i-manager Publications

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 12 Issue 1

Most Cited

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
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models

Volume 4 Issue 2 February - April 2016

Research Paper

Automation in Speed Control and Overcurrent Protection of Three Phase Induction Motor

M. M. Kharade* , N.R Kulkarni**

* PG Scholar, Department of Electrical Engineering, Modern College of Engineering, Pune, India.

** Professor and HOD, Department of Electrical Engineering, Modern College of Engineering, Pune, India.

Kharade, M.M., and Kulkarni, N.R. (2016). Automation in Speed Control and Overcurrent Protection of Three Phase Induction Motor. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 1-6. https://doi.org/10.26634/jic.4.2.4876

Abstract

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 [1]. 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 [1], [2].

References

[1]. K. Sandeep Kumar, and K. Pritam Satsangi, (2007). “Micro –Controller Based Closed Loop Control of Induction Motor Using V/F Method”. IET International Conference on Information and Communication Technology in Electrical Science, pp.340-342 .

[2]. Mineo Tsuji, Shuo Chen, and Shin-ichi Hamasaki, (2008). “A Noval V/F Control of Induction Motors for Wide and Precise Speed Operation”. IEEE International Symposium Power Electronics, Electrical Drives, Automation and Motion, pp.1130-1135.

[3]. Maria G. Ioannides, (2004). “Design and Implementation of PLC-Based Monitoring Control System for Induction Motor”. IEEE Transactions on Energy Conservation, Vol. 19, No. 3.

[4]. N. D. Ramesh, (2011). “Programmable Logic Controllers and SCADA. Seminar Projects.

[5]. S. Da'na, (2008). “Development of a Monitoring and Control Platform for PLC based Applications”. Journal Computer Standards & Interfaces, Vol. 30, No. 3, pp. 157- 166.

[6]. Jignesha Ahir, (2011). “Design and Development of PLC and SCADA Based Control Panel for Continuous Monitoring of 3 Phase Induction Motor”. National Conference on Recent Trends in Engineering & Technology..

[7]. Mahir Zajmovic, Hadzib Salkic, and Sasa Stanic, (2012). “Management of Induction (asynchronous) Motor using PLC”. Journal of Information Technology and Applications, Vol.2, pp. 95-102.

[8]. Ravi Masand, Deepika Jadwani, and S.P. Shukla, (2013). “Fault Diagnosis of Induction Motor using PLC & SCADA”. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.2, No.12.

[9]. Rinchen Geongmit Dorjee, (2014). “Monitoring and Control of a Variable Frequency Drives using PLC and SCADA”. International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 2, No.10.

[10]. D. Amudhavalli, and L. Narendra, (2012). “Speed control of an Induction Motor by V/F Method using an Improved Z Source Inverter”. IEEE International Conference on Emerging Trends in Electrical Engineering and Energy Management, pp.436-440.

[11]. Cristina Anita Bejan, Mihai Iacob and Gheorghe- Daniel Andreescu, (2009). "SCADA Automation System th Laborator y, Elements and Applications". IEEE 7 International Symposium on Intelligent Systems and Informatics, pp.181-186.

[12]. Mini S. Thomas, Pramod Kumar, and V.K. Chandna, (2004). "design, Development, and Commissioning of a Supervisory Control and Data Acquisition (SCADA) Laboratory for Research and Training". IEEE Transactions on Power System, Vol.19, No. 3, pp.1582-1588.

[13]. Al-Ali, A.R., (2000). “A PLC Based Power Factor Controller for a 3-phase Induction Motor”. IEEE International conference on Industry Applications, Vol.2, pp.1065-1072.

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Research Paper

Model Order Reduction using Chebyshev Polynomial, Stability Equation Method and Fuzzy C-means Clustering

Maneesh Kumar Gupta* , Awadhesh Kumar**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, Uttar Pradesh, India.

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, Uttar Pradesh, India.

Gupta, M.K., and Kumar, A. (2016). Model Order Reduction using Chebyshev Polynomial, Stability Equation Method and Fuzzy C-means Clustering. i-manager’s Journal on Instrumentation and Control Engineering, 4(21), 7-13. https://doi.org/10.26634/jic.4.2.4877

Abstract

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.

