MOPSO-based predictive control strategy for efficient operation of sensorless vector-controlled fuel cell electric vehicle induction motor drives
MOPSO-based predictive control strategy for efficient operation of sensorless vector-controlled fuel cell electric vehicle induction motor drives
This paper introduces an optimal control strategy of model-based predictive control (MPC) based on multiobjective particle swarm optimization (MOPSO) for a sensorless vector control induction motor, which is used in a fuel cell electric vehicle drive system. The proposed MPC-MOPSO algorithm is implemented to tune the weighting parameters of the MPC controller to tackle all the conflicting objective functions. The paper handles the following fitness functions: minimizing the speed error, minimizing the torque ripple, minimizing the DC-link voltage ripple, and minimizing machine flux ripple. Computer simulations studies have been completed utilizing MATLAB/Simulink with a specific end goal of assessing the dynamic performance of the proposed MPC-MOPSO optimal controller and comparing it with single-objective particle swarm optimization and traditional PI controllers. The simulation results demonstrate the good dynamic response of the proposed MPC-MOPSO optimal tuning strategy over the traditional PI controllers for more accurate tracking performance through the whole speed range, especially at starting conditions and load change disturbances.
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- [1] Abdallah T, Mamadou BC, Brayima D, Yacine A. DC/DC and DC/AC converters control for hybrid electric vehicles energy management: ultracapacitors and fuel cell. IEEE T Ind Inform 2013; 9: 686-696.
- [2] Thounthong P, Pierfederici S, Martin JP, Hinaje M, Davat B. Modeling and control of fuel cell/supercapacitor hybrid source based on differential flatness control. IEEE T Veh Technol 2010; 59: 2700-2710.
- [3] Camara MB, Dakyo B, Gualous H. Supercapacitors and battery energy management based on new European driving cycle. Journal of Energy and Power Engineering 2012; 6: 168-177.
- [4] Ahmed ZD, Vladimir P. Vector controlled induction motor drive based on model predictive control. In: 11th International Conference on Actual Problems of Electronics Instrument Engineering; 24 October 2012; Novosibirsk, Russia. pp. 167-173.
- [5] Jose T, Jayendra T, Kamalesh H, Krishna V. An improved scheme for extended power loss ride-through in a voltagesource-inverter-fed vector-controlled induction motor drive using a loss minimization technique. IEEE T Ind Appl 2016; 52: 1500-1508.
- [6] Jaroslaw G, Haitham A. Speed sensorless induction motor drive with predictive current controller. IEEE T Ind Electron 2013; 60: 699-709.
- [7] Andrew S, Shady G, John F. Improved rotor flux estimation at low speeds for torque MRAS-based sensorless induction motor drives. IEEE T Energy Conver 2016; 31: 270-282.
- [8] Yaman Z, Shady G, David A. Model predictive MRAS estimator for sensorless induction motor drives. IEEE T Ind Electron 2016; 63: 3511-3521.
- [9] Xiaodong S, Long C, Zebin Y, Huangqiu Z. speed-sensorless vector control of a bearingless induction motor with artificial neural network inverse speed observer. IEEE-ASME T Mech 2013; 18: 1357-1366.
- [10] Francesco A, Tommaso C, Filippo D, Adriano F, Antonino S. Convergence analysis of extended Kalman filter for sensorless control on induction motor. IEEE T Ind Electron 2015; 62: 2341-2352.
- [11] Habibullah MD, Dylan DL. A speed-sensorless FS-PTC of induction motors using extended Kalman filters. IEEE T Ind Electron 2015; 62: 6765-6778.
- [12] Mousavi-Aghdam SR, Sharifian MBB. Nonlinear adaptive observer for sensorless control of induction motor. In: 20th Iranian Conference on Electrical Engineering; 2012: Tehran, Iran. pp. 376-379.
- [13] Yung-Chang L, Chen-Lung T, Wen-Cheng P, Cheng-Tao T. Full-order stator flux observer based sensorless vector controlled induction motor drives applying particle swarm optimization algorithm. In: International Symposium on Computer, Consumer and Control; 46 July 2016; Xian, China. pp. 899-902.
- [14] Patel C, Ramchand R, Sivakumar K, Das A, Gopakumar K. A rotor flux estimation during zero and active vector periods using current error space vector from a hysteresis controller for a sensorless vector control of IM drive. IEEE T Ind Electron 2011; 58: 2334-2344.
