodic random excitation signals, the open loop gain, and the common mode and power supply rejection ratios of operational amplifiers are measured and modeled. The proposed modeling technique 1) takes into account the measurement uncertainty and the nonlinear distortions, 2) gives information about possible unmodeled dynamics, 3) detects, quantifies, and classifies the nonlinear distortions, and 4) provides opamp parameters (time constants, gain-bandwidth product, etc.) with confidence bounds. The approach is suitable for the experimental characterization of operational amplifiers (see Part II) as well as the fast evaluation of new operational amplifiers designs using network simulators (see [12]). Part II handles the calibration of the experimental setup and illustrates the theory on real measurements.

An extensive analysis of the effects of High Voltage Direct Current (HVDC) systems on the small-disturbance stability of power systems is presented within this chapter. Initial comparisons are made between different HVDC technologies, assessing the impact of their placement and power transfer on the electromechanical modes within a small test network. Following this, Power Oscillation Damping (POD) controllers are designed and their effects are investigated for a variety of system configurations. This comprehensive analysis of the supplementary POD control includes multi-terminal HVDC studies, demonstrating the effectiveness of HVDC grids for oscillation damping control. For a wide range of operating configurations, the use of wide area signals and a multivariable control structure is shown to outperform traditional POD controller designs.KeywordsActive PowerPower FlowHigh Voltage Direct CurrentModulation CapacityConverter StationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper studies the proportional-derivative (PD) control method in the control field, which can be applied in industrial automation, robot control, aerospace, process control, and other fields. This paper discusses the PD control to adjust the motor angle and the fuzzy proportional-integral (PI)-PD control of the brushless direct current (DC) motor. How to apply PD control theory to practice is a part worth exploring. In the traditional control field, proportional-integral-derivative (PID) control parameters cannot adjust the changes of controlled parameters, and the traditional PID control must cooperate with three parameters at the same time to work. Therefore, in this paper, PD control is applied to the adjustment of motor angle, and PI-PD fuzzy control is used to adjust the controller parameters in real time. This method can improve the response speed of the controller, apply the control theory to the actual motor control, and improve the anti-interference ability. This research can improve the application scope of PD control and combine it with a variety of control theories, to achieve a better effect of the control system, optimize the response speed of the motor, and achieve the target speed faster.

This paper extends previous research to analytically identify leaks within a water distribution network (WDN), by combining hydraulic simulation and network science based data analysis techniques. The WDN model is used to run several ‘leakage scenarios’, by varying leak location (pipe) and severity, and to build a dataset with corresponding variations in pressure and flow, induced by the leak. All junctions and pipes are considered for potential pressure and flow sensors deployment; a clustering procedure on these locations identifies the most relevant nodes and pipes, and cost-effectiveness was considered. A graph is then generated from the dataset, having scenarios as nodes and edges weighted by the similarity between each pair of nodes (scenarios), in terms of pressure and flow variation due to the leak. Spectral clustering groups together similar scenarios in the eigen-space spanned by the most relevant eigen-vectors of the Laplacian matrix of the graph. This method uses superior traditional techniques. Finally, support vector machines classification learning is used to learn the relation between variations in pressure and flow at the deployed meters and the most probable set of pipes affected by the leak.

Turbulent combustion of a lean premixed methane-air mixture is simulated numerically using unsteady CFD. The configuration is a matrix burner suitable for stationary gas turbine applications. The geometry consists of the following: seven slots which constitute the flame holder for stabilizing the flame, a diffuser which serves the purpose of lowering the pressure loss across the burner and a combustion chamber. A contraction ensures that a recirculation zone is created close to the exit of the flame holder for anchoring and stabilizing the flame. Fuel is injected in 112 holes, 8 along each end of the 7 slots. The injected fuel meets the in-coming high velocity air stream for mixing to begin in the premixed ducts before finally entering the combustion chamber. This paper validates the cold flow velocity field and the steady flame results from CFD with measurements and investigates combustion instability in a matrix burner, the onset of which can be attributed to changes in flow variables using URANS. Particularly, the effect of the mixture strength variation caused by fluctuations in the velocity field on the unsteady heat rate inside the combustor is investigated. The fuel inlet is assumed to be choked due to the high pressure drop across it. The time lag between the time fuel is injected and the time it reaches the flame front is estimated. Quantifying this time delay (or “flight time”) helps to characterize the burner with respect to thermo-acoustic instabilities. The flame frequency response to a white noise forcing at the air inlet is determined. This is followed by the determination of the acoustic transfer matrix linking the pressure and velocity downstream and upstream of the burner/flame. This is done by using system identification that is common in control theory. The determined flame frequency response and the time lag are used in a 1D acoustic network code for determining the longitudinal eigenmodes of the combustor of the matrix burner.