References

[1]. Dharmendra Singh, and Om Prakash Gujela, (2015). ”Performance Analysis of Time Moments, Markov's Parameters and Eigen Spectrum Using Matching Moments”. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, No.3.

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[3]. K. Sinha, and J. Pal, (1990). “Simulation Based Reduced Order Modeling using a Clustering Techniques”. Computer and Electrical Engineering, Vol.16, No.3, pp.159-169.

[4]. R. Parthasarathy and K. N. Jayasimhaj, (1982). “System Reduction using Stability-Equation Method and Modified Cauer Continued Fraction”. Proceedings of the IEEE, Vol. 70, No. 10, pp.1234-1236.

[5]. Y. Bistritz and G. Langholz, (1979). “Model Reduction by Chebyshev Polynomial Techniques”. IEEE Transactions on Automatic Control, Vol.24, No. 5.

[6]. N. Sai Dinesh, M. Siva Kumar, and Srinivasa Rao, (2013). “Order Reduction of Discrete Time System using Modified Pole Clustering Technique”. International Journal of Engineering Research and Applications, Vol. 3, pp.565- 569.

[7]. V. Singh, D. Chandra and H. Kar, (2004). “Improved Routh Pade Approximants: A Computer Aided Approach”. IEEE Transactions on Automatic Control, Vol. 49, No.2, pp.292-296.

[8]. R. Prasad and J. Pal, (1991). “Stable Reduction of Linear System by Continued Fractions”. Journal on Industrical Engineering, Vol. 72, 113-116.

[9]. Dunn J.C, (1974). “A Fuzzy Relative of the Isodata Procers and its use in Detecting Well Seprated Clusters”. Journal of Cybernetics, Vol.3, No.3, pp.32-57.

[10]. N. Gupta and A. Narain, (2015). “Reduction of Discrete Interval Systems through Fuzzy-C means clustering th with Dominant Pole Retention”. IEEE 5 Australian Control Conference, Vol. 72, pp. 348-35.

[11]. L.S. Shieh and Y. J. Wei, (1975). “A mixed method for multivariable system reduction”. IEEE Transactions on Automatic Control, Vol. 20, pp. 429-432.

[12]. G. Parmar, R. Prasad and S. Mukherjee, (2007). “Order Reduction of Linear Dynamic Systems using Stability Equation Methods and GA”. World Academy of Science, Engineering and Technology.

[13]. V. P. Singh, Prateek Chaubey and D. Chandra, (2012). “Model Order Reduction of Continuous Time Systems using Pole Clustering and Chebyshev Polynomial”. IEEE Students Conference on Engineering and Systems, pp.1-4.

[14]. Anirudha Narain, Dinesh Chandra, and R. K. Singh, (2014). “Model Order Reduction Using Fuzzy C-means Clustering”. Transactions of Institute of Measurement nd Control.

[15]. B. Vishwakarma, (2011). “Order Reduction Using Modified Pole Clustering and Padè Approximation”. World Academy of Science, Engineering and Technology, Vol.56.

[16]. Awadhesh Kumar and D. Chandra, “Improved Padè Pole Clustering Approximant”. International Conference on Computer Science and Electronics Engineering.

[17]. J. C. Mason and D. C. Handscomb, (2003). Chebyshev Polynomials. Chapman & Hall/CRC.

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Research Paper

Approximation of Large Scale Systems by Balanced Truncation and Singular Perturbation Method

Deepak Gupta* , Awadhesh Kumar**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

Gupta, D., and Kumar, A. (2016). Approximation of Large Scale Systems by Balanced Truncation and Singular Perturbation Method. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 14-20. https://doi.org/10.26634/jic.4.2.4878

Abstract

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.

References

[1]. Moore, B. C, (1981). “Principal Component Analysis in Linear System: Controllability, Observability and Model Reduction”. IEEE Transactions on Automatic Control, Vol.26, pp.17-32.

[2]. Stewart, D.E and Leyk, T.S., (1996). “Error Estimates for Krylov Subspace Approximations of Matrix Exponential”. Journal on Computer and Applied Mathematics, Vol.72, pp.359–369.

[3]. Y.V. Genin and S.Y. Kung, “A two-variable Approach to the Model Reduction Problem with Hankel Norm Criterion”. IEEE Transactions on Circuits and System, Vol. 28, No. 9, pp. 912-924.

