Journal Papers

In Elsevier Journals – 51 papers (Available online via ScienceDirect)

In IEEE Journals – 11 papers (Available online via IEEEXplore)

In Multidisciplinary Digital Publishing Institute (MDPI) Journals - 9 papers (Available online via MDPI) 

In Public Library of Science (PLoS) Journals – 5 paper (Available online via PLoS Journals)

In Springer Journals – 4 papers (Available online via SpringerLink)

In Institute of Physics (IOP) Journals – 3 papers (Available online via IOPScience)

In Cambridge University Press Journals – 2 paper (Available online via Cambridge Core) 

In Taylor & Francis Journals – 2 paper (Available online via Taylor & Francis Online)

In American Institute of Physics (AIP) Journals – 1 paper (Available online via AIP Scitation)

In American Chemical Society (ACS) Journals – 1 paper (Available online via ACS Publications)

In Royal Society Journals – 1 paper (Available online via The Royal Society Publishing)

In Oxford University Press Journals – 1 paper (Available online via Oxford Academic Journals) 

In Nature Research Journals – 1 paper (Available online via Nature)

1. K. Halder and S. Das, "Stability Region Patterns in Dominant Pole Placement based PID Controller Design for SOPTD Systems", Chemical Engineering Science (Elsevier), vol. 317, p.122020, Nov. 2025, doi: https://doi.org/10.1016/j.ces.2025.122020.

2. D.K. Panda, S. Das and M. Abusara, "Parametric Study of Adaptive Reinforcement Learning for Battery Operations in Microgrids", Renewable Energy (Elsevier), vol. 250, p.123250, Sep. 2025, doi: https://doi.org/10.1016/j.renene.2025.123250.

3. D.K. Panda and S. Das, "Gaussian process digital twin for voltage stability analysis of complex power networks under perturbation", Expert Systems with Applications (Elsevier), vol. 267, p.126151, Apr. 2025, doi: https://doi.org/10.1016/j.eswa.2024.126151.

4. M. Ali, S. Das and S. Townley, "Robot Differential Drive Navigation through Probabilistic Roadmap and Pure Pursuit", IEEE Access, vol. 13, pp. 22118-22132, Jan. 2025, doi: https://doi.org/10.1109/ACCESS.2025.3536383.

5. S. Dong, S. Das and S. Townley, "Drone motion prediction from flight data: a nonlinear time series approach", Systems Science & Control Engineering (Taylor & Francis), vol. 12, no. 1, p.2409098, Oct. 2024, doi: https://doi.org/10.1080/21642583.2024.2409098.

6. E. Udoha, S. Das and M. Abusara, "Centralised Control and Energy Management of Multiple Interconnected Standalone AC Microgrids", Energies (MDPI), vol. 17, no. 20, p.5201, Oct. 2024, doi: https://doi.org/10.3390/en17205201.

7. D.K. Panda, K. Halder, S. Das and S. Townley, "Observer based decentralized load frequency control with false data injection attack for specified network quality and delay", Chaos, Solitons & Fractals (Elsevier), vol. 186, p. 115323, Sep. 2024, doi: https://doi.org/10.1016/j.chaos.2024.115323.

8. A.H. Alibrahim and S. Das, "Fourier Series Related to p-Trigonometric Functions", Axioms (MDPI), vol. 13, no. 9, p.600, Sep. 2024, doi: https://doi.org/10.3390/axioms13090600.

9. A.H. Alibrahim and S. Das, "The forgotten p-versine and p-coversine family of functions revisited", PLoS ONE, vol. 19, no. 8, p.e0308529, Aug. 2024, doi: https://doi.org/10.1371/journal.pone.0308529.

10. L. Alyami, S. Das and S. Townley, "Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia", PLOS Global Public Health, vol. 4, no. 7, p.e0003467, Jul. 2024, doi: https://doi.org/10.1371/journal.pgph.0003467.

11. K. Halder and S. Das, "Stabilizing regions of dominant pole placement for second order lead processes with time delay using filtered PID controllers", PLoS One, vol. 19, no. 6, p.e0304128, Jun. 2024, doi: https://doi.org/10.1371/journal.pone.0304128.

12. A.H. Alibrahim and S. Das, "Bridging the p-special functions between the generalized hyperbolic and trigonometric families", Mathematics (MDPI), vol. 12, no. 8, p.1242, Apr. 2024, doi: https://doi.org/10.3390/math12081242.

