Problem Of Parameter Variations Research Articles

Research of Compound Control for DC Motor System Based on Global Sliding Mode Disturbance Observer

Aiming at the problems of modeling errors, parameter variations, and load moment disturbances in DC motor control system, one global sliding mode disturbance observer (GSMDO) is proposed based on the global sliding mode (GSM) control theory. The output of GSMDO is used as the disturbance compensation in control system, which can improve the robust performance of DC motor control system. Based on the designed GSMDO in inner loop, one compound controller, composed of a feedback controller and a feedforward controller, is proposed in order to realize the position tracking of DC motor system. The gains of feedback controller are obtained by means of linear quadratic regulator (LQR) optimal control theory. Simulation results present that the proposed control scheme possesses better tracking properties and stronger robustness against modeling errors, parameter variations, and friction moment disturbances. Moreover, its structure is simple; therefore it is easy to be implemented in engineering.

Effects of dynamic and non‐dynamic element changes in RC and RL networks

AbstractThe paper deals with the redesign of passive electric networks by changes of single dynamic and non‐dynamic elements which may retain or affect the natural topology of the network. It also deals with the effect of such changes on the natural dynamics of the network, the natural frequencies. The impedance and admittance modeling for passive electrical networks is used which provides a structured, symmetric, integral‐differential description, which in the special cases of RC and RL networks is reduced to matrix pencil descriptions. The transformations on the network are expressed as those preserving, or modifying the two natural topologies of the network, the impedance graph and the admittance graph topologies. For the special cases of RC and RL networks, we consider the problem of the effect of changes of a single dynamic or non‐dynamic element on the natural frequencies. Using the Determinantal Assignment Framework, it is shown that the family of single parameter variation problems is reduced to equivalent Root Locus problems with the possibility of fixed modes. An explicit characterization of the fixed modes is given, and a number of interesting properties of the spectrum are derived such as the interlacing property of poles and zeros for the entire family of Root Locus problems. Copyright © 2013 John Wiley & Sons, Ltd.

Electronic Load Controller for Self Exited Induction Generator Using Fuzzy Logic Controller

This paper deals with the electronic load controller for self exited induction generator using fuzzy logic controller. The self-excited induction generators (SEIGs) are considered to be well suited for generating electricity by means of conventional energy sources and for supplying electrical energy in remote and rural areas. Induction generators have many advantages such as cost, reduced maintenance, rugged, and simple construction, brushless rotor (squirrel cage). A three phase induction generator can be operated on a delta connection for supplying single phase loads. The main disadvantage of SEIG has is that it poor voltage regulation, and its value depends on the prime mover speed, capacitance, load current and power factor of the load. The electronic load controller (ELC) can be used for maintaining constant voltage and frequency of SEIG with variable consumer load driven by constant prime mover. This paper presents the simulation design and implementation of ELC using fuzzy logic method for an SEIG feeding single-phase load. The ELC consist of a rectifier, IGBT as a chopper switch, PI controller, voltage sensor, and resistive dump load in which power consumption was varied through the duty cycle of the chopper. However an ELC consist of electronics system, in general, has complex nonlinear model with parameter variation problem, and the control need to be very fast. The fuzzy logic based controller gives nonlinear control with fast response and virtually no overshoot. The simulation of ELC for self exited induction generator is carried out on MATLAB/SIMULINK. By this proposed ELC using FLC for SEIG we can maintain the constant voltage and frequency of SEIG with variable consumer load.

A Magnetization Curve Based Stator Resistance and Fuzzy Rotor Resistance Adaptation Technique Suitable for Speed Sensorless Field-oriented Control of Induction Machine

Speed sensorless field-oriented control schemes for induction machines are widely used in modern industries. The parameter variation problem at different operating conditions is the major difficulty for such a scheme. In this article, a model-based technique is developed utilizing the magnetization curve of the machine to estimate simultaneously the stator resistance and magnetizing inductance. In the proposed technique, the stator current axis is taken as the orientation of the d-axis. The estimated stator resistance is independent of rotor speed and, hence, is applicable for speed sensorless schemes. A correct estimation of speed also requires accurate knowledge of rotor resistance for such drives. In the proposed scheme, the rotor resistance variation is also tracked through a fuzzy logic based estimator depending on the stator resistance variation and other input variables. Simulations based on this technique are shown, which are also verified by suitable experimental results.

Supervised Online Adaptive Control of Inverted Pendulum System Using ADALINE Artificial Neural Network with Varying System Parameters and External Disturbance

Generalized Adaptive Linear Element (GADALINE) Artificial Neural Network (ANN) as an Artificial Intelligence (AI) technique is used in this paper to online adaptive control of a Non-linear Inverted Pendulum (IP) system. The ANN controller is designed with specifications as: network type is three (Input, Hidden and Output) layered Feed-Forward Network (FFN), training is done by Widrow-Hoffs delta rule or Least Mean Square algorithm (LMS), that updates weight and bias states to minimize the error function. The research is focused on how to adapt the control actions to solve the problem of parameter variations. The method is applied to the Nonlinear IP model with the application of some uncertainties, and the experimental results show that the system responds very well to handle those uncertainties.