[4]. Sirovich, L, (1987). “Turbulence and the Dynamics of Coherent Structures”. Quarterly of Applied Mathematics, Vol.5, pp.561–590.

[5]. Saad, Y., (1992). “Analysis of Some Krylov Subspace Approximations to the Matrix Exponential Operator”. SIAM Journal on Numerical Analysis, Vol.29, pp. 209-228.

[6]. Gugercin, S., Antoulas, A.C., and Beattie, C.A., (2006). “A Rational Krylov Iteration for Optimal H2 Model th Reduction”. Proceedings of the 17 International Symposium on Mathematical Theory of Networks and Systems, pp.1665-1667.

[7]. Y. –F. Su, J, Wang, X, Zeng, Z Bai, Charles Chiang and D. Zhou, (2004). “SAPOR: Second-Order Arnoldi Method for Passive Order Reduction of RCS Circuits”. Proceedings of IEEE International Conference on Computer Aided Design.

[8]. Y. Chahlaoui, and P. Van Dooren, (2002). “A Collection of Benchmark Examples for Model Reduction of Linear Time Invariant Dynamical Systems”. Retrived from http://citeseerx.ist.psu.edu/viewdoc/download?doi= 10.1.1.546.9783&rep=rep1&type=pdf

[9]. Anderson, B. D. O., and Vongpanitlerd, S., (1973). Network Analysis and Synthesis. Englewood Cliffs, NJ: Prentice-Hall.

[10]. Antoulas, A. C. (2003). Lectures on the Approximation of Linear Dynamical Systems. Draft, to appear, SIAM Press.

[11]. Antoulas, A. C., (1998). ”Approximation of Linear Operators in the 2-norm”. Special Issue of LAA (Linear Algebra and Applications) on Challenges in Matrix Theory, Vol.278, pp.309–316.

[12]. Antoulas, A. C., Sorensen, D. C., and Gugercin, S., (2001). “A Survey of Model Reduction Methods for Large Scale Systems”. Contemporary Mathematics, AMS Publications, Vol.28, pp. 193-219.

[13]. Antoulas, A. C., and Sorensen, D. C., (2002). “The Sylvester Equation and Approximate Balanced Reduction”. Fourth Special Issue on Linear Systems and Control, Linear Algebra and Its Applications, pp.351–352.

[14]. Benner, P., E., Quintana-Orti, E. S. and Quintana-Orti, G., (2001). “Efficient Numerical Algorithms for Balanced Stochastic Truncation”. International Journal of Applied Mathematics and Computer Science, Special Issue: Numerical Analysis and Systems Theory, Vol. 11, No. 5.

[15]. Antoulas, A. C., Sorensen, D. C., and Zhou, Y. K., (2002). “On the decay rate of Hankel Singular Values and Related Issues”. Systems and Control Letters, Vol. 46, pp.323–342.

[16]. Enns, D., (1984). “Model Reduction with Balanced Realizations: An Error Bound and a Frequency Weighted rd Generalization”. Proceedings of the 23 IEEE Conference Decision and Control.

[17]. Green, M., (1981). “A Relative Error Bound for Balanced Stochastic Truncation”. IEEE Transactions on Automatic Control, Vol. 33.

[18]. Gawronski, W., and Juang, J. N., (1990). “Model Reduction in Limited Time and Frequency Intervals”. International Journal on Systems Science, Vol. 21, No. 2, pp. 349-376.

[19]. Green, M., (1988). “A Relative Error Bound for Balanced Stochastic Truncation”. IEEE Transactions on Automatic Control, pp.961–965.

[20]. Pernebo, L., and Silverman, L. M., (1982). ”Model Reduction via Balanced State Space Representation”. IEEE Transactions on Automatic Control, pp.382–382.

[21]. Zhou, K., Doyle, J. C., and Glover, K., (1996). Robust and Optimal Control. Engelwood Cliffs, NJ: Prentice Hall.

[22]. Gugercin, S., and Antoulas, A. C., (2000). “A Comparative Study of 7 Model Reduction Algorithms”. Proceedings of IEEE International Conference on Decision and Control.