This paper focuses on the mathematical and computational aspects of the modeling and control of thermal manufacturing processes, with a dynamic and distributed-parameter nature. A generic numerical simulation model is first developed for thermal processing, with an embedded melt flow model to the conduction field description. This is linearized by small perturbations near the nominal process conditions, and yields a computationally efficient state space model with a sparse, diagonally banded structure in the system matrices. A multivariable controller with similar structure is then designed, and its independent gains are selected by optimization of the closed-loop thermal eigenvalues. This procedure can be implemented in real time by identification of the model and by adaptation of the controller parameters. This methodology is validated by computer simulation, emulating scan welding and thermal rapid prototyping applications.

This chapter illustrates complications which arise in control theory when control parameters have to satisfy various constraints. Controllable systems with the set of controls being either a bounded neighbourhood of 0 ∈ ℝ m or the positive cone ℝ + m are characterized.

Dielectric elastomer actuators also known as DEAs are widely used as soft actuators. In order to control such actuators its mechanical properties are needed. Since viscoelastic materials are mainly used as a base for DEAs, an appropriate mathematical model is needed to get appropriate control parameters. Viscoelastic elastomers have very complex mechanical characteristics. They exhibit a combination of behaviors of solid material and liquid. Usually, solid materials exhibit elasticity up to a certain point. Liquids exhibits viscosity. They change their shape regardless on the stress applied on it and do not regain their original shape. Viscoelastic materials exhibit both behaviors at once. However, the ratio between elasticity and viscosity depends on the material being used. The use of fractional Kelvin–Voigt model can be of great help in determining the elastic and/or viscous material properties. Even more, a comparison between single and double fractional Kelvin–Voigt models will be given for low frequency strain ranges. It will be shown that viscoelastic mechanical properties are frequency dependent. At the end a least-square method is introduced for determination of optimal parameters for both models. This study can be used as a guidance for viscoelastic material parameter identification either for single or double fractional Kelvin–Voigt model. Both models can be further used in control theory with fractional derivatives to obtain proper control parameters since they are easily transferable to Laplace domain.

Production from reservoirs consisting of an oil layer sandwiched between a gas cap and bottom water has always been a challenging task for petroleum engineers because of the coning problems. The present paper addresses the problem of determining optima! location of horizontal welis used to produce from such reservoirs. An optimization procedure based on optima! control theory is outlined, and implemented to determine optimal location for horizontal producdon weils. The optimization technique is based on . 2D three-phase cross-sectional model • adjoint equations • linear programming � and is general enough so that the optimization can he carried out for a variety of reservoir and control parameters. It is demonstrated that the optima! well location strongly depends on the development term, and on the heterogeneity of the reservoir. We present several numerical examples with different development terms and different types of heterogeneities where placing of a producing horizontal well in oil. water, or gas zone is optimal from the point of view of fina! oi! recovery.

In this paper, a dynamic continuous time model of the firm, encompassing operations and investments, is formulated as an optimal control problem in an activity analysis context. In the model, the objective of the firm is to maximize, subject to various constraints, the discounted value of operating profits less the costs of new capacity and the interest on borrowed funds (or plus the interest on savings) over a fixed decision-making interval plus the discounted value of capacity at the end of the period. The state variables are capacity and debt, and the controls are scale of operation and rate of purchase of new capacity; the final capacity and debt are control parameters. There are several inequality constraints. Using results in control theory, the optimal controls are determined for the case the rate of production is linear in the scale variable, the growth of capacity is linear in the rate of purchase of new capacity and the attrition of capacity is linear in the amount of capacity. Extensions of the model are made to include multiple processes.

