An Intelligent Controller Based on LAMDA for Speed Control of a Three-Phase Inductor Motor

Luis A. Morales, Paúl Fabara, David Fernando Pozo


Three-phase induction motors are widely used in the industrial field due to their low cost and robustness; therefore, it is essential to continuously develop new proposals that improve their behavior and response in applications where speed control is required. This paper proposes the development of an intelligent controller programmed in a PLC and interconnected with a three-phase induction motor through a VFD. The novel intelligent controller bases its operation on the LAMDA algorithm, which acts as a decision-making system based on the state of the error with respect to the speed reference and its derivative, obtaining a closed-loop controller. In addition, the VFD receives commands from the PLC to operate the motor at a constant voltage-frequency ratio in which flux remains constant. The proposed controller has been validated in two study cases: i) reference changes and ii) rejection of disturbances. The results obtained are promising and show a good performance of the LAMDA controller when compared qualitatively and quantitatively with the controller most commonly used in industrial systems, such as PID, and controllers with similar characteristics, such as fuzzy, based on Mamdani and Takagi-Sugeno inference.


Doi: 10.28991/ESJ-2023-07-03-01

Full Text: PDF


LAMDA; Intelligent Control; VFD; PLC. Induction Motor.


Mohd Shah, M. H., Rahmat, M. F. ad, Danapalasingam, K. A., & Wahab, N. A. (2013). PLC based adaptive FUZZY PID speed control of DC belt conveyor system. International Journal on Smart Sensing and Intelligent Systems, 6(3), 1133–1152. doi:10.21307/ijssis-2017-583.

Salunkhe, S., & Kalkhambkar, V. N. (2019). VFD Control for Industrial Machines using PLC and LC Filter. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019, 1, 1653–1658. doi:10.1109/ICICICT46008.2019.8993387.

Bocker, J., & Mathapati, S. (2007). State of the Art of Induction Motor Control. 2007 IEEE International Electric Machines & Drives Conference. doi:10.1109/iemdc.2007.383643.

Mencou, S., Ben Yakhlef, M., & Tazi, E. B. (2022). Advanced Torque and Speed Control Techniques for Induction Motor Drives: A Review. 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). doi:10.1109/iraset52964.2022.9738368.

Jesus Bobin, V., & Marsaline Beno, M. (2023). Performance Analysis of Optimization Based FOC and DTC Methods for Three Phase Induction Motor. Intelligent Automation & Soft Computing, 35(2), 2493–2511. doi:10.32604/iasc.2023.024679.

Kushwaha, A. K., & Sharma, A. K. (2022). Direct Torque Control Based Induction Machines for Speed-Torque Regulation. 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India. doi:10.1109/icaccs54159.2022.9785361.

Jankowska, K., & Dybkowski, M. (2022). Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Cri Markers in the PMSM Drive System. Applied Sciences (Switzerland), 12(19). doi:10.3390/app12199405.

Gholipour, A., Ghanbari, M., Alibeiki, E., & Jannati, M. (2023). Sensorless FOC Strategy for Current Sensor Faults in Three-Phase Induction Motor Drives. Journal of Operation and Automation in Power Engineering, 11(1), 1–10. doi:10.22098/JOAPE.2022.9274.1648.

Vu, Q. S., Tran, C. D., Dinh, B. H., Dong, C. S. T., Huynh, H. T., & Phan, H. X. (2022). A current sensor fault diagnosis method based on phase angle shift technique applying to induction motor drive. International Journal of Power Electronics and Drive Systems, 13(3), 1315–1325. doi:10.11591/ijpeds.v13.i3.pp1315-1325.

Hadla, H., & Santos, F. (2022). Performance Comparison of Field-oriented Control, Direct Torque Control, and Model-predictive Control for SynRMs. Chinese Journal of Electrical Engineering, 8(1), 24–37. doi:10.23919/CJEE.2022.000003.

Elgbaily, M., Anayi, F., & Alshbib, M. M. (2022). A Combined Control Scheme of Direct Torque Control and Field-Oriented Control Algorithms for Three-Phase Induction Motor: Experimental Validation. Mathematics, 10(20), 3842. doi:10.3390/math10203842.

Nahavandi, R., Asadi, M., & Torkashvand, A. (2022). A SOSM Control for Induction Motor Using ANN-based Sensorless Speed and Flux Estimation under Parametric Uncertainty in FOC Control Method. 2022 13th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), Tehran, Iran. doi:10.1109/pedstc53976.2022.9767386.