[23]. Lin, C. A., and Chiu, T. Y., (1992). “Model Reduction via Frequency Weighted Balanced Realization”. Control Theory and Advanced Technology, Vol. 8, pp. 341-351.

[24]. Wang, G., Sreeram, V., and Liu, W. Q., (1999). “A New Frequency Weighted Balanced Truncation Method and an Error Bound”. IEEE Transactions on Automatic Control, Vol. 44, No. 9, pp. 1734-1737.

[25]. Wang, W., and Safanov, M. G., (1992). ”Multiplicativeerror Bound for Balanced Stochastic Truncation Model Reduction”. IEEE Transactions on Automatic Control, Vol. 37, No. 8, pp. 1265-1267.

[26]. Ober, R., (1991). “Balanced Parameterization of Classes of Linear Systems”. SIAM Journal on Control Applications.

[27]. Opdenacker, P. C., and Jonckheere, E. A., (1988). “A Contraction Mapping Preserving Balanced Reduction Scheme and its Infinity Norm Error Bounds”. IEEE Transactions on Circuits and Systems.

[28]. Jonckheere, E. A., and Silverman, L. M., (1983). “A New of Invariants for Linear System Application to Reduced Order Compensator Designs”. IEEE Transactions on Automatic Control, Vol.28, pp.953-964.

[29]. Liu, Y., and Anderson, B.D.O., (1989). “Singular Perturbation Approximation of Balanced System”. International Journal of Control, Vol.50, pp.1379–140.

[30]. Sreeram, V., Anderson, B. D. O., and Madievski, A. G., (1995). “Frequency Weighted Balanced Reduction Technique: Generalization and an Error Bound”. in Proceedings of 34 IEEE Conference on Decision and Control, New Orleans, Louisiana, USA.

[31]. J. Li, F. Wang, and J. White, (1999). “An Efficient Lyapunov Equation-based Approach for Generating Reduced-order Models of Interconnect”. Proceedings of 36 IEEE/ACM Design Automation Conference, New Orleans, LA.

[32]. T. Penzl, (2000). “Eigen values Decay Bounds for Solutions of Lyapunov Equation the Symmetric Case”. Systems and Control Letters.

[33]. Kumar. A, and Chandra. D., (2014). “Improved Padepole Clustering Approximant”. International Journal of Computer Applications, Vol.114, No.1, pp. 28-32.

[34]. Gupta. D, and Kumar. A., (2015). “An Investigation into Model Order Reduction through Balancing Methods and their Error Norm”. International Journal Engineering Technology and Advanced Engineering, Vol. 6, No.3, pp.287-292.

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Research Paper

Fuzzy Control of Inverted Pendulum System

Rajesh Tanna* , 0, Vivek Viswanadh*

, Assistant Professor, Department of Electrical and Electronics Engineering, Vignan Institute of Information Technology, Andhra Pradesh, India.

Professor & Principal, Vignan Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

Tanna, R., Mary, K.A., and Viswanadh, V. (2016). Fuzzy Control of Inverted Pendulum System. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 21-28. https://doi.org/10.26634/jic.4.2.4879

Abstract

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.

References

[1]. Kazuo Tanaka, Takayuki Ikeda, and Hua O Wang, (1998). “Fuzzy Regulators and Fuzzy Observers: Relaxed Stability Conditions and LMI Based Designs”. IEEE Transactions on Fuzzy Systems, Vol. 6, No. 2, pp.250-265.

[2]. Kazuo Tanaka, Hiroshi Ohtake, and Hua O. Wang, (2007). “A Descriptor System Approach to Fuzzy Control System Design via Fuzzy Lyapunov Functions”. IEEE Transactions on Fuzzy Systems, Vol.15, No. 3, pp.333-341.

[3]. Kuang-Yow Lian, Hui-Wen Tu, and Jeih-Jang Liou, (2000). “Stability Conditions for LMI Based Fuzzy Control From Viewpoint of Membership Functions”. IEEE Transactions on Fuzzy Systems, Vol. 14, No. 6, pp.874-884.

[4]. L. Behera and I. Kar, (2008). Intelligent Systems and Control: Principles and Applications. Oxford.

[5]. Kazuo Tanaka and Hua O. Wang, (2001). “Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach ”. IEEE Transactions on Fuzzy Systems, Vol.17, No.4, pp.911-922.