13. D.K. Panda, O. Turner, S. Das and M. Abusara, "Prioritized experience replay based deep distributional reinforcement learning for battery operation in microgrids", Journal of Cleaner Production (Elsevier), vol. 434, pp. 139947, Jan. 2024, doi: https://doi.org/10.1016/j.jclepro.2023.139947 .

14. E. Udoha, S. Das and M. Abusara, "Power Flow Management of Interconnected AC Microgrids Using Back-to-Back Converters". Electronics (MDPI), vol. 12, no. 18, pp. 3765, Sep. 2023, doi: https://doi.org/10.3390/electronics12183765.

15. L. Alyami, D.K. Panda, and S. Das, “Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters”, Sensors (MDPI), vol. 23, no. 10, pp. 4734, May 2023, doi: https://doi.org/10.3390/s23104734.

16. N. Bhadra, S.K. Chatterjee, and S. Das, "Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data", PLoS One, vol. 18, no. 5, pp. e0285321, May 2023, doi: https://doi.org/10.1371/journal.pone.0285321.

17. D.K. Panda, S. Das, and S. Townley, "Hyperparameter Optimized Classification Pipeline for Handling Unbalanced Urban and Rural Energy Consumption Patterns", Expert Systems with Applications (Elsevier), vol. 214, pp. 119127, Mar. 2023, doi: https://doi.org/10.1016/j.eswa.2022.119127.

18. K.H. Ali, M. Abusara, A. Tahir and S. Das, "Dual-Layer Q-learning strategy for energy management of battery storage in grid-connected microgrids", Energies (MDPI), vol. 16, no. 3, pp. 1334, Jan. 2023, doi: https://doi.org/10.3390/en16031334.

19. M.B. Sigalo, S. Das, A.C. Pillai, and M. Abusara, "Real-Time Economic Dispatch of CHP Systems with Battery Energy Storage", Energies (MDPI), vol. 16, no. 3, pp. 1274, Jan. 2023, doi: https://doi.org/10.3390/en16031274.

20. S. Das and K. Halder, "Stabilizing Region in Dominant Pole Placement based Discrete Time PID Control of Delayed Lead Processes using Random Sampling", Chaos, Solitons and Fractals (Elsevier), vol. 165, part 2, pp. 112873, Dec. 2022, doi: https://doi.org/10.1016/j.chaos.2022.112873.

21. K. Halder, D.K. Panda, S. Das, S. Das, and A. Gupta "Specified QoS based networked observer and PI controller design with disturbance and noise rejection under random packet dropout", Physica A: Statistical Mechanics and its Applications (Elsevier), vol. 604, pp. 127965, Oct. 2022, doi: https://doi.org/10.1016/j.physa.2022.127965.

22. M. Dutta Choudhury, S. Das, A.G. Banpurkar, and A. Kulkarni, "Regression Analysis of Wetting Characteristics for Different Random Surface Roughness of Polydimethylsiloxane Using Sandpapers", Colloids and Surfaces A: Physicochemical and Engineering Aspects (Elsevier), vol. 647, pp. 129038, Aug. 2022, doi: https://doi.org/10.1016/j.colsurfa.2022.129038.

23. D.K. Panda, S. Das, and S. Townley, "Towards a More Renewable Energy Based Load Frequency Control for Nonstationary Generation and Demand Profiles", IEEE Transactions on Automation Science and Engineering, vol. 19, no. 2, pp. 1217-1232, Apr. 2022, doi: https://doi.org/10.1109/TASE.2020.3042570. 

24. E.P.M. Grist, T.J. McKinley, S. Das, T. Tregenza, A. Jeffries and N. Tregenza, "Estimating Cetacean Population Trends from Static Acoustic Monitoring Data using Paired Year Ratio Assessment (PYRA)", PLoS One, vol. 17, no. 3, pp. e0264289, Mar. 2022, doi: https://doi.org/10.1371/journal.pone.0264289 .

25. H. Chen, S. Das, J. Morgan and K. Maharatna, "Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering", Computers in Biology and Medicine (Elsevier), vol. 142, pp. 105180, Mar. 2022, doi: https://doi.org/10.1016/j.compbiomed.2021.105180.

26. K. Halder, S. Das, and A. Gupta, “Time delay handling in dominant pole placement with PID controllers to obtain stability regions using random sampling”, International Journal of Control (Taylor & Francis), vol. 94, no. 12, pp. 3384-3405, Dec. 2021, doi: https://doi.org/10.1080/00207179.2020.1764110.

