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19 results on '"Panda S"'

1. A Multi-objective Approach to Study the Effects of Ball Race Conformity on Optimum Design of Rolling Element Bearing Using Metaheuristics

Author

Panda, S. N., Panda, S., Khamari, D. S., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pati, Bibudhendu, editor, Panigrahi, Chhabi Rani, editor, Buyya, Rajkumar, editor, and Li, Kuan-Ching, editor

Published
2020
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2. Reevaluation of Ball-Race Conformity Effect on Rolling Element Bearing Life Using PSO

Author

Panda, S. N., Panda, S., Khamari, D. S., Mishra, P., Pattanaik, A. K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Behera, Himansu Sekhar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, and Abraham, Ajith, editor

Published
2019
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4. Optimum Design of Rolling Element Bearing

Author

Panda, S., Mohanty, T., Mishra, D., Biswal, B. B., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Panigrahi, Bijaya Ketan, editor, Suganthan, Ponnuthurai Nagaratnam, editor, and Das, Swagatam, editor

Published
2015
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5. Modeling and Optimization of Rolling Process: A Multi-Objective Approach.

Author

Panda, S. and Panda, S. N.

Subjects
PARTICLE swarm optimization, PROCESS optimization, LUBRICATION & lubricants, ELASTOHYDRODYNAMIC lubrication, PRODUCTION engineering, PRODUCT improvement
Abstract

In a high-speed cold strip rolling process, it is necessary to optimize the process parameters for improved quality in the product. In this study, two separate multi-objective optimization problems for a cold rolling process are formulated. The objectives in one of the cases are minimum isothermal film thickness and film temperature rise in the inlet zone and in another case it is minimum thermal film thickness and film temperature rise in the inlet zone. Particle swarm optimization algorithm has been used for solving the optimization problem. The key input parameters for the cold rolling process are identified and prioritized through the convergence study and the coefficient of variation analysis. A response analysis is performed on the critical input variables. This study assists the process engineer to understand the lubrication in cold strip rolling at high speed and select an appropriate lubricant for a given combination of strip and rolls. [ABSTRACT FROM AUTHOR]

Published
2020
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6. A comparative study of meta-heuristics for local path planning of a mobile robot.

Author

Pattnaik, S. K., Mishra, D., and Panda, S.

Subjects
ROBOTIC path planning, PARTICLE swarm optimization, ALGORITHMS, COST functions, GENETIC algorithms
Abstract

Recent trends in path planning have led to a proliferation of studies that find solutions to the path planning problems in an unknown cluster environment. This study aims to find an optimum impact-free path length for a mobile robot with a multi-objective optimization approach. The multi-objective optimization problem is formulated by using path length and a safety aspect as the two objectives. A hybrid population-based optimization algorithm, i.e. the hybrid particle swarm and chemical reaction optimization (HPCRO) algorithm, has been used to obtain a smooth path for the robot in an unknown environment with circular and/or polygonal obstacles. The results of the HPCRO algorithm are then compared with those of genetic algorithms, chemical reaction optimization and particle swarm optimization. Some statistical tests are performed to illustrate the superiority and potential applicability of the hybrid algorithm. The results of the hybrid algorithm are encouraging in terms of cost function value and computational cost. [ABSTRACT FROM AUTHOR]

Published
2022
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7. Optimal Multi-robot Path Planning Using Particle Swarm Optimization Algorithm Improved by Sine and Cosine Algorithms.

Author

Paikray, H. K., Das, P. K., and Panda, S.

Subjects
ROBOTIC path planning, PARTICLE swarm optimization, MATHEMATICAL optimization, ENERGY consumption, ALGORITHMS
Abstract

This paper highlights a new approach to generate an optimal collision-free trajectory path for each robot in a cluttered and unknown workspace using enhanced particle swarm optimization (IPSO) with sine and cosine algorithms (SCAs). In the current work, PSO has enhanced with the notion of democratic rule in human society and greedy strategy for selecting the optimal position in the successive iteration using sine and cosine algorithms. The projected algorithm mainly emphasizes to produce a deadlock-free successive location of every robot from their current location, preserve a good equilibrium between diversification and intensification, and minimize the path distance for each robot. Results achieved from IPSO–SCA have equated with those developed by IPSO and DE in the same workspace to authenticate the efficiency and robustness of the suggested approach. The outcomes of the simulation and real platform result reveal that IPSO–SCA is superior to IPSO and DE in the form of producing an optimal collision-free path, arrival time, and energy utilization during travel. [ABSTRACT FROM AUTHOR]

Published
2021
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8. An approach for design optimization of 3R manipulator using Adaptive Cuckoo Search algorithm.

