Diagnosis of Air-Gap Eccentricity Fault for Inverter Driven Induction Motor Drives in the Transient Condition
Modelling and Simulation Study of a Helicopter with an External Slung Load System
Comparative Study of Single Phase Power Inverters Based on Efficiency and Harmonic Analysis
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
The intent of the paper is to control the speed and give protection to Induction Motor (IM). Thereby it limits the starting current and protects the induction motor under load/ overload conditions. The control panel is designed to control the speed and protection of 3 phase IM and required control is done by PLC (Programmable Logic Controller). The motor is run at no load condition and load condition, for manual mode and PLC mode. At manual mode, if frequency is changed, the motor speed changes by keeping v/f ratio constant but, regulation in speed is high. At PLC mode, if frequency is changed, the motor speed remains constant, by keeping v/f ratio constant and speed regulation zero. The speed control of the induction motor in manual and PLC mode is accessed by using the selector switch . SCADA (Supervisory Control and Data Aquisition) is used for monitoring and controlling of induction motor performance and it also collects performance data of the induction motor. This is how the starting, control the speed and protection of IM is achieved by using PLC and SCADA, and the operation is sufficiently high efficient and reliable , .
In this paper, the authors have presented a mixed method for Model Order Reduction (MOR) of a continuous approach for Single Input Single Output (SISO) system. The numerator of higher order transfer function of the model reduces by Chebyshev polynomial technique and the denominator reduces by two different methods. The Fuzzy C-Means Clustering method is used for reducing the denominator and the Stability equation technique is also used for reducing the denominator. The results are then compared for both the techniques.
Model Order Reduction (MOR) is one of the important methods to reduce the order of large scale dynamical system which come in account from previous few decades. Here, the authors inspect one of the simple and efficient methods of MOR which is Balanced Realization, and it is further divided in to two methods of MOR, among them first one is the Balanced Truncation method and second one is the Balanced Singular Perturbation Approximation method. In this paper, the authors consider three examples of real time dynamical system namely, Building Model, Partial Differential Equation Model and CD Player. Both the methods have been applied on these models and an exhaustive comparison has been made. The authors consider both the Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) system and have applied in the above examples.
This paper gives the position and tracking control of a nonlinear system. Inverted pendulum is an under actuated, unstable and non-linear system. Therefore, control design of such a system is a challenging task. There are many methods to design a controller, but Takagi-Sugeno (T-S) fuzzy controller is one of the traditional control technique which is applied to a non-linear system to check the performance and stability. In this paper, the non-linear system is represented as a linear system by using sector nonlinearity method. Here, the control objective is to control the system such that, the inverted pendulum is stabilized in the upright position. Simulation is performed on the inverted pendulum system, and the effectiveness of the proposed site is demonstrated and the robustness is also verified. In this paper, the algorithm has been applied to the Inverted pendulum system with eight nonlinearities. The simulations are performed on inverted pendulum system and the effectiveness of the proposed method is demonstrated and also the robustness of the system is verified.
Model Order Reduction (MOR) plays an important role in determining a Reduced Order Model (ROM) from a large scale system, while preserving their input-output behaviour. A Reduced Order Model is a lower dimensional computational model which can faithfully reproduce the essential feature of a higher dimensional model. This paper presents, the overview of Model Order Reduction with emphasis on Krylov-Subspace based technique and its algorithm. Krylovsubspace methods are well known and used in different applications of MOR. Krylov-subspaces replaces the large and expensive model by a smaller model, with excellent approximating properties and at the same time by means of efficient computational approach. The paper overviewed on the algorithms of Krylov-subspace technique that is Arnoldi algorithm and Two-sided Arnoldi algorithm which is used for obtaining the reduced-order models of high-order linear time invariant systems with an appropriate implicitly matching of Time Moments and Markov Parameters. Further, three numerical examples have been carried out to obtain their reduced order models with the preservation of stability.