Self-excited vibration often emerges in closed-loop systems due to parameter points out of the stable region in the parameter space. Consequently, based on control theory, various analysis methods have been developed to determine the parameter condition of equilibrium stability and to calculate the amplitude and the frequency of self-excited vibration in high-order closedloop systems, of which frequency method is a very useful tool. Particularly, for the transfer function is invariant in nonsingular linear coordinate transformation for a given dynamic system, it may be obtained in a simple manner either from system equations or experimentally. Usually, the block diagram is a convenient model for the frequency method. Thus, the contents arranged in this chapter are divided into five sections: the first is devoted to establishing mathematical models in frequency domain for closed-loop systems; the second provides a frequency criterion of equilibrium stability for linear closed-loop systems; the third recommends a frequency criterion of equilibrium stability for nonlinear closed-loop systems; the fourth introduces a well-known describing function method used to calculate the amplitude and the frequency of self-excited vibration in closed-loop systems; and the fifth gives a quadratic form optimal algorithm to determine the control parameters, which can prevent the controlled system from self-excited vibration.

Fractional integro-differentiation (or, non-integer order integro-differentiation) is a mathematical framework that leads to efficient tools for modeling and control many of physical systems. Nevertheless, the overall area is commonly refereed as Fractional Calculus (FC) and become popular during the last years. In fact, FC can be used to describe in a solid and compact form systems characterized by long-range temporal or spatial dependence phenomena. Furthermore, the extension of classical and modern control theories to the new perspective, allows the development of algorithms applicable both integer and non-integer order systems. The application of fractional calculus can have a considerable impact on everyday life namely in technology, social and health issues. Therefore, important challenges are still posed to the scientific community that motivate researchers to explore new features of fractional systems. The special issue Challenges in Fractional Dynamics and Control Theory includes a selection of nine papers addressing topics that recently emerged in this area and is organized as follows. Lopes and Machado (2015) contribute with the manuscript entitled State space analysis of forest fires. Burnt area, precipitation and atmospheric temperatures are interpreted as state variables of a complex system and the correlations between them are investigated. The study sheds light about a complex phenomenon that needs to be better understood in order to mitigate its devastating consequences, at both economical and environmental levels. Balachandran et al. (2015) present the paper Controllability of nonlinear implicit neutral fractional Volterra integrodifferential systems. The control problem of non-linear neutral fractional Volterra integrodifferential systems with implicit fractional derivative is established. Sufficient conditions for controllability are obtained by means of the notions of condensing map and measure of noncompactness of a set. Meerschaert, et al. (2015) have the study Anisotropic fractional diffusion tensor imaging. Traditional diffusion tensor imaging (DTI) maps brain structure by fitting a diffusion model to the magnitude of the electrical signal acquired in magnetic resonance imaging (MRI). Fractional DTI employs anomalous diffusion models to obtain a better fit to real MRI data, which can exhibit anomalous diffusion in both time and space. The paper describes the challenge of developing and employing anisotropic fractional diffusion models for DTI. Pinto and Carvalho (2015) develop the work Fractional complex-order model for HIV infection with drug resistance during therapy. They propose a fractional complex-order model for drug resistance in HIV infection. The fractional complex-order system reveals rich dynamics and variation of the value of the complex-order derivative sheds new light on the modeling of the intracellular delay. Muresan et al. (2015) contribute with Design and analysis of a multivariable fractional order controller for a non-minimum phase system. Two control strategies for multivariable processes are proposed, based on a decentralised and a steady state decoupling approach. The designed controllers are fractional order PIs. The efficiency and robustness of the fractional algorithms is tested and validated using a non-minimum phase process. Ventura et al. (2015) add the work Fractional direct and inverse models of the dynamics of a human arm. The paper presents a comparative study of both direct and inverse models of the human arm at the elbow joint.

Using Embedded Discrete Fracture Model (EDFM) in numerical simulation of complex hydraulic fracture networks calibrated by microseismic monitoring data

Rotational Motion Control by Feedback with Minimum L1-Norm

Finite time identification in unstable linear systems