Author

Panda, S., Mishra, D., and Biswal, B. B.

Subjects
*PARTICLE swarm optimization, *CUCKOOS, *ALGORITHMS, *INDUSTRIAL robots, *BIOLOGICALLY inspired computing, *DIFFERENTIAL evolution, *SEARCH algorithms, *TABU search algorithm
Abstract

In this study, the workspace volume of a 3R manipulator has been maximized using a biologically inspired optimization algorithm, namely Adaptive Cuckoo Search (ACS) algorithm. The proposed algorithm is tested on four diverse cases involving different constraints and without imposing any constraint. The outcomes of this study are compared with the standard results of different heuristics, such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), and Cuckoo Search (CS) algorithm. Statistical tests were performed to test the superiority of the proposed algorithm. Further, the cross-sectional area of the voids in the workspace has been estimated. The convergence study along with the coefficient of variation analysis identifies the critical kinematic parameters. A constraint conformation study has been performed to investigate the relative importance of the constraints. In addition, to ensure the applicability of the proposed algorithm in practice it is tested by using the kinematic parameters of two existing industrial robot manipulators (KUKA KR-30 and Mitsubishi MRP-700A). It has been found that the predicted results from the proposed algorithm for the two robot manipulators are in line with the actual values. [ABSTRACT FROM AUTHOR]

Published
2020
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9. Process Parameter Optimization of Hydrostatic Extrusion Using Metaheuristic.

Author

Panda, S. and Mishra, D.

Subjects
HYDROSTATIC extrusion, METAL-to-metal contacts, DEFORMATIONS (Mechanics), FRICTION, METAHEURISTIC algorithms, DIES (Metalworking)
Abstract

Friction at die work piece interface can be reduced by the presence of lubricant in the entry zone. As the metal–metal contact in the deformation zone is avoided, the deformation force is reduced and at the same time the product quality and die life are improved. So the prediction of minimum film thickness and its control through process variables is gaining interest in the industry. Two multi-objective nonlinear optimization problems are formulated in this study, one using the minimum thermal thickness and billet temperature at the work zone. And the other optimization problem is formulated by including the minimum isothermal thickness at entry zone and the billet temperature at the work zone. These two multi-objective optimization problems have been solved using particle swarm optimization algorithm (PSO). The key process variables are identified by coefficient of variance (COV) analysis. A sensitivity analysis on the process variables is performed to prioritize the process variables. A design procedure is explored to demonstrate the industrial application of this analysis. [ABSTRACT FROM AUTHOR]

Published
2018
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10. Model Reduction Of Linear Systems By Conventional And Evolutionary Techniques

Author

Panda, S., Tomar, S. K., Prasad, R., and CEMAL ARDIL

Subjects
Mihailov Stability Criterion, Reduced Order Modeling, Particle Swarm Optimization, Continued Fraction Expansions, Integral Squared Error, Stability
Abstract