Adaptive Low-speed Control of Permanent Magnet Synchronous Motors

Most servo control systems generally adopt incremental optical encoders for speed detection when considering cost and performance requirements. For a fixed sampling period, this kind of encoder along with the generally used so-called M method, may degrade the response or even cause the system to become unstable in a low-speed operating region because of the resulting speed detection delay. In this article, a reference model improves low-speed responses; parameter identification by recursive least square error algorithm overcomes the problem of parameter variations and an adaptive proportional-integral control strategy based on the parameter identification results further justifies the proposed method. A digital signal processor based permanent magnet synchronous motor drive will be used to carry out the experimental results, which show the effectiveness of the proposed method.

Applying a Novel Fuzzy-PI Controller on the Model of Continuous Stirred Tank Reactor

Continuous Stirred Tank Reactor (CSTR) presents a significant role in the most of chemical process industries. The inherent nonlinear dynamic of CSTR results in various difficulties of stabilizing and controlling of the process. Likewise, functional changes with elapsing of the time and different disturbances associated with real environment are the other hints which must put into perspective. In this paper, a novel strategy based on the combination of fuzzy gain scheduling and a socio-political optimization algorithm, imperialist competitive algorithm, has been employed to design an intelligent Fuzzy-PI controller so that it surmounts the problem of system's parameter variation throughout the controlling of CSTR temperature. The controller has two inputs which are error and derivative of error and two outputs that are the proportional gain and integral gain. The controller is updated continuously to track the changes which are occurred in the system. To obtain the high performance control, imperialist competitive algorithm was employed to determine the optimum membership functions of the input and output variables. The performance of the proposed controller has been consider based on the Sum of the Square Error (SSE) and Integral Absolute Error(IAE) Criteria. In addition, conventional PI controller and classic online PI controller were applied to the system for the performance comparison. The results clearly indicate the robustness of Fuzzy-PI controller in dealing with uncertainties throughout the tracking of the reference signal. Fuzzy-PI controller also embodies an outstanding accuracy of tracking which leads to the least tracking error criterion. Algorithm (ICA), Gain Scheduling.

THE CARTAN FORM AND ITS GENERALIZATIONS IN THE CALCULUS OF VARIATIONS

In this paper, we discuss possible extensions of the concept of the Cartan form of classical mechanics to higher-order mechanics on manifolds, higher-order field theory on jet bundles and to parametric variational problems on slit tangent bundles and on bundles of nondegenerate velocities. We present a generalization of the Cartan form, known as a Lepage form, and basic properties of the Lepage forms. Both earlier and recent examples of differential forms generalizing the Cartan form are reviewed.

Precision Control and Compensation of Servomotors and Machine Tools via the Disturbance Observer

The computerized numerical control machine tool is a highly integrated mechatronic system in manufacturing processes. However, uncertainties degrade its motion accuracy. These include modeling errors, parameter variation, friction, and measurement errors that are present in either linear or nonlinear nature. In this paper, a state-space disturbance observer was successfully applied to servomotors to estimate and compensate for the uncertainties of parameter variation and current measurement problems, in the velocity and current loops, respectively. Furthermore, an autotuning procedure was developed accordingly to identify the varied parameters of the motor. Furthermore, by implementing the present servomotor systems in high-precision machine tools, the nonlinear friction compensation was adopted to reduce the slip-stick effect in contouring motion. Experimental results indicate that the roundness error has been significantly reduced from 13.3 to 2.0 ?m by applying the proposed approaches.

Some concepts of regularity for parametric multiple-integral problems in the calculus of variations

We show that asserting the regularity (in the sense of Rund) of a first-order parametric multiple-integral variational problem is equivalent to asserting that the differential of the projection of its Hilbert-Caratheodory form is multisymplectic, and is also equivalent to asserting that Dedecker extremals of the latter (m + 1)-form are holonomic.

Upper semicontinuity in a parametric general variational problem and application

In this paper, we give sufficient conditions for the upper semicontinuity property of the solution mapping of a general model which includes many generalized vector quasi-equilibrium problems with set-valued maps as special cases. The main result generalizes and improves several recent results. An example is given to illustrate such generalization and improvement. The main result is also applied to a model which can be interpreted as a system of generalized vector quasi-equilibrium problems with moving cones. The main tools of the paper are some new notions of cone-semicontinuity properties and openness/closedness properties of families of set-valued maps.

Stability of Solutions in Parametric Variational Relation Problems

The purpose of this paper is to investigate topological properties and stability of solution sets in parametric variational relation problems. The results of the paper give a unifying way to treat these questions in the theory of variational inequalities, variational inclusions and equilibrium problems.