Djelamda, I., & Bochareb, I. (2022). Field-oriented control based on adaptive neuro-fuzzy inference system for PMSM dedicated to electric vehicle. Bulletin of Electrical Engineering and Informatics, 11(4), 1892–1901. doi:10.11591/eei.v11i4.3818.

Gupta, S., & Sharma, S. C. (2005). Selection and application of advance control systems: PLC, DCS and PC-based system. Journal of Scientific and Industrial Research, 64(4), 249–255.

Alphonsus, E. R., & Abdullah, M. O. (2016). A review on the applications of programmable logic controllers (PLCs). Renewable and Sustainable Energy Reviews, 60, 1185–1205. doi:10.1016/j.rser.2016.01.025.

Li, Y., Zhou, J., Li, Q., He, L., Zhang, Y., Song, S. (2020). The Design of an Intelligent Screw Extruder Control System Based on Fuzzy Control. Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, 594, Springer, Singapore. doi:10.1007/978-981-32-9698-5_30.

Li, Y., Zhang, K., & Zhu, Y. (2018). Fuzzy Control of Welding Trolley Speed Based on PLC. 2018 37th Chinese Control Conference (CCC). doi:10.23919/chicc.2018.8483972.

Baldovino, R. G., & Dadios, E. P. (2016). A hybrid fuzzy logic-PLC-based controller for earthquake simulator system. Journal of Advanced Computational Intelligence and Intelligent Informatics, 20(1), 100–105. doi:10.20965/jaciii.2016.p0100.

Zhong, L., Yongkang, C., Lepeng, S., Junlin, Z., & Huabin, W. (2018). A novel control system design for automatic feed drilling operation of the PLC-based oil rig. Vibroengineering Procedia, 18, 176–182. doi:10.21595/vp.2018.19786.

Guo, X., Song, L., Huang, J., & Xiong, F. (2019). Automatic Walnut Sorting System Based on Adaptive Fuzzy Control. 2019 6th International Conference on Information Science and Control Engineering (ICISCE). doi:10.1109/icisce48695.2019.00141.

Khrulkov, V., Vasilchenko, S., & Cherny, S. (2020). Method of Implementing a Fuzzy Logic Controller by Hardware. 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), Vladivostok, Russia. doi:10.1109/fareastcon50210.2020.9271539.

Kocian, J., Koziorek, J., Pokorny, M. (2011). An Approach to PLC-Based Fuzzy Expert System in PID Control. Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 252. Springer, Berlin, Germany. doi:10.1007/978-3-642-25453-6_18.

Graham, A. M., & Etezadi-Amoli, M. (2000). Design, implementation, and simulation of a PLC based speed controller using fuzzy logic. 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134), Seattle, WA, USA. doi:10.1109/pess.2000.867380.

Han, J. B., Liu, S. Z., Li, P., Yao, Z. Y., & Mi, J. C. (2014). A PLC-Based Tension Training System Design. Advanced Materials Research, 912–914, 563–565. doi:10.4028/

Salkic, A., Muhovic, H., & Jokic, D. (2022). Siemens S7-1200 PLC DC Motor control capabilities. IFAC-Papers Online, 55(4), 103–108. doi:10.1016/j.ifacol.2022.06.017.

Al-Manfi, A. A. (2018). Implementation of Remote Self-Tuning Fuzzy PID Controller for Induction Motor through Ethernet. Proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia, 12-17.

Giannoutsos, S. V., & Manias, S. N. (2014). A systematic power-quality assessment and harmonic filter design methodology for variable-frequency drive application in marine vessels. IEEE Transactions on industry applications, 51(2), 1909-1919. doi:10.1109/TIA.2014.2347453.

Khudier, K. H., Mohammed, K. G., & Ibrahim, M. S. (2021, February). Design and Implementation of Constant Speed control System for the Induction motors Using Programmable logic Controller (PLC) and Variable Frequency Derive (VFD). IOP Conference Series: Materials Science and Engineering 1076, 012007. doi:10.1088/1757-899X/1076/1/012007.

Ordoñez Avila, J. L., & Portillo, E. S. (2020). Diseño e Implementación de un Controlador Difuso para Control de Frecuencia de un Motor en un PLC S7-1200. Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integration, And Alliances for A Sustainable Development” “Hemispheric Cooperation for Competitiveness and Prosperity on A Knowledge-Based Economy.” doi:10.18687/laccei2020.1.1.490.