27. M.B. Sigalo, A.C. Pillai, S. Das and M. Abusara, "An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming", Energies (MDPI), 14(19), p.6212, Sep. 2021, doi: https://doi.org/10.3390/en14196212.

28. K.H. Ali, M. Sigalo, S. Das, E. Anderlini, A.A. Tahir and M. Abusara, "Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation", Energies (MDPI), 14(18), p.5688, Sep. 2021, doi: https://doi.org/10.3390/en14185688.

29. H. Chen, S. Das, J. Morgan, and K. Maharatna, "An effective PSR-based arrhythmia classifier using self-similarity analysis", Biomedical Signal Processing and Control (Elsevier), Aug. 2021, doi: https://doi.org/10.1016/j.bspc.2021.102851 .

30. K. Halder, S. Das, D.K. Panda, S. Das, and A. Gupta, "QoS Aware Joint Observer and Networked PI/PID Controller Design using LMIs under Specified Rate of Packet Dropouts", Applied Mathematics and Computation (Elsevier), vol. 401, pp. 126125, Jul. 2021, doi: https://doi.org/10.1016/j.amc.2021.126125.

31. D.K. Panda, S. Das, "Smart Grid Architecture Model for Control, Optimization and Data Analytics of Future Power Networks with More Renewable Energy". Journal of Cleaner Production (Elsevier), vol. 301, pp. 126877, Jun. 2021, doi: doi.org/10.1016/j.jclepro.2021.126877.

32. D.K. Panda and S. Das, "Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles", Renewable and Sustainable Energy Reviews (Elsevier), vol. 137, Mar. 2021, pp. 110474, doi: https://doi.org/10.1016/j.rser.2020.110474.

33. S. Das, M.P. Hobson, F. Feroz, X. Chen, S. Phadke, J. Goudswaard, and D. Hohl, "Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling", Data-Centric Engineering (Cambridge University Press), vol. 2, pp. e1, Feb. 2021, doi: https://doi.org/10.1017/dce.2021.1 .

34. S. Das, K. Halder, and A. Gupta, "Delay handling method in dominant pole placement based PID controller design", IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 980-991, Feb. 2020, doi: https://doi.org/10.1109/TII.2019.2918252.  

35. K. Halder, S. Das and A. Gupta, "Transformation of LQR weights for discretization invariant performance of PI/PID dominant pole placement controllers", Robotica (Cambridge University Press), vol. 38, no. 2, pp. 271-298, Feb. 2020, doi: https://doi.org/10.1017/S0263574719000596.

36. M. Dutta Choudhury, S. Das, B. Roy, S. Sengupta, S. Tarafdar, and S. Karmakar, “Salt crystal growth in interacting drops of a complex biopolymer: statistical characterization using FESEM images”, Chemical Engineering Science (Elsevier), vol. 207, pp. 1121-1139, Nov. 2019, doi: https://doi.org/10.1016/j.ces.2019.07.037.

37. N. Vemishetty, R.L. Gunukula, A. Acharyya, P.E. Puddu, S. Das, and K. Maharatna, “Phase space reconstruction based CVD classifier using localized features”, Scientific Reports (Nature Research), vol. 9, no. 14593, Oct. 2019, doi: https://doi.org/10.1038/s41598-019-51061-8.

38. X. Chen, M. Hobson, S. Das, and P. Gelderblom, "Improving the efficiency and robustness of nested sampling using posterior repartitioning", Statistics and Computing (Springer), vol. 29, no. 4, pp. 835-850, Jul. 2019, doi: https://doi.org/10.1007/s11222-018-9841-3.

39. S. Das, X. Chen, M.P. Hobson, S. Phadke, B. van Beest, J. Goudswaard, and D. Hohl, “Surrogate regression modelling for fast seismogram generation and detection of microseismic events in heterogeneous velocity models”, Geophysical Journal International (Oxford University Press), vol. 215, no. 2, pp. 1257-1290, Nov. 2018, doi: http://dx.doi.org/10.1093/gji/ggy283.

40. B.A. Cociu, S. Das, L. Billeci, W. Jamal, K. Maharatna, S. Calderoni, A. Narzisi, and F. Muratori, "Multimodal functional and structural brain connectivity analysis in autism: a preliminary integrated approach with EEG, fMRI and DTI", IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 213-226, Jun. 2018, doi: http://dx.doi.org/10.1109/TCDS.2017.2680408.

