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MRAC Based PID Controller Design with Genetic Algorithm for a Single Joint Robot Arm


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DOI: https://doi.org/10.15866/irea.v9i2.19863

Abstract


The Nonlinearity of the actuators in dynamical systems leads to bad performance of the conventional feedback controllers in online execution. Furthermore, the closed loop controllers may be subjected to the changes in the environmental conditions and fluctuation of disturbances. In order to tackle this issue, this paper depicts a PID controller design based on a Model as a Reference with Genetic Adaptive Control, which is presently named (MRGAC) arrangement via employing the MIT rule in order to implement the adaption mechanism. In this abovementioned rule (MIT) the cost function is predefined as an optimization function that indicates the inaccuracy between the outputs of the working plant and the reference model used. The constraints of the controller are adjusted in a way so that this cost function is reduced to the minimum. The goal behind this work is to increase the stability of the DC motor angle that positions a single joint robot arm in free space by defining a reference model to mimic and track its response performance parameters with acceptable values. MIT rule is used for the Model Reference that is already stable and has known parameters. The contribution of this work is to adopt the gradient (MIT) with genetic algorithm (GA) that is auto tune the controller’s factors. Adaption of PID using of the optimization tactics, which is Genetic algorithm, will improve the system performance to reach steady state rapidly.
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Keywords


Adaptive Control; PID Controller; Genetic Algorithm (GA); Robot Arm; Model Reference Genetic Adaptive Control (MRGAC)

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References


C. Cunha, A. Araujo, D. Barbalho and F. Mota, A Dual-Mode Adaptive Robust Controller Applied To The Speed Control Of A Three-Phase Induction Motor, Asian Journal of Control, vol. 7, no. 2, pp. 197-201, 2008.
https://doi.org/10.1111/j.1934-6093.2005.tb00389.x

Sapiee, M. R., H. Selamat, A. Noordin, and A. N. Jahari. PI Controller design using model reference adaptive control approaches for a chemical process. In Proceedings of 2008 Student Conference on Research and Development (SCOReD 2008), pp. 155-1. 2008.

A. Suresh kumar, M. Subba Rao, Y.S.K. Babu, Model reference linear adaptive control of dc motor using fuzzy controller, Proceeding of the IEEE/TECON, pp.1-5, Hyderabad, Nov. 2008.
https://doi.org/10.1109/tencon.2008.4766484

R. Guo, J. Chen, X. Hao. Position servo control of a DC electromotor using a hybrid method based on model reference adaptive control (MRAC), Proceeding of the IEEE/CMCE, Vol. 4, pp. 534-537, Aug. 2010.
https://doi.org/10.1109/cmce.2010.5610092

Ibrahim K. Mohammed, Abdulla I. Abdulla, Jamal M. Ahmed Speed Control of DC Motor using MRAC and Genetic Algorithm based PID Controller, International Journal of Industrial Electronics and Electrical Engineering (IJIEEE) , Volume-8,Issue-1 (Jan, 2020).

Al-Awad, Nasir Ahmad. Model Reference Adaptive Control-Based Genetic Algorithm Design For Heading Ship Motion. Acta Polytechnica 60, no. 3 (2020): 197-205.
https://doi.org/10.14311/ap.2020.60.0197

Khather, S.I., Almaged, M. and Abdullah, A.I., 2018. Fractional order based on genetic algorithm PID controller for controlling the speed of DC motors. International Journal of Engineering & Technology, 7(4), pp.5386-5392.
https://doi.org/10.14419/ijet.v7i4.25601

Iskender, S. C. I., & Ariani, H. A. Comparing Control Performances Of Mrac And Pid Applied On A Brushless Dc Motor, (2020) International Journal on “Technical and Physical Problems of Engineering” (IJTPE), 44 (12), pp.10-15.

Chaiyot, R., Kinnares, V., Sensorless Speed Vector Control Based on MRAS of Asymmetrical Two-Phase Induction Motor, (2020) International Review of Electrical Engineering (IREE), 15 (2), pp. 134-146.
https://doi.org/10.15866/iree.v15i2.17278

Chacko, S., Bhende, C., Jain, S., Nema, R., Model Reference Adaptive Controller Based Rotor Resistance Estimation for Vector Controlled Induction Motor Drive Using Artificial Intelligence, (2015) International Review on Modelling and Simulations (IREMOS), 8 (4), pp. 418-426.
https://doi.org/10.15866/iremos.v8i4.7038

R. Thusoo, S. Jain and S. Bangia, PID and MRAC control of a Quadrotor, 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), NOIDA, India, 2019, pp. 197-201.
https://doi.org/10.1109/rdcape47089.2019.8979106

A. A. Ponomarev, Y. I. Kudinov, F. F. Pashchenko and E. S. Duvanov, Analysis and Synthesis of Adaptive PID Controller with MRAC-MIT System, 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, 2020, pp. 527-532.
https://doi.org/10.1109/summa50634.2020.9280651