Reduction of Single Input Single Output (SISO) continuous systems into Reduced Order Model (ROM), using a conventional and an evolutionary technique is presented in this paper. In the conventional technique, the mixed advantages of Mihailov stability criterion and continued fraction expansions (CFE) technique is employed where the reduced denominator polynomial is derived using Mihailov stability criterion and the numerator is obtained by matching the quotients of the Cauer second form of Continued fraction expansions. In the evolutionary technique method Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example., {"references":["M. J. Bosley and F. P. Lees, \"A survey of simple transfer function\nderivations from high order state variable models\", Automatica, Vol. 8,\npp. 765-775, !978.","M. F. Hutton and B. Fried land, \"Routh approximations for reducing\norder of linear time- invariant systems\", IEEE Trans. Auto. Control, Vol.\n20, pp 329-337, 1975.","R. K. Appiah, \"Linear model reduction using Hurwitz polynomial\napproximation\", Int. J. Control, Vol. 28, no. 3, pp 477-488, 1978.","T. C. Chen, C. Y. Chang and K. W. Han, \"Reduction of transfer\nfunctions by the stability equation method\", Journal of Franklin\nInstitute, Vol. 308, pp 389-404, 1979.","Y. Shamash, \"Truncation method of reduction: a viable alternative\",\nElectronics Letters, Vol. 17, pp 97-99, 1981.","P. O. Gutman, C. F. Mannerfelt and P. Molander, \"Contributions to the\nmodel reduction problem\", IEEE Trans. Auto. Control, Vol. 27, pp 454-\n455, 1982.","Y. Shamash, \"Model reduction using the Routh stability criterion and\nthe Pade approximation technique\", Int. J. Control, Vol. 21, pp 475-484,\n1975.","T. C. Chen, C. Y. Chang and K. W. Han, \"Model Reduction using the\nstability-equation method and the Pade approximation method\", Journal\nof Franklin Institute, Vol. 309, pp 473-490, 1980.","Bai-Wu Wan, \"Linear model reduction using Mihailov criterion and\nPade approximation technique\", Int. J. Control, Vol. 33, pp 1073-1089,\n1981.\n[10] V. Singh, D. Chandra and H. Kar, \"Improved Routh-Pade\nApproximants: A Computer-Aided Approach\", IEEE Trans. Auto.\nControl, Vol. 49. No. 2, pp292-296, 2004.\n[11] J. Kennedy and R. C. Eberhart, \"Particle swarm optimization\", IEEE\nInt.Conf. on Neural Networks, IV, 1942-1948, Piscataway, NJ, 1995.\n[12] S. Panda, and N. P. Padhy \"Comparison of Particle Swarm Optimization\nand Genetic Algorithm for FACTS-based Controller Design\", Applied\nSoft Computing. Vol. 8, pp. 1418-1427, 2008.\n[13] Wan Bai-Wu, \"Linear model reduction using Mihailov criterion and\nPade approximation technique.\" Int. J. Control, 1981, No 33, pp 1073-\n1089.\n[14] Chen, C.F. and Sheih, L.S \"A Novel approach to linear model\nsimplification.\" Int. J. Control, 1968, No 8, pp 561-570.\n[15] T. N. Lukas. \"Linear system reduction by the modified factor division\nmethod\" IEEE Proceedings Vol. 133 Part D No. 6, nov.-1986, pp-293-\n295.\n[16] M. Lal and H. Singh, \"On the determination of a transfer function matrix\nfrom the given state equations.\" Int. J. Control, Vol. 15, pp 333-335,\n1972.\n[17] Sidhartha Panda, N.P.Padhy, R.N.Patel, \"Power System Stability\nImprovement by PSO Optimized SSSC-based Damping Controller\",\nElectric Power Components & Systems, Vol. 36, No. 5, pp. 468-490,\n2008.\n[18] Sidhartha Panda and N.P.Padhy, \"Optimal location and controller design\nof STATCOM using particle swarm optimization\", Journal of the\nFranklin Institute, Vol.345, pp. 166-181, 2008."]}

Published
2009
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11. Evolutionary Techniques For Model Order Reduction Of Large Scale Linear Systems

Author

Panda, S., Yadav, J. S., Patidar, N. P., and CEMAL ARDIL

Subjects
Genetic Algorithm, Particle Swarm Optimization, Integral Squared Error, Stability, Order Reduction, Transfer Function
Abstract