41. I. Pan and S. Das, "Evolving chaos: Identifying new attractors of the generalised Lorenz family", Applied Mathematical Modelling (Elsevier), vol. 57, pp. 391-405, May 2018, doi: https://doi.org/10.1016/j.apm.2018.01.015.

42. S. Das, K. Halder, and A. Gupta, "Performance analysis of robust stable PID controllers using dominant pole placement for SOPTD process models", Knowledge-Based Systems (Elsevier), vol. 146, pp. 12-43, Apr. 2018, doi: https://doi.org/10.1016/j.knosys.2018.01.030.

43. S.K. Chatterjee, S. Das, K. Maharatna, E. Masi, L. Santopolo, I. Colzi, S. Mancuso and A. Vitaletti, "Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response", Sensors and Actuators B: Chemical (Elsevier), vol. 249, pp. 278-295, Oct. 2017, doi: http://dx.doi.org/10.1016/j.snb.2017.04.071.

44. S. Dutta Sinha, S. Das, S. Tarafdar, and T. Dutta, "Monitoring of wild Pseudomonas biofilm strain conditions using statistical characterization of scanning electron microscopy images", Industrial & Engineering Chemistry Research (ACS Publications), vol. 56, no. 34, pp. 9496-9512, Aug. 2017, doi: http://dx.doi.org/10.1021/acs.iecr.7b01106.

45. S. Das, X. Chen, and M.P. Hobson, "Fast GPU-based seismogram simulation from microseismic events in marine environments using heterogeneous velocity models", IEEE Transactions on Computational Imaging, vol. 3, no. 2, pp. 316-329, Jun. 2017, doi: http://dx.doi.org/10.1109/TCI.2017.2654127.

46. D.S. Pandey, S. Das, I. Pan, J.J. Leahy, and W. Kwapinski, "Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor", Waste Management (Elsevier), vol. 58, pp. 202–213, Dec. 2016, doi: http://dx.doi.org/10.1016/j.wasman.2016.08.023.

47. S. Das, I. Pan, and S. Das, "Effect of random parameter switching on commensurate fractional order chaotic systems", Chaos, Solitons & Fractals (Elsevier), vol. 91, pp. 157-173, Oct. 2016, doi: http://dx.doi.org/10.1016/j.chaos.2016.05.014.

48. I. Pan and S. Das, “Fractional order AGC for distributed energy resources using robust optimization", IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2175-2186, Sep. 2016, doi: http://dx.doi.org/10.1109/TSG.2015.2459766.

49. V. Bono, S. Das, W. Jamal, and K. Maharatna, “Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG”, Journal of Neuroscience Methods (Elsevier), vol. 267, pp. 89-107, Jul. 2016, doi: http://dx.doi.org/10.1016/j.jneumeth.2016.04.006.

50. I. Pan and S. Das, “Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO”, ISA Transactions (Elsevier), vol. 62, pp. 19-29, May. 2016, doi: http://dx.doi.org/10.1016/j.isatra.2015.03.003.

51. S. Das, B.J. Ajiwibawa, S.K. Chatterjee, S. Ghosh, K. Maharatna, S. Dasmahapatra, A. Vitaletti, E. Masi, and S. Mancuso, "Drift removal in plant electrical signals via IIR filtering using wavelet energy", Computers and Electronics in Agriculture (Elsevier), vol. 118, pp. 15-23, Oct. 2015, doi: http://dx.doi.org/10.1016/j.compag.2015.08.013.

52. S. Das, I. Pan, and S. Das, "Multi-objective LQR with optimum weight selection to design FOPID controllers for delayed fractional order processes", ISA Transactions (Elsevier), vol. 58, pp. 35-49, Sep. 2015, doi: http://dx.doi.org/10.1016/j.isatra.2015.06.002.

53. W. Jamal, S. Das, K. Maharatna, I. Pan, and D. Kuyucu, “Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks”, Physica A: Statistical Mechanics and its Applications (Elsevier), vol. 434, pp. 273-295, Sep. 2015, doi: http://dx.doi.org/10.1016/j.physa.2015.03.087.

