Optimization of PI Controller on Level Control of Hopper Tank System with PSO Technique
How to Cite?
Vinothkumar C, Esakkiappan C, "Optimization of PI Controller on Level Control of Hopper Tank System with PSO Technique," International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 178-185, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I10P223
Abstract
Regulating the usage of hazardous chemicals in the manufacturing of medicines is critical in industries. Therefore it is required to regulate the level in the nonlinear tank and to avoid the wastage of chemicals used for its preparation. The research deals with the control of the level for a nonlinear hopper process tank. The main benefit of the hopper tank system is that it can able to store more quantity and also provides an easy flow of materials. To provide a better performance analysis, Fractional-Order PI (FOPI) is compared with the Particle Swarm Optimization method to optimize the PI controller parameters like Kp & Ki. The Optimized PI controller gain parameters provide the fastest settling time and reduce error using performance indices. The servo and regulatory responses were analyzed with different individual region-based control and combined region-based control of hopper tank system to achieve minimized settling time and minimized ISE, IAE & ITAE error values of process response.
Keywords
Nonlinear Hopper Tank process, Fractional Order PI control, Particle Swarm Optimization, Servo- Regulatory response, Multi-Region Model.
Reference
[1] Suresh Manic Kesavan, T V N Padmesh, Chan Woei Shyan, Controller Tuning for Nonlinear Hopper Process Tank – A Real Time Analysis, Journal of Engineering Science and Technology. Special Issue (2014) 59-67.
[2] D.Marshiana and P.Thirusakthimurugan, Fractional order PI controller for nonlinear systems, International Conference on Control Instrumentation Communication and Computational Technologies. (2014) 322-326.
[3] G.Saravanakumar, S.Dinesh, S.Preteep & P.Sridhar, Controller tuning method for non-linear conical tank system, Asian Journal of Applied Science and Technology. 1(2) (2017) 224-228.
[4] Sarif. B. Mabu, DV Ashok Kumar, and M. Venu Gopala Rao, Comparison Study of PID Controller Tuning using Classical/Analytical Methods, International Journal of Applied Engineering Research. 13(8) (2018) 5618-5625.
[5] M. Venkatesan and V. R. Ravi, Sliding mode observer based sliding mode controller for interacting nonlinear system, Second International Conference on Current Trends In Engineering and Technology, IEEE-33344. (2014) 1-6.
[6] V. R. Ravi and T. Thyagarajan, A decentralized PID controller for interacting non linear systems, IEEE International Conference on Emerging Trends in Electrical and Computer Technology. (2011) 297-302.
[7] V. R. Ravi and T. Thyagarajan, Application of adaptive control technique to interacting Non Linear Systems, IEEE 3rd International Conference on Electronics Computer Technology. (2011) 386-392.
[8] V. R. Ravi, T. Thyagarajan and M. Monika Darshini, A Multiple Model Adaptive Control Strategy for Model Predictive Controller for Interacting Non Linear Systems, International Conference on Process Automation, Control and Computing, (2011) 1-8.
[9] Ravi.V. R., and T. Thyagarajan, Adaptive decentralized PI controller for two conical tank interacting level system, Arabian Journal for Science and Engineering. 39 (2014) 8433-8451.
[10] D. Marshiana and Thirusakthimurugan.P, Design of Deadbeat Algorithm for a Nonlinear Conical tank system, Procedia Computer Science. 57 (2015) 1351-1358.
[11] D. Marshiana and Thirusakthimurugan. P, Control of level in chemical industry for a nonlinear conical tank process, Research Journal of Pharmaceutical Biological and Chemical Sciences. 6(3) (2015) 1322-1328.
[12] K. Suresh Manic et al., Soft computing Approach to design PID controller for Tank Liquid level control problem, Journal of Engineering Science and Technology. Special Issue (2015) 82-97.
[13] V. Murugananthan, M. Valluvan, and G. Sakthivel, Level Control of Hopper Tank Process Using Model-Based Controller, Electronic Systems and Intelligent Computing, Springer. (2020) 453-463.
[14] Alireza Alfi and Hamidreza Modares, System identification and control using adaptive particle swarm optimization, Applied Mathematical Modelling. 35(3) (2011) 1210-1221.
[15] Dušan Fister et al., Parameter tuning of PID controller with reactive nature-inspired algorithms, Robotics and Autonomous Systems. 84 (2016) 64-75.
[16] Zhao Jun, Tianpeng Li and Jixin Qian, Application of particle swarm optimization algorithm on robust PID controller tuning, Lecture notes in computer science, Springer. 3612 (2005) 948-957.
[17] D. Kalpana, T. Thyagarajan, and N. Venkatachalam, Design of fractional order PI controller for MIMO system using relay feedback, Trends in Industrial Measurement and Automation, IEEE. (2017) 1-7.
[18] D.Pamela and T. Jebarajan, Design of intelligent controller for temperature process, International Conference on Future Generation Communication and Networking, Springer. 350 (2012) 278-284.
[19] Cristian Jauregui et al., Conical tank level control using fractional order PID controllers: A simulated and experimental study, Control Theory and Technology. 14(4) (2016) 369-384.
[20] Diary R. Sulaiman, Multi-objective Pareto front and particle swarm optimization algorithms for power dissipation reduction in microprocessors, International Journal of Electrical and Computer Engineering. 10(6) (2020) 6549-6557.
[21] Umbrin Sultana, Sajid Hussain Qazi, Nadia Rasheed, and M. W. Mustafa, Performance analysis of real-time PSO tuned PI controller for regulating voltage and frequency in an AC microgrid, International Journal of Electrical and Computer Engineering. 11(2) (2021) 1068-1076.
[22] Aliyu Hamza Sule et al., Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer, International Journal of Electrical and Computer Engineering. 10(5) (2020) 5251-5261.
[23] S.Nithya, N.Sivakumaran, T.Balasubramanian, and N.Anantharaman, Design of controller for nonlinear process using soft computing, Instrumentation Science and Technology. 36(4) (2008) 437–450.
[24] R.Anandanatarajan, M.Chidambaram and T.Jayasingh, Limitations of PI controller for a first-order nonlinear process with dead time, ISA Transactions. 45 (2014) 185-99.
[25] Vijayalakshmi et al., Closed loop experimental validation of linear paramerter varying model with Adaptive PI Controller for conical tank system, Journal of Control Engineering and Applied Informatics. 16(4) (2014) 12-19.
[26] Ngoc-Khoat Nguyen, Duy-Trung Nguyen , A Comparative Study on PI and PD -Type Fuzzy Logic Control Strategies, International Journal of Engineering Trends and Technology. 69(7) (2021) 101- 108.
[27] Ritika Thusoo, Sheilza Jain, Sakshi Bangia, Control of non-linear Quadrotor using PID and Backstepping Techniques, International Journal of Engineering Trends and Technology. 69(7) (2021) 167- 173.
[28] M.Kalarathi, K.Jayanthi, Dual State DC-DC Converter with PI and Fuzzy PI Controller for LED Drivers, International Journal of Engineering Trends and Technology. 69(3) (2021) 180-184.