- [15] Lascu C, Boldea I, Blaabjerg F. A class of speed-sensorless sliding mode observers for high-performance induction motor drives. IEEE T Ind Electron 2009; 56: 3394-3403.
- [16] Zaky MS. Stability analysis of speed and stator resistance estimators for sensorless induction motor drives. IEEE T Ind Electron 2012; 59: 858-870.
- [17] Boglietti A, Cavagnino A, Lazzari M. Computational algorithms for induction-motor equivalent circuit parameter determinationPart I: Resistances and leakage reactances. IEEE T Ind Electron 2011; 58: 3723-3733.
- [18] Ko J, Choi J, Chung D. Hybrid artificial intelligent control for speed control of induction motor. In: International Joint Conference SICE-ICASE; 1821 October 2006; Busan, South Korea. pp. 678-683.
- [19] Ji ZC, Shen YX. Back-stepping position control for induction motor based on neural network. In: 1st IEEE Conference on Industrial Electronics and Applications; 2426 May 2006; Singapore. New York, NY, USA: IEEE. pp. 1-5.
- [20] Lin FJ, Shieh HJ, Shyu KK, Huang PK. On-line gain tuning IP controller using real coded genetic algorithm. Electr Pow Syst Res 2004; 72: 157-169.
- [21] Khalil M, Ahmed E, Mohamed R. Model predictive control using FPGA. International Journal of Control Theory and Computer Modeling 2015; 5: 1-14.
- [22] Sivakumar R, Shennes M. Design and development of model predictive controller for binary distillation column. International Journal of Science and Research 2014; 5: 445-451.
- [23] Holkar K, Waghmare L. An overview of model predictive control. International Journal of Control and Automation 2010; 3: 47-63.
- [24] Thomsen S, Hoffmann N, Fuchs F. PI control, PI-Based state space control and model-based predictive control for drive systems with elastically coupled loads-a comparative study. IEEE T Ind Electron 2011; 58: 3647-3657.
- [25] Mapok K, Zuva T, Masebu H, Zuva K. Performance comparison of two controllers on a nonlinear system. International Journal of Chaos, Control, Modelling and Simulation 2013; 2: 17-30.
- [26] Bayoumi EHE, Awadallah MA, Soliman HM. Deadbeat performance of vector-controlled induction motor drives using particle swarm optimization and adaptive neuro-fuzzy inference systems. Electro-motion Scientific Journal 2011; 18: 231-242.
- [27] Ryohei S, Fukiko K, Hideyuki I, Chikashi N, Yoshikazu F, Eitaro A. Automatic tuning of model predictive control using particle swarm optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium; 2007. New York, NY, USA: IEEE. pp. 221-226.
- [28] Sofiane B, Mohammed C, Fouad A, Salim F. A new approach for fuzzy predictive adaptive controller design using particle swarm optimization algorithm. Int J Innov Comput I 2013; 9: 3741-3758.
- [29] Ngatchou P, Zarei A, El-Sharkawi A. Pareto multi objective optimization. In: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems; 610 November 2005. pp. 84 -91.
- [30] Berizzi A, Innorta M, Marannino P. Multiobjective optimization techniques applied to modern power systems. In: IEEE Power Engineering Society Winter Meeting; 2001. New York, NY, USA: IEEE. pp. 1503-1508.
- [31] Coello CA, Lechuga MS. MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002. Congress on Evolutionary Computation, Part of the 2002 IEEE World Congress on Computational Intelligence; May 2002. New York, NY, USA: IEEE. pp. 1051-1056.
- [32] Jihane K, Mohamed C. Optimization of hybrid renewable energy power systems using evolutionary algorithms. In: 5th International Conference on Systems and Control; 2527 May 2016; Marrakesh, Morocco. pp. 383-388.
- [33] Fawzan S, Mohamed A. Model predictive control for deadbeat performance of induction motor drives. WSEAS Transactions on Circuits and Systems 2015; 14: 304-312.
- [34] Finn H. Comparing PI tuning methods in a real benchmark temperature control system. Norwegian Society of Automatic Control, Modeling, Identification and Control 2010; 31: 79-91.