54. A. Pakhira, S. Das, I. Pan, and S. Das, “Symbolic representation for analog realization of a family of fractional order controller structures via continued fraction expansion”, ISA Transactions (Elsevier), vol. 57, pp. 390-402, Jul. 2015, doi: http://dx.doi.org/10.1016/j.isatra.2015.01.007.

55. I. Pan and S. Das, “When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming”, Chaos, Solitons & Fractals (Elsevier), vol. 76, pp. 141–155, Jul. 2015, doi: http://dx.doi.org/10.1016/j.chaos.2015.03.017.

56. W. Jamal, S. Das, K. Maharatna, F. Apicella, G. Chronaki, F. Sicca, D. Cohen, F. Muratori, "On the existence of synchrostates in multichannel EEG signals during face-perception tasks", Biomedical Physics & Engineering Express (IOP Publishing), vol. 1, no. 1, pp. 015002, Jun. 2015, doi: http://dx.doi.org/10.1088/2057-1976/1/1/015002.

57. I. Pan, S. Das, and A. Routh, “Towards a global controller design for guaranteed synchronization of switched chaotic systems”, Applied Mathematical Modelling (Elsevier), vol. 39, no. 8, pp. 2311-2331, Apr. 2015, doi: http://dx.doi.org/10.1016/j.apm.2014.10.039.

58. I. Pan and S. Das, “Fractional order load-frequency control of interconnected power systems using chaotic multi-objective optimization”, Applied Soft Computing (Elsevier), vol. 29, pp. 328-344, Apr. 2015, doi: http://dx.doi.org/10.1016/j.asoc.2014.12.032.

59. S.K. Chatterjee, S. Das, K. Maharatna, E. Masi, L. Santopolo, S. Mancuso, and A. Vitaletti, “Exploring strategies for classification of external stimuli using statistical features of the plant electrical response”, Journal of the Royal Society Interface, vol. 12, no. 104, pp. 20141225, Mar. 2015, doi: http://dx.doi.org/10.1098/rsif.2014.1225.

60. D.S. Pandey, I. Pan, S. Das, J.J. Leahy, and W. Kwapinski, “Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier”, Bioresource Technology (Elsevier), vol. 179, pp. 524-533, Mar. 2015, doi: http://dx.doi.org/10.1016/j.biortech.2014.12.048.

61. G. Koulaouzidis, S. Das, G. Cappiello, E.B. Mazomenos, K. Maharatna, P.E. Puddu, and J.M. Morgan, “Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG”, International Journal of Cardiology (Elsevier), vol. 182, pp. 38-43, Mar. 2015, doi: http://dx.doi.org/10.1016/j.ijcard.2014.12.067.

62. W. Jamal, S. Das, I.A. Oprescu, and K. Maharatna, “Prediction of synchrostate transitions in EEG signals using Markov chain models”, IEEE Signal Processing Letters, vol. 22, no. 2, pp. 149-152, Feb. 2015, doi: http://dx.doi.org/10.1109/LSP.2014.2352251.

63. I. Pan and S. Das, “Kriging based surrogate modeling for fractional order control of microgrids”, IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 36-44, Jan. 2015, doi: http://dx.doi.org/10.1109/TSG.2014.2336771.

64. G. Cappiello, S. Das, E.B. Mazomenos, K. Maharatna, G. Koulaouzidis, J. Morgan, and P.E. Puddu, “A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits”, Physiological Measurement (IOP Publishing), vol. 36, no. 1, pp. 107-131, Jan. 2015, doi: http://dx.doi.org/10.1088/0967-3334/36/1/107.

65. I. Pan, S. Das, and S. Das, “Multi-objective active control policy design for commensurate and incommensurate fractional order chaotic financial systems”, Applied Mathematical Modelling (Elsevier), vol. 39, no. 2, pp. 500-514, Jan. 2015, doi: http://dx.doi.org/10.1016/j.apm.2014.06.005.

66. S. Das and I. Pan, “On the mixed H2/H∞ loop shaping trade-offs in fractional order control of the AVR system”, IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 1982-1991, Nov. 2014, doi: http://dx.doi.org/10.1109/TII.2014.2322812.

67. W. Jamal, S. Das, I.A. Oprescu, K. Maharatna, F. Apicella, and F. Sicca, “Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates”, Journal of Neural Engineering (IOP Publishing), vol. 11, no. 4, pp. 046019, Aug. 2014, doi: http://dx.doi.org/10.1088/1741-2560/11/4/046019.