H. Ahmadian, H. A. Talebi and I. Sharifi, L1 Adaptive Controller Design for Single-Link Flexible Joint Manipulator with Fuzzy-PID Filter, 2020 28th Iranian Conference on Electrical Engineering (ICEE), Tabriz, Iran, 2020, pp. 1-6.
https://doi.org/10.1109/icee50131.2020.9261009

J. Na, J. Yang and G. Gao, Reinforcing Transient Response of Adaptive Control Systems Using Modified Command and Reference Model, in IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 3, pp. 2005-2017, June 2020.
https://doi.org/10.1109/taes.2019.2939612

M. Kumar and B. Tyagi, Design of A Model Reference Adaptive Controller (MRAC) for DC-DC Boost Converter for Variations in Solar Outputs using modified MIT Rule in an Islanded Microgrid, 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), Cochin, India, 2020, pp. 1-6.
https://doi.org/10.1109/pesgre45664.2020.9070679

A. Goel, R. Kumar and S. Narayan, Design of MRAC augmented with PID controller using genetic algorithm, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, 2016, pp. 1-5.
https://doi.org/10.1109/icpeices.2016.7853149

Mohammed, I. K., & Abdulla, A. I. (2020). Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(5).
https://doi.org/10.12928/telkomnika.v18i5.14717

Mohammed, Ibrahim K., and Abdulla I. Abdulla. Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid control system by GA-LQR based PID controller. International Journal of Electrical & Computer Engineering (2088-8708) 10 (2020).
https://doi.org/10.11591/ijece.v10i2.pp1868-1884

Majed D. Youns , Salih M. Attya , Abdulla I. Abdulla Position Control Of Robot Arm Using Genetic Algorithm Based PID Controller Al-Rafidain Engineering Vol.21 No. 6 December 2013.
https://doi.org/10.33899/rengj.2013.82374

Kalpesh B Pathak,Dipak M Adhyaru Survey of Model reference adaptive control IEEE international conference on engineering December 2015.
https://doi.org/10.1109/NUICONE.2012.6493284

Sherin A Kochummen,Dr.N E Jaffar,Prof.Nasar A Model reference adaptive controller designs of steam turbine speed based on MIT rule IEEE international conference on control communication and computing India November 2015.
https://doi.org/10.1109/iccc.2015.7432861

Maheswary Mohan & Prof. Priyanka CP A Model Reference Adaptive PI Controller for the Speed Control of Three Phase Induction Motor, International Journal of Engineering Research & Technology (IJERT), Vol. 5 Issue 07, July-2016.
https://doi.org/10.17577/ijertv5is070203

Santosh Kumar Suman, Vinod Kumar Giri Genetic Algorithms Techniques Based Optimal PID Tuning For Speed Control of DC Motor, American Journal of Engineering and Technology Management, 1(4): 59-64, 2016.
https://doi.org/10.1109/icetech.2016.7569318

Alharbi, W. N. H., and Barry Gomm. Genetic Algorithm Optimisation of PID Controllers for a Multivariable Process. International Journal of Recent Contributions from Engineering, Science & IT (iJES) 5, no. 1 (2017): 77-96.
https://doi.org/10.3991/ijes.v5i1.6692

Elzaghir, Walid, Yi Zhang, Narasimhamurthi Natarajan, Frank Massey, and Chunting Chris Mi. Model reference adaptive control for hybrid electric vehicle with dual clutch transmission configurations. IEEE Transactions on Vehicular Technology 67, no. 2 (2017): 991-999.
https://doi.org/10.1109/tvt.2017.2751543

Jaber, A., Mohammed, K., Shalash, N., Optimization of Electrical Power Systems Using Hybrid PSO-GA Computational Algorithm: a Review, (2020) International Review of Electrical Engineering (IREE), 15 (6), pp. 502-511.
https://doi.org/10.15866/iree.v15i6.18599

Ismagilov, F., Vavilov, V., Urazbakhtin, R., Optimization of Synchronous Electric Motors with Asynchronous Start by Genetic Algorithms, (2018) International Review of Aerospace Engineering (IREASE), 11 (2), pp. 66-75.
https://doi.org/10.15866/irease.v11i2.13365

Mendjel, D., Bekkouche, S., Messast, S., Boukhatem, G., Analysis of Uncertainty Effect on Slopes Reliability by Genetic Algorithm Method, (2018) International Review of Civil Engineering (IRECE), 9 (6), pp. 234-240.
https://doi.org/10.15866/irece.v9i6.16241

Torres, C., Suárez F., C., Ramos M., L., Puerto L., G., Synthesis of Asymmetric Radiation Patterns with Non-Uniform Linear Arrays Using Evolutionary Algorithms, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (6), pp. 408-418.
https://doi.org/10.15866/irecap.v10i6.19377


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