Aguilar-Martin, J., & De Mantaras, R. L. (1982). The process of classification and learning the meaning of linguistic descriptors of concepts. Approximate reasoning in decision analysis, 1982, 165-175.

Hernandez, H. R., Camas, J. L., Medina, A., Perez, M., & Le Lann, M. V. (2014). Fault diagnosis by LAMDA methodology applied to drinking water plant. IEEE Latin America Transactions, 12(6), 985–990. doi:10.1109/TLA.2014.6893990.

Mora-Florez, J., Barrera-Núñez, V., & Carrillo-Caicedo, G. (2007). Fault location in power distribution systems using a learning algorithm for multivariable data analysis. IEEE Transactions on Power Delivery, 22(3), 1715–1721. doi:10.1109/TPWRD.2006.883021.

Ruiz, F. A., Isaza, C. V., Agudelo, A. F., & Agudelo, J. R. (2017). A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines. Engineering Applications of Artificial Intelligence, 60, 117–127. doi:10.1016/j.engappai.2017.02.005.

Morales, L., Aguilar, J., Chávez, D., & Isaza, C. (2020). LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning. International Journal of Information Technology & Decision Making, 19(01), 283–316. doi:10.1142/s0219622019500457.

Morales, L., & Aguilar, J. (2020). An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA. IEEE Access, 8, 162917–162944. doi:10.1109/access.2020.3021675.

Bedoya, C., Waissman Villanova, J., Isaza Narvaez, C.V. (2014). Yager–Rybalov Triple Π Operator as a Means of Reducing the Number of Generated Clusters in Unsupervised Anuran Vocalization Recognition. Nature-Inspired Computation and Machine Learning. MICAI 2014, Lecture Notes in Computer Science, 8857. Springer, Cham, Switzerland. doi:10.1007/978-3-319-13650-9_34.

Morales, L., Pozo-Espín, D., Aguilar, J., & R-Moreno, M. D. (2022). Approaches based on LAMDA control applied to regulate HVAC systems for buildings. Journal of Process Control, 116, 34–52. doi:10.1016/j.jprocont.2022.05.013.

Morales Escobar, L., Aguilar, J., Garces-Jimenez, A., Gutierrez De Mesa, J. A., & Gomez-Pulido, J. M. (2020). Advanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings. IEEE Access, 8, 16111–16126. doi:10.1109/access.2020.2966545.

Morales, L., Aguilar, J., Camacho, O., & Rosales, A. (2021). An intelligent sliding mode controller based on LAMDA for a class of SISO uncertain systems. Information Sciences, 567, 75–99. doi:10.1016/j.ins.2021.03.012.

Morales, L., Aguilar, J., Rosales, A., Chávez, D., & Leica, P. (2020). Modeling and control of nonlinear systems using an Adaptive LAMDA approach. Applied Soft Computing, 95, 106571. doi:10.1016/j.asoc.2020.106571.

Gull, C. Q., Aguilar, J., & R-Moreno, M. D. (2022, July). A Multi-label Approach for Diagnosis Problems in Energy Systems using LAMDA algorithm. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy. doi:10.1109/FUZZ-IEEE55066.2022.9882828.

Kempowsky, T., Aguilar, J., Subias, A., & Le Lann, M.-V. (2003). Classification Tool Based on Interactivity Between Expertise and Self-Learning Techniques 1. IFAC Proceedings Volumes, 36(5), 675–680. doi:10.1016/s1474-6670(17)36570-9.

Morales, L., Aguilar, J., Rosales, A., Mesa, J. A. G. de, & Chavez, D. (2019). An Intelligent Controller based on LAMDA. IEEE 4th Colombian Conference on Automatic Control (CCAC), 15-18 October 2019, Medellin, Colombia. doi:10.1109/ccac.2019.8921299

Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116–132. doi:10.1109/tsmc.1985.6313399.

Bala Abhirami, M., & Thirunavukkarasu, I. (2023). Maximum Sensitivity-Based PID Controller for a Lab-Scale Batch Reactor. In Smart Sensors Measurement and Instrumentation: Select Proceedings of CISCON 2021, 183-194. Singapore: Springer Nature Singapore. doi:10.1007/978-981-19-6913-3_12.

Smith, C. A., & Corripio, A. B. (2005). Principles and practices of automatic process control. John Wiley & sons, Hoboken, United States.

Full Text: PDF

DOI: 10.28991/ESJ-2023-07-03-01


  • There are currently no refbacks.

Copyright (c) 2023 Luis Morales, Paúl Fabara, David Fernando Pozo