[6]. Javier Aracil and Francisco Gordillo, (2000). Stability Issues in Fuzzy Control. Springer Verlag, New York.

[7]. Stanislaw H. Zak,(1999). “Stabilizing Fuzzy System Models Using Linear Controllers”. IEEE Transactions on Fuzzy Systems, Vol. 7, No. 2, pp.236-240.

[8]. Hua O. Wang, Kazuo Tanak, and Michael F. Griffin, (1996). “An Approach to Fuzzy Control of Nonlinear Systems: Stability and Design Issues”. IEEE Transactions on Fuzzy Systems, Vol. 4, No. 1, pp.14-23.

[9]. H. Wang and K. Tanaka, (1996). “An LMI-based stable Fuzzy Control of Nonlinear Systems and its Application to th Control of Chaos”. Proceedings of 5 IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 1433–1438.

[10]. K. Tanaka and M. Sugeno, (1993). “Concept of Stability Margin of Fuzzy Systems and Design of Robust Fuzzy th Controllers”. in Proceedings of 5 IEEE International Conference on Fuzzy Systems, San Francisco, CA, Vol. 1, pp. 29–34.

[11]. S. Kawamoto et al., (1996). “Nonlinear Control and Rigorous Stability Analysis Based on Fuzzy System for th Inverted Pendulum”. in Proceedings of 5 IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 1427–1432.

[12]. Wang, H.O., K. Tanaka and M. Griffin, (1995). ”Parallel Distributed Compensation of Nonlinear Systems by Takagi–sugeno Fuzzy Model”. IEEE, pp.531–538.

[13]. Rajesh Tanna, and Alok Kanti Deb, (2015). “Takagi- Sugeno Fuzzy Control for Nonlinear Systems”. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 4, No.9, pp.7830- 7839

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Research Paper

Model Order Reduction Using Krylov-Subspace Based Two-sided Arnoldi Algorithm

Nikku * , Awadhesh Kumar**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

Shahi, N., and Kumar, A. (2016). Model Order Reduction Using Krylov-Subspace Based Two-sided Arnoldi Algorithm. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 29-38. https://doi.org/10.26634/jic.4.2.4880

Abstract

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.

References

[1]. Salimbahrami, B., Lohmann, B., Bechtold, T., and Korvink, J. G., (2003). "Two-sided Arnoldi Algorithm and Its Application in Order Reduction of MEMS". in Proceedings of 4^th Mathmod, pp. 1021–1028.

[2]. Salimbahrami, B. and Lohmann, B., (2002). "Two sided Arnoldi Method for Model Order Reduction of High Order MIMO Systems”. Retrived from http://www.iat.unibren.de/ mitarbeiter/salimbahrami/2arnoldi mimo.pdf

[3]. Freund, R.W., (2000). "Krylov Subspace Methods for Reduced Order Modeling in Circuit Simulation". Journal on Computer and Applied Mathematics, Vol.123, pp.395–421.

[4]. A.C. Antoulas, (2005). Approximation of Large-Scale Dynamical Systems. SIAM Philadelphia, PA.

[5]. A.C. Antoulas, D.C. Sorenson, and S. Gugercin, (2001). "A Survey of Model Reduction for Large Scale Systems". Contemporary Mathematics, AMS Publications, Vol.280, pp.193-219.

[6]. S. Gugercin, and J. Rebecca Li, (2003). "Smith type methods for Balanced Truncation of Large Sparse Systems". in Proceedings of Dimension Reduction of Large Scale Systems, Germany.

[7]. D. Chaniotis, and M.A. Pai, (2005). "Model Reduction in Power Systems Using Krylov Subspace Methods". IEEE Transactions on Power Systems, Vol.20, No.2, pp.888-894.

[8]. Freund, R.W, (2000). "Passive Reduced Order Modelling via Krylov Subspace Methods". Numerical Analysis Manuscript.

[9]. W.E. Arnoldi, (1951). "The Principle of Minimized Iteration in Solution of the Matrix Eigen value Problem". Quarterly Journal of Mechanics and Applied Mathematics, Vol.9, pp.17–29.