68. S.K. Chatterjee, S. Ghosh, S. Das, V. Manzella, A. Vitaletti, E. Masi, L. Santopolo, S. Mancuso, and K. Maharatna, “Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants”, Measurement (Elsevier), vol. 53, pp. 101-116, Jul. 2014, doi: http://dx.doi.org/10.1016/j.measurement.2014.03.040.

69. S. Das, A. Acharya, and I. Pan, “Simulation studies on the design of optimum PID controllers to suppress chaotic oscillations in a family of Lorenz-like multi-wing attractors”, Mathematics and Computers in Simulation (Elsevier), vol. 100, pp. 72-87, Jun. 2014, doi: http://dx.doi.org/10.1016/j.matcom.2014.03.002.

70. G. Koulaouzidis, S. Das, G. Cappiello, E.B. Mazomenos, K. Maharatna, and J. Morgan, “A novel approach for the diagnosis of ventricular tachycardia based on phase space reconstruction of ECG”, International Journal of Cardiology (Elsevier), vol. 172, no. 1, pp. e31-e33, Mar. 2014, doi: http://dx.doi.org/10.1016/j.ijcard.2013.12.088.

71. A. Acharya, S. Das, I. Pan, and S. Das, “Extending the concept of analog Butterworth filter for fractional order systems”, Signal Processing (Elsevier), vol. 94, pp. 409-420, Jan. 2014, doi: http://dx.doi.org/10.1016/j.sigpro.2013.07.012.

72. S. Das and K. Maharatna, “Fractional dynamical model for the generation of ECG like signals from filtered coupled Van-der Pol oscillators”, Computer Methods and Programs in Biomedicine (Elsevier), vol. 112, no. 3, pp. 490-507, Dec. 2013, doi: http://dx.doi.org/10.1016/j.cmpb.2013.08.012.

73. I. Pan, D.S. Pandey, and S. Das, “Global solar irradiation prediction using a multi-gene genetic programming approach”, Journal of Renewable and Sustainable Energy (AIP Publishing), vol. 5, no. 6, pp. 063129, Nov. 2013, doi: http://dx.doi.org/10.1063/1.4850495.

74. I. Pan and S. Das, “Frequency domain design of fractional order PID controller for AVR system using chaotic multi-objective optimization”, International Journal of Electrical Power and Energy Systems (Elsevier), vol. 51, pp. 106-118, Oct. 2013, doi: http://dx.doi.org/10.1016/j.ijepes.2013.02.021.

75. S. Das, I. Pan, and S. Das, “Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time”, ISA Transactions (Elsevier), vol. 52, no. 4, pp. 550-566, Jul. 2013, doi: http://dx.doi.org/10.1016/j.isatra.2013.03.004.

76. I. Pan and S. Das, “Design of hybrid regrouping PSO-GA based sub-optimal networked control systems with random packet losses”, Memetic Computing (Springer), vol. 5, no. 2, pp. 141-153, Jun. 2013, doi: http://dx.doi.org/10.1007/s12293-013-0107-5.

77. S. Das, S. Mukherjee, S. Das, I. Pan, and A. Gupta, “Continuous order identification of PHWR models under step-back for the design of hyper-damped power tracking controller with enhanced reactor safety”, Nuclear Engineering and Design (Elsevier), vol. 257, pp. 109-127, Apr. 2013, doi: http://dx.doi.org/10.1016/j.nucengdes.2013.01.001.

78. S. Das, I. Pan, and S. Das, “Fractional order fuzzy control of nuclear reactor power with thermal-hydraulic effects in the presence of random network induced delay and sensor noise having long range dependence”, Energy Conversion and Management (Elsevier), vol. 68, pp. 200-2018, Apr. 2013, doi: http://dx.doi.org/10.1016/j.enconman.2013.01.003.

79. S. Das, I. Pan, K. Halder, S. Das and A. Gupta, “LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index”, Applied Mathematical Modelling (Elsevier), vol. 37, no. 6, pp. 4253-4268, Mar. 2013, doi: http://dx.doi.org/10.1016/j.apm.2012.09.022.

80. S. Das, S. Das, and I. Pan, “Multi-objective optimization framework for networked predictive controller design”, ISA Transactions (Elsevier), vol. 52, no. 1, pp. 56-77, Jan. 2013, doi: http://dx.doi.org/10.1016/j.isatra.2012.09.004.

