Level control of nonlinear systems is an important problem in process industries. The conical tank system is a nonlinear process because the cross-sectional area varies with the height of the liquid. Conventional PID controllers often fail to provide satisfactory performance over the entire operating range of such nonlinear systems. This paper presents the design and implementation of a Gain Scheduling PID controller for controlling the liquid level in a conical tank. The nonlinear model of the conical tank is derived using mass balance principles. The operating region of the tank is divided into different sections and separate PID parameters are designed for each region. A gain scheduling mechanism is used to switch the controller parameters based on the current tank level. The proposed control system is implemented and simulated using MATLAB/Simulink. Simulation results show improved dynamic response, reduced overshoot, faster settling time and better set-point tracking compared to a conventional PID controller.
Introduction
The text explains the control of liquid level in a conical tank, which is an important industrial process in areas like chemical plants, refineries, and water treatment systems. Because the tank has a changing cross-sectional area, its behavior is nonlinear, making it difficult to control using traditional PID controllers across all operating conditions.
To address this, the paper proposes a Gain Scheduling PID controller, where different PID parameters are used for different operating regions of the tank. The system is mathematically modeled using mass balance principles and Torricelli’s law, resulting in a nonlinear differential equation that is also approximated using a FOPDT transfer function for control design.
A brief literature review highlights previous work on PID control, nonlinear control techniques, and model predictive control, showing that nonlinear systems require more adaptive and robust control strategies.
Finally, simulations are conducted to validate the proposed approach.
Conclusion
The design and simulation of a Gain Scheduling PID controller for controlling the level of a conical tank system. The nonlinear behavior of the tank was handled by dividing the operating range into multiple regions and assigning different PID gains for each region. Simulation results demonstrated that the Gain Scheduling PID controller significantly improves the dynamic performance compared to a conventional PID controller. The proposed method provides faster response, reduced overshoot, and better set-point tracking.
References
[1] Kadu, C. B., & Patil, C. Y. (2016). Design and implementation of stable PID controller for interacting level control system. Procedia Computer Science, 79, 737-746.
[2] Eapen, I. M., & Rose, L. (2017, February). Non linear intelligent controller for a level process. In 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT) (pp. 128-131). IEEE.
[3] Villani, M., Sani, L., Pecori, R., Amoretti, M., Roli, A., Mordonini, M., ... & Cagnoni, S. (2018). An Iterative Information?Theoretic Approach to the Detection of Structures in Complex Systems. Complexity, 2018(1), 3687839.
[4] Binette, J. C., & Srinivasan, B. (2016). On the use of nonlinear model predictive control without parameter adaptation for batch processes. Processes, 4(3), 27.
[5] Šekara, T. B., Mataušek, M. R., & de Oliveira Serra, G. L. (2012). PID controller tuning based on the classification of stable, integrating and unstable processes in a parameter plane. In Frontiers in advanced control systems (pp. 117-142). InTech.
[6] Londhe, P. P., Kadu, C. B., & Parvat, B. J. (2016). IMC-PID controller designing for FOPDT & SOPDT systems. IJIREEICE, 4(5), 185-189.
[7] Baloochy, B. (2013). Pid Controller Tuning: Improvement Of Classic Approaches In Chemical Processes. Petroleum & Coal, 55(3).
[8] Kumar, D. D., Meenakshipriya, B., & Ram, S. S. (2016). Design of PSO based I-PD controller and PID controller for a spherical tank system. Indian Journal of Science and Technology, 9(12), 1-5.
[9] Shamsuzzoha, M. (2013). Closed-loop PI/PID controller tuning for stable and integrating process with time delay. Industrial & Engineering Chemistry Research, 52(36), 12973-12992.
[10] Kadu, C. B., & Sakhare, D. V. (2015). Improved inverse response of boiler drum level using fuzzy self adaptive PID controller. International Journal of Engineering Trends and Technology, 19.
[11] Horn, I. G., Arulandu, J. R., Gombas, C. J., VanAntwerp, J. G., & Braatz, R. D. (1996). Improved filter design in internal model control. Industrial & engineering chemistry research, 35(10), 3437-3441.
[12] Lee, Y., Lee, J., & Park, S. (2000). PID controller tuning for integrating and unstable processes with time delay. Chemical engineering science, 55(17), 3481-3493.
[13] Zafiriou, E., & Morari, M. (1991). Internal model control: Robust digital controller synthesis for multivariable open-loop stable or unstable processes. International Journal of control, 54(3), 665-704.