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. In this paper both PSO and GA optimization are employed for finding stable reduced order models of single-input- single-output large-scale linear systems. Both the techniques guarantee stability of reduced order model if the original high order model is stable. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example from literature and the results are compared with recently published conventional model reduction technique., {"references":["M.J.Bosley and F.P.Lees, \"A survey of simple transfer function\nderivations from high order state variable models\", Automatica, Vol. 8,\npp. 765-775, !978.","M.F.Hutton and B. Fried land, \"Routh approximations for reducing\norder of linear time- invariant systems\", IEEE Trans. Auto. Control, Vol.\n20, pp 329-337, 1975.","R.K.Appiah, \"Linear model reduction using Hurwitz polynomial\napproximation\", Int. J. Control, Vol. 28, no. 3, pp 477-488, 1978.","T. C. Chen, C.Y.Chang and K.W.Han, \"Reduction of transfer functions\nby the stability equation method\", Journal of Franklin Institute, Vol.\n308, pp 389-404, 1979.","Y.Shamash, \"Truncation method of reduction: a viable alternative\",\nElectronics Letters, Vol. 17, pp 97-99, 1981.","P.O.Gutman, C.F.Mannerfelt and P.Molander, \"Contributions to the\nmodel reduction problem\", IEEE Trans. Auto. Control, Vol. 27, pp 454-\n455, 1982.","Y. Shamash, \"Model reduction using the Routh stability criterion and\nthe Pade approximation technique\", Int. J. Control, Vol. 21, pp 475-484,\n1975.","T.C.Chen, C.Y.Chang and K.W.Han, \"Model Reduction using the\nstability-equation method and the Pade approximation method\", Journal\nof Franklin Institute, Vol. 309, pp 473-490, 1980.","Bai-Wu Wan, \"Linear model reduction using Mihailov criterion and\nPade approximation technique\", Int. J. Control, Vol. 33, pp 1073-1089,\n1981.\n[10] V.Singh, D.Chandra and H.Kar, \"Improved Routh-Pade Approximants:\nA Computer-Aided Approach\", IEEE Trans. Auto. Control, Vol. 49. No.\n2, pp292-296, 2004.\n[11] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and\nMachine Learning, Addison-Wesley, 1989.\n[12] S.Panda and N.P.Padhy, \"Comparison of Particle Swarm Optimization\nand Genetic Algorithm for FACTS-based Controller Design\", Applied\nSoft Computing. Vol. 8, Issue 4, pp. 1418-1427, 2008.\n[13] S.Panda and N.P.Padhy, \"Application of Genetic Algorithm for PSS and\nFACTS based Controller Design\", International Journal of\nComputational Methods, Vol. 5, Issue 4, pp. 607-620, 2008.\n[14] S.Panda and R.N.Patel, \"Transient Stability Improvement by Optimally\nLocated STATCOMs Employing Genetic Algorithm\" International\nJournal of Energy Technology and Policy, Vol. 5, No. 4, pp. 404-421,\n2007.\n[15] S.Panda and R.N.Patel, \"Damping Power System Oscillations by\nGenetically Optimized PSS and TCSC Controller\" International Journal\nof Energy Technology and Policy, Inderscience, Vol. 5, No. 4, pp. 457-\n474, 2007.\n[16] S.Panda and R.N.Patel, \"Optimal Location of Shunt FACTS Controllers\nfor Transient Stability Improvement Employing Genetic Algorithm\",\nElectric Power Components and Systems, Taylor and Francis, Vol. 35,\nNo. 2, pp. 189-203, 2007.\n[17] J. Kennedy and R.C.Eberhart, \"Particle swarm optimization\", IEEE\nInt.Conf. on Neural Networks, IV, 1942-1948, Piscataway, NJ, 1995.\n[18] S.Panda, N.P.Padhy, R.N.Patel, \"Power System Stability Improvement\nby PSO Optimized SSSC-based Damping Controller\", Electric Power\nComponents & Systems, Taylor and Francis, Vol. 36, No. 5, pp. 468-\n490, 2008.\n[19] S.Panda and N.P.Padhy, \"Optimal location and controller design of\nSTATCOM using particle swarm optimization\", Journal of the Franklin\nInstitute, Elsevier, Vol.345, pp. 166-181, 2008.\n[20] S.Panda, N.P.Padhy and R.N.Patel, \"Robust Coordinated Design of PSS\nand TCSC using PSO Technique for Power System Stability\nEnhancement\", Journal of Electrical Systems, Vol. 3, No. 2, pp. 109-\n123, 2007.\n[21] C. B. Vishwakarma and R.Prasad, \"Clustering Method for Reducing\nOrder of Linear System using Pade Approximation\", IETE Journal of\nResearch, Vol. 54, Issue 5, pp. 326-330, 2008.\n[22] S.Mukherjee, and R.N.Mishra, Order reduction of linear systems using\nan error minimization technique, Journal of Franklin Inst. Vol. 323, No.\n1, pp. 23-32, 1987.\n[23] S.Panda, S.K.Tomar, R.Prasad, C.Ardil, \"Reduction of Linear Time-\nInvariant Systems Using Routh-Approximation and PSO\", International\nJournal of Applied Mathematics and Computer Sciences, Vol. 5, No. 2,\npp. 82-89, 2009.\n[24] S.Panda, S.K.Tomar, R.Prasad, C.Ardil, \"Model Reduction of Linear\nSystems by Conventional and Evolutionary Techniques\", International\nJournal of Computational and Mathematical Sciences, Vol. 3, No. 1, pp.\n28-34, 2009.\n[25] R.Parthasarathy, and K. N. Jayasimha, System reduction using stability\nequation method and modified Cauer continued fraction, Proc. IEEE\nVol. 70, No. 10, pp. 1234-1236, Oct. 1982.\n[26] L.S. hieh, and Y.J.Wei, A mixed method for multivariable system\nreduction, IEEE Trans. Autom. Control, Vol. AC-20, pp. 429-432, 1975.\n[27] R.Prasad, and J.Pal, Stable reduction of linear systems by continued\nfractions, J. Inst. Eng. India, IE (I) J.EL, Vol. 72, pp. 113-116, Oct.\n1991.\n[28] J. Pal, Stable reduced -order Pade approximants using the Routh-\nHurwitz array, Electronics letters, Vol. 15, No. 8, pp. 225-226, April\n1979."]}

