Abstract
With the development of high-speed microprocessors, it is now possible to implement mathematically complex vector control algorithms without compromising on the performance of motor drive. Among vector control techniques space vector proportional-integral (PI), direct-torque control (DTC), field-oriented control (FOC), model-predictive control (MPC) are being widely used in industries. But their limitations have urged researchers to develop more advance techniques. In this paper, a new technique learning and adaptive model - based predictive control (termed as LAMPC) is proposed for the vector control of three phase induction motor. In the proposed method, the dynamic model of induction motor is updated adaptively based on prediction (receding horizon principle) for the inner control loop (current control) while the brain emotional learning-based intelligent controller (BELIC) is used for the outer control loop (speed control). The proposed methodology offers desired dynamic response, precise tracking, good disturbance handling capability along with satisfactory steady-state performance. To show the effectiveness of the proposed approach, benchmark simulation results for various inputs are presented using MATLAB/Simulink. Finally, the detailed qualitative and quantitative comparison of the proposed LAMPC is made with the most relevant vector techniques to show its significance.
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Abbreviations
- Ψs, Ψr :
-
Stator and rotor flux (space vector representation)
- θ e, θ s :
-
Electrical and synchronous angle
- ω e, ω s, ω r :
-
Electrical, slip and rotor angular speed
- V sd, V sq :
-
‘d’ and ‘q’ component of stator voltage in dq-frame
- i sd, i sq :
-
‘d’ and ‘q’ component of rotor current in dq-frame
- Ψrd, Ψrq :
-
‘d’ and ‘q’ component of rotor flux in dq-frame
- Ψrα, Ψrβ :
-
‘α’ and ‘β’ component of rotor flux in αβ-frame
- T e :
-
Electromagnetic torque
- K osp, K osi :
-
Outer speed loop proportional and integral gain
- K ifp, K ifi :
-
Inner loop flux control proportional and integral gain
- K itp, K iti :
-
Inner loop torque control proportional and integral gain
- k Ψ, k T :
-
Weighting factor for stator flux and torque
- α,β :
-
Amygdala and orbitofrontal cortex learning rate constant
- p, q :
-
Weighing co-efficient for state and actuation matrices
- T s :
-
Sampling time
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Muhammad Affan received his B.E. degree in electrical engineering from the Department of Electrical Engineering at NED University of Engineering and Technology, Karachi, Pakistan. His current research interests include model-based and model-free control design for electrical drives and robotic applications.
Riaz Uddin received his B.E. and M.E. degrees in electrical engineering from the Department of Electrical Engineering at NED University of Engineering and Technology, Karachi, Pakistan, in 2005 and 2008, respectively. He received his Ph.D. degree from the School of Mechatronics, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea, in 2016. He joined NED as a lecturer in 2005 and now he is working as an Assistant Professor in the Department of Electrical Engineering & Director of the Office of Reseach, Innovation and Commercialization in NED University of Engineering and Technology. He is also the PI/Director of Haptics, Human-Robotics and Condition Monitoring Lab affiliated Lab of National Center of Robotics and Automation (NCRA), HEC/PC, Pakistan. He recently developed an indigenous ICU-ventilator during COVID-19 pandemic. His research interests include control systems, automation, instrumentation, smart systems, computer networked Systems, robotics, haptics and teleoperation.
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Affan, M., Uddin, R. Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology. Int. J. Control Autom. Syst. 19, 3122–3135 (2021). https://doi.org/10.1007/s12555-020-0306-z
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DOI: https://doi.org/10.1007/s12555-020-0306-z