Optimal Iterative Learning Control for Batch Processes with Model Parameter Variations Using Strong Tracking Filter

An optimal iterative learning control (ILC) strategy is proposed to track product quality trajectories of batch processes by updating a linear time-varying perturbation (LTVP) model. To address the problem of model parameter variations from batch to batch, the LTVP model is renewed by using strong tracking filter (STF) algorithm. Comparing recursive least squares (RLS), STF can capture the changing dynamics of the process more accurately. The tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. Sufficient conditions of convergence are derived for the optimal ILC based on the LTVP model. It has also been proved that the tracking error will converge to a small constant but depend on the accuracy of the LTVP model error. If there is no model error, the tracking error can converge to zero. By using STF to update the LTVP model, the model accuracy is improved and the tracking control performance is also enhanced. The proposed strategy is illustrated on a typical batch reactor, and the results demonstrate that the performance of tracking product qualities can be improved under the proposed strategy when model parameter variations occur with respect to the batch index.

PARAMETRIC GENERALIZED MIXED IMPLICIT QUASI-VARIATIONAL INCLUSIONS

An existence theorem for a new class of parametric generalized mixed implicit quasi-variational inclusion problems is established in Hilbert spaces. Further, we study the behavior and sensitivity analysis of the solution set in this class of parametric variational inclusion problems.

Development and Validation of a Fluorescence Polarization-Based Competitive Peptide-Binding Assay for HLA-A*0201A New Tool for Epitope Discovery

Various approaches are currently proposed to successfully develop therapies for the prevention and treatment of infectious diseases and cancer. One of the most promising approaches is the development of vaccines that elicit cytotoxic T lymphocyte (CTL) responses. Consequently, identification and exact definition of molecular parameters involved in peptide-MHC class-I interactions of putative CTL epitopes are of prime importance for the development of immunomodulating compounds. To better facilitate epitope discovery, we developed and validated a novel state-of-the-art biochemical HLA-A0201 assay, which is comprised of technologically advanced cutting edge reagents. The technique is based on competition and uses a FITC-labeled reference peptide and highly purified soluble HLA-A0201 molecules to quantitatively measure the binding capacity of nonlabeled peptide candidates. Detection by fluorescence polarization allows real-time measurement of binding ratios without separation steps. During standardization, the problem of assay parameter variation is discussed, showing the dramatic influence of HLA and reference peptide concentrations as well as the choice of the reference peptide itself on IC(50) determinations. For validation, a panel of 15 well-defined HLA-A0201 ligands from various sources covering a broad range of binding affinities was tested. Binding data were used to compare against pre-existing quantitative assay systems. The results obtained demonstrated significant correlation among assay procedures, suggesting that the application of fluorescence polarization in combination with recombinant sHLA molecules is highly advantageous for the accurate assessment of peptide binding. Furthermore, the assay also features high-throughput screening capacity, providing uniquely efficient means of identifying and evaluating immune target molecules.

Incremental Training of Support Vector Machines

We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.

A new perturbed algorithm for generalized quasi-variational inclusions

In this paper, a perturbed iterative method for solving a general class of quasi-variational inclusions was studied. The existence and convergence of solution were proved. Furthermore, a new iterative method for solving a general class of quasi-variational inclusions was proposed. The continuity of the perturbed solution was also proved for a parametric variational inclusion problem. Several special cases were discussed.

Isoperimetric inequalities for parametric variational problems

We prove isoperimetric inequalities for general parametric variational double integrals F, whose Lagrangians F depend on the position vector X and on the surface normal N. As an essential tool we introduce Sauvigny's F-conformal parameters adapted to the parametric integrand and use the notion of generalized mean and Gaussian curvature. The special cases of minimal surfaces, surfaces of bounded mean curvature and ▪-minimizing surfaces are also discussed.

Unilateral Crack Identification: A Filter-Driven, Iterative, Boundary Element Approach

An identification problem for parametric variational inequalities and linear com-plementarity problems is solved here by means of iterative filter techniques. A concrete application in engineering mechanics, the unilateral crack identification problem, is solved. The elastic contact problem is formulated by boundary element-linear complementarity techniques. By means of numerical results and comparison with previous approaches based on optimization and neural networks it is shown that this method is advantageous. In view of the difficulty of the considered bilevel optimization problem, this approach may be of interest for other applications as well.

On two-dimensional parametric variational problems

Let \(\mathcal{F}(X)\) be a two-dimensional parametric variational integral the Lagrangian F(x,z) of which is positive definite and elliptic, and suppose that \(\Gamma\) is a closed rectifiable Jordan curve in \(\mathbb{R}^{3}\). We then prove that there is a conformally parametrized minimizer of \(\mathcal{F}\) in the class \(\mathcal{C}(\Gamma)\) of surfaces \(X \in H^{1,2}(B, \mathbb{R}^{3}) \cap C^{0}(\overline{B}, \mathbb{R}^{3})\) of the type of the disk B which are bounded by \(\Gamma\). An immediate consequence of this theorem is that the Dirichlet integral and the area functional have the same infima, a result whose proof usually requires a Lichtenstein-type mapping theorem or else Morrey's lemma on \(\epsilon\)-conformal mappings. In addition we show that the minimizer of \(\mathcal{F}\) is Holder continuous in B, and even in \(\overline{B}\) if \(\Gamma\) satisfies a chord-arc condition. In Section 1 it is described how our results are related to classical investigations, in particular to the work of Morrey. Without difficulty our approach can be carried over to two-dimensional surfaces of codimension greater than one.