Published
2009
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12. Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller.

Author

Manoharan, N., Dash, S. S., Rajesh, K. S., and Panda, S.

Subjects
PID controllers, MATHEMATICAL optimization, AUTOMATIC control systems, PARTICLE swarm optimization, ELECTRIC power systems, GENETIC algorithms, DEGREES of freedom
Abstract

A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportional-integral-derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system. [ABSTRACT FROM AUTHOR]

Published
2017
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13. Comparative study on optimum design of rolling element bearing.

Author

Panda, S., Panda, S.N., Nanda, P., and Mishra, D.

Subjects
*ROLLING (Metalwork), *BEARINGS (Machinery), *METAL fatigue, *COMPARATIVE studies, *MATHEMATICAL optimization
Abstract

Long fatigue life is the most important objective in the optimum design of rolling element bearing. In the present study the fatigue life of a radial ball bearing is maximized. The nonlinear constrained optimization problem has been solved using particle swarm optimization algorithm and a hybrid PSO and Teaching Learning based Optimization algorithm. The algorithm uses a ranking method of constraint handling and contact stress has been introduced as a new constraint. The results have been compared with the established available results. A constraint violation study has been carried out to prioritize the constraints. A convergence study has been performed to identify the key design variables. Encouraging results in terms of objective function values and CPU time have been reported in this study. [ABSTRACT FROM AUTHOR]

Published
2015
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14. Optimization of Multiple Response Characteristics of EDM Process Using Taguchi-Based Grey Relational Analysis and Modified PSO.

Author

Panda, S., Mishra, D., Biswal, B. B., and Nanda, P.

Subjects
ELECTRIC metal-cutting, GREY relational analysis, PARTICLE swarm optimization, RESPONSE surfaces (Statistics), TAGUCHI methods
Abstract

Electrical discharge machining is an alternative process for machining complex and intricate shapes. In this paper, an inter-relationship of various electrical discharge machining parameters, namely discharge current, pulse on and off time and dielectric flow rate on material removal rate (MRR), tool wear rate (TWR), surface finish (a) and dimensional tolerance using a Taguchi-Grey relational analysis. The response surface methodology is used to develop a second order model for MRR, TWR and a in terms of process parameters. Finally, a multi-objective optimization problem is formulated by using MRR, TWR and a. The multi-objective problem is then optimized through a modified particle swarm optimization (PSO) algorithm to find the optimum level of parameters. In this research, the results of the proposed method are validated through confirmation experiment. The work piece material used for experimentation is stainless steel of S304 grade. [ABSTRACT FROM AUTHOR]

Published
2015
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15. Design and comparative performance analysis of PID controlled automatic voltage regulator tuned by many optimizing liaisons.