[10]. T. Penzl, (1999). "Algorithms for Model Reduction of La rge Dynamics Systems". Linear Algebra and its Applications, Vol. 415, No. 2,pp.322-343.

[11]. Cullum, J. and Zhang, T, (2002). "Two-sided Arnoldi and Nonsymmetric Lanczos Algorithms". SIAM Journal on Matrix Analysis and Applications, Vol.24, No.2, pp. 303-309.

[12]. E.J. Grimme, (2002). “Krylov Projection Methods for Model Reduction”. PhD thesis, Department of Electrical Engineering, University of Illinois at Urbana Campaign.

[13]. Salimbahrami, B. and Lohmann, B., (2002). "Krylov subspace methods in Linear Model Order Reduction: Introduction and Invariance properties". Scientific report, Retrived from http://www.iat.uni-bremen.de/mitarbeiter/ salimbahrami/Invariance properties.pdf

[14]. G. Parmar, S. Mukherjee and R. Prasad, (2007). "system Reduction Using Factor Division Algorithm and EigenSpectrum Analysis". Applied Mathematical Modeling, Elsevier, Vol.31, pp.2542-2552.

[15]. Moore, B.C, (1981). "Principal Component Analysis in Linear Systems: Controllability, Observability and Model Reduction". IEEE Transactions on Automatic Control, Vol.26, pp.17-31.

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 4 Issue 2 February - April 2016

Automation in Speed Control and Overcurrent Protection of Three Phase Induction Motor

M. M. Kharade* , N.R Kulkarni**

* PG Scholar, Department of Electrical Engineering, Modern College of Engineering, Pune, India. ** Professor and HOD, Department of Electrical Engineering, Modern College of Engineering, Pune, India.

Kharade, M.M., and Kulkarni, N.R. (2016). Automation in Speed Control and Overcurrent Protection of Three Phase Induction Motor. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 1-6. https://doi.org/10.26634/jic.4.2.4876

Abstract

References

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Model Order Reduction using Chebyshev Polynomial, Stability Equation Method and Fuzzy C-means Clustering

Maneesh Kumar Gupta* , Awadhesh Kumar**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, Uttar Pradesh, India. ** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, Uttar Pradesh, India.

Gupta, M.K., and Kumar, A. (2016). Model Order Reduction using Chebyshev Polynomial, Stability Equation Method and Fuzzy C-means Clustering. i-manager’s Journal on Instrumentation and Control Engineering, 4(21), 7-13. https://doi.org/10.26634/jic.4.2.4877

Abstract

References

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Approximation of Large Scale Systems by Balanced Truncation and Singular Perturbation Method

Deepak Gupta* , Awadhesh Kumar**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India. ** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

Gupta, D., and Kumar, A. (2016). Approximation of Large Scale Systems by Balanced Truncation and Singular Perturbation Method. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 14-20. https://doi.org/10.26634/jic.4.2.4878

Abstract

References

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Fuzzy Control of Inverted Pendulum System

Rajesh Tanna* , 0**, Vivek Viswanadh***

*,*** Assistant Professor, Department of Electrical and Electronics Engineering, Vignan Institute of Information Technology, Andhra Pradesh, India. ** Professor & Principal, Vignan Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

Tanna, R., Mary, K.A., and Viswanadh, V. (2016). Fuzzy Control of Inverted Pendulum System. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 21-28. https://doi.org/10.26634/jic.4.2.4879

Abstract

References

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Model Order Reduction Using Krylov-Subspace Based Two-sided Arnoldi Algorithm

Nikku * , Awadhesh Kumar**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India. ** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

Shahi, N., and Kumar, A. (2016). Model Order Reduction Using Krylov-Subspace Based Two-sided Arnoldi Algorithm. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 29-38. https://doi.org/10.26634/jic.4.2.4880

Abstract

References

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* PG Scholar, Department of Electrical Engineering, Modern College of Engineering, Pune, India.

** Professor and HOD, Department of Electrical Engineering, Modern College of Engineering, Pune, India.

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, Uttar Pradesh, India.

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, Uttar Pradesh, India.

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

Rajesh Tanna* , 0, Vivek Viswanadh*

, Assistant Professor, Department of Electrical and Electronics Engineering, Vignan Institute of Information Technology, Andhra Pradesh, India.

Professor & Principal, Vignan Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.