81. I. Pan, A. Korre, S. Das, and S. Durucan, “Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise”, Nonlinear Dynamics (Springer),  vol. 70, no. 4, pp. 2445-2461, Dec. 2012, doi: http://dx.doi.org/10.1007/s11071-012-0632-7.

82. I. Pan and S. Das, “Chaotic multi-objective optimization based design of fractional order PIλDμ controller in AVR system”, International Journal of Electrical Power & Energy Systems (Elsevier), vol. 43, no. 1, pp. 393-407, Dec. 2012, doi: http://dx.doi.org/10.1016/j.ijepes.2012.06.034.

83. S. Das, I. Pan, S. Das, and A. Gupta, “Master-slave chaos synchronization via optimal fractional order PIλDμ controller with bacterial foraging algorithm”, Nonlinear Dynamics (Springer), vol. 69, no. 4, pp. 2193-2206, Sep. 2012, doi: http://dx.doi.org/10.1007/s11071-012-0419-x.

84. S. Saha, S. Das, S. Das, and A. Gupta, “A conformal mapping based fractional order approach for sub-optimal tuning of PID controllers with guaranteed dominant pole placement”, Communications in Nonlinear Science and Numerical Simulations (Elsevier), vol. 17, no. 9, pp. 3628-3642, Sep. 2012, doi: http://dx.doi.org/10.1016/j.cnsns.2012.01.007.

85. S. Das, I. Pan, S. Das, and A. Gupta, “Improved model reduction and tuning of fractional order PIλDμ controllers for analytical rule extraction with genetic programming”, ISA Transactions (Elsevier), vol. 51, no. 2, pp. 237-261, Mar. 2012, doi: http://dx.doi.org/10.1016/j.isatra.2011.10.004.

86. S. Das, I. Pan, S. Das, and A. Gupta, “A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices”, Engineering Applications of Artificial Intelligence (Elsevier), vol. 25, no. 2, pp. 430-442, Mar. 2012, doi: http://dx.doi.org/10.1016/j.engappai.2011.10.004.

87. S. Das, S. Das, and A. Gupta, “Fractional order modeling of a PHWR under step-back condition and control of its global power with a robust PIλDμ controller”, IEEE Transactions on Nuclear Science, vol. 58, no. 5, part 2, art. no. 6025228, pp. 2431-2441, Oct. 2011, doi: http://dx.doi.org/10.1109/TNS.2011.2164422.

88. I. Pan, S. Das, and A. Gupta, “Handling packet dropouts and random delays for unstable delayed processes in NCS by optimal tuning of PIλDμ controllers with evolutionary algorithms”, ISA Transactions (Elsevier), vol. 50, no. 4, pp. 557-572, Oct. 2011, doi: http://dx.doi.org/10.1016/j.isatra.2011.04.002.

89. S. Das, S. Saha, S. Das, and A. Gupta, “On the selection of tuning methodology for FOPID controllers for the control of higher order processes”, ISA Transactions (Elsevier), vol. 5, no. 3, pp. 376-388, Jul. 2011, doi: http://dx.doi.org/10.1016/j.isatra.2011.02.003.

90. I. Pan, S. Das, and A. Gupta, “Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay”, ISA Transactions (Elsevier), vol. 50, no. 1, pp. 28-36, Jan. 2011, doi: http://dx.doi.org/10.1016/j.isatra.2010.10.005.

91. S. Saha, S. Das, R. Ghosh, B. Goswami, R. Balasubramanian, A.K. Chandra, S. Das, and A. Gupta, “Design of a fractional order phase shaper for iso-damped control of a PHWR under step-back condition”, IEEE Transactions on Nuclear Science, vol. 57, no. 3, part. 3, art. no. 5485191, pp. 1602-1612, Jun. 2010, doi: http://dx.doi.org/10.1109/TNS.2010.2047405.

92. S. Saha, S. Das, R. Ghosh, B. Goswami, R. Balasubramanian, A.K. Chandra, S. Das, and A. Gupta, “Fractional order phase shaper design with Bode’s integral for iso-damped control system”, ISA Transactions (Elsevier), vol. 49, no. 2, pp. 196-206, Apr. 2010, doi: http://dx.doi.org/10.1016/j.isatra.2009.12.001.

Most articles in the following Journals (>4 papers):

     ISA Transactions (Elsevier)         Applied Mathematical Modelling (Elsevier) Energies (MDPI)

                (10 papers)                                               (4 papers) (5 papers)