Author

Sahu, B. K., Mohanty, P. K., Panda, S., Kar, S. K., and Mishra, N.

Abstract

This paper deals with the design of Proportional, Integral, and Derivative (PID) controller to an Automatic Voltage Regulator (AVR) tuned by recently developed Simplified Particle Swarm Optimization algorithm so called, Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particle's best known position and making it easier to tune the behavioural parameters. The proposed method is compared with the earlier used PSO algorithm. For performance studies; Transient response analysis, Bode plot analysis and Root locus analysis are explained in details. The robustness analysis is done by varying the time constants of amplifier, exciter, generator & sensor in the range of −50% to + 50% with a step size of 25% respectively. The results of these analyses using the MOL algorithm are found to be better with respect to the analysis of the PID controller using PSO algorithm. [ABSTRACT FROM PUBLISHER]

Published
2012
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16. Revolute manipulator workspace optimization: A comparative study.

Author

Panda, S., Mishra, D., and Biswal, B.B.

Subjects
MATHEMATICAL optimization, COMPARATIVE studies, INDUSTRIAL robots, PARAMETER estimation, GENETIC algorithms, MANIPULATORS (Machinery), PARTICLE swarm optimization
Abstract

Abstract: Robotic manipulators with three revolute families of positional configurations are very common in the industrial robots. The capability of a robot largely depends on the workspace of the manipulator apart from other parameters. In this work, an evolutionary optimization algorithm based on foraging behavior of Escherichia coli bacteria present in human intestine is utilized to optimize the workspace volume of a 3R manipulator. The proposed optimization method is subjected to some modifications for faster convergence than the original algorithm. Further, the method is also very useful in optimization problems in a highly constrained environment such as the robot workspace optimization. The test results are compared with standard results available using other optimization algorithms such as Differential Evolution, Genetic Algorithm and Particle Swarm Optimization. In addition, this work extends the application of the proposed algorithm to two different industrial robots. An important implication of this paper is that the present algorithm is found to be superior to other methods in terms of computational efficiency. [Copyright &y& Elsevier]

Published
2013
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17. Power-system Stability Improvement by PSO Optimized SSSC-based Damping Controller.

Author

Panda, S., Padhy, N. P., and Patel, R. N.

Subjects
*VIBRATION (Mechanics), *MATHEMATICAL optimization, *FLUCTUATIONS (Physics), *OSCILLATIONS, *WAVES (Physics)
Abstract

Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this article. The design problem of the proposed controller is formulated as an optimization problem, and the particle swarm optimization technique is employed to search for the optimal controller parameters. By minimizing a time-domain-based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved, stability performance of the system is improved. The performance of the proposed controller is evaluated under different disturbances for both a single-machine infinite-bus power system and a multi-machine power system. Results are presented to show the effectiveness of the proposed controller. It is observed that the proposed SSSC-based controller provides efficient damping to power-system oscillations and greatly improves the system voltage profile under various severe disturbances. Furthermore, the simulation results show that in a multi-machine power system, the modal oscillations are effectively damped by the proposed SSSC controller. [ABSTRACT FROM AUTHOR]

Published
2008
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18. Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization

Author

Panda, S., Sahu, B.K., and Mohanty, P.K.

Subjects
*AUTOMATIC control of voltage regulators, *PID controllers, *PARTICLE swarm optimization, *TRANSIENTS (Dynamics), *ROOT-locus method, *HEURISTIC algorithms
Abstract

Abstract: This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of −50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms. [Copyright &y& Elsevier]

Published
2012
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19. Conventional and PSO Based Approaches for Model Reduction of SISO Discrete Systems

Author

Tomar, S. K., Prasad, R., Panda, S., and CEMAL ARDIL

Subjects
Modified CauerForm, Polynomial Differentiation, Bilinear Transformation, Particle Swarm Optimization, Integral Squared Error, Reduced Order Model, Discrete System, Single Input Single Output (SISO)
Abstract

Reduction of Single Input Single Output (SISO) discrete systems into lower order model, using a conventional and an evolutionary technique is presented in this paper. In the conventional technique, the mixed advantages of Modified Cauer Form (MCF) and differentiation are used. In this method the original discrete system is, first, converted into equivalent continuous system by applying bilinear transformation. The denominator of the equivalent continuous system and its reciprocal are differentiated successively, the reduced denominator of the desired order is obtained by combining the differentiated polynomials. The numerator is obtained by matching the quotients of MCF. The reduced continuous system is converted back into discrete system using inverse bilinear transformation. In the evolutionary technique method, Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example., {"references":["R. Genesio and M. Milanese, \"A note on the derivation and use of\nreduced-order models\", IEEE Transactions on Automatic Control, Vol.\n21, pages 118-122, 1976.","S. Panda, S. K. Tomar, R. Prasad and C. Ardil, \"Reduction of Linear\nTime-Invariant Systems Using Routh-Approximation and PSO\",\nInternational Journal of Applied Mathematics and Computer Sciences,\nVol. 5, No. 2, pp. 82-89, 2009.","S. Panda, S. K. Tomar, R. Prasad and C. Ardil, \"Model Reduction of\nLinear Systems by Conventional and Evolutionary Techniques\",\nInternational Journal of Computational and Mathematical Sciences,\nVol. 3, No. 1, pp. 28-34, 2009.","S. Panda, J. S. Yadav, N. P. Patidar and C. Ardil, \"Evolutionary\nTechniques for Model Order Reduction of Large Scale Linear Systems\",\nInternational Journal of Applied Science, Engineering and Technology,\nVol. 5, No. 1, pp. 22-28, 2009.","J. S. Yadav, N. P. Patidar, J. Singhai, S. Panda and C. Ardil \"A\nCombined Conventional and Differential Evolution Method for Model\nOrder Reduction\", International Journal of Computational Intelligence,\nVol. 5, No. 2, pp. 111-118, 2009.","Y. Shamash, \"Continued fraction methods for the reduction of discrete\ntime dynamic systems\", Int. Journal of Control, Vol. 20, pages 267-268,\n1974.","C.P. Therapos, \"A direct method for model reduction of discrete\nsystem\", Journal of Franklin Institute, Vol. 318, pp. 243-251, 1984.","J.P. Tiwari, and S.K. Bhagat, \"Simplification of discrete time systems by\nimproved Routh stability criterion via p-domain\", Journal of IE (India),\nVol. 85, pp. 89-192, 2004.","J. Kennedy and R. C. Eberhart, \"Particle swarm optimization\",\nIEEEInt.Conf. on Neural Networks, IV, 1942-1948, Piscataway, NJ,\n1995.\n[10] Sidhartha Panda and N. P. Padhy, \"Comparison of Particle Swarm\nOptimization and Genetic Algorithm for FACTS-based Controller\nDesign\", Applied Soft Computing, Vol. 8, Issue 4, pp. 1418-1427, 2008.\n[11] A.C. Davies, \"Bilinear transformation of polynomials,\" IEEE\nTransaction on Circuits and systems, CAS-21, pp 792-794, 974.\n[12] R. Parthasarthy and K.N. Jaysimha, \"Bilinear Transformations by\nSynthetic Division,\" IEEE Transaction on Automatic Control. Vol. 29,\nNo. 6, pp. 575-576, 1984.\n[13] P.Gutman, C.F.Mannerfelt and P.Molandar, \"Contribution to the model\nreduction problem,\" IEEE Transaction on.Automatic Control, Vol. 27,\npp 454-455,1982.\n[14] R. Parthasarthy and Sarasu John, \"System reduction by Routh\napproximation and modified Cauer continued fraction,\" Electronics\nLetters, Vol. 5, No. 21, pp 691-692. 1979.\n[15] Sunita Devi and Rajendra Prasad, \"Reduction of Discrete time systems\nby Routh approximation, National System Conference,\" IIT Kharagpur,\nNSC 2003, pp 30-33, Dec 17-19, 2003.\n[16] R. Parthasarthy and Sarasu John, \"System Reduction using Cauer\nContinued Fraction Expansion about s=0 and s= ∞,\" Electronics Letters,\nVol. 14, No. 8, pp .261-262, 1978.\n[17] Ching-Shieh Hsieh and Chyi Hwang,\"Model reduction of linear\ndiscrete-time systems using bilinear Schwartz approximation,\"\nInternational Journal of System & Science, Vol .21, No 1, pp. 33-49,\n1990."]}

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