Abstract
This paper proposes field programmable gate array (FPGA) realization of fuzzy PI controller for typical industrial process called stirred tank heater system. Due to the complex nature of fuzzy controller families, its implementation on digital signal processors (DSPs) result computational time delay which, in other words, makes the processing speed slower. To address the limitations of DSP implementation, an alternative digital hardware programmable device called FPGAs are being largely used for digital implementation of algorithms with higher computational speed and accuracy. The proposed work is inspired by this quality of FPGAs and aims to take advantage of reliable performance of fuzzy PI controllers with its complexity and having no considerable impact on processing speed. Here, fast and novel design approach for rapid prototyping of fuzzy PI controller for stirred tank heater is presented thoroughly. Xilinx system generator SIMULINK add on from VIVADO is used to generate very high-speed integrated circuit hardware description language (VHDL) directly from MATLAB. The developed controller for stirred tank system is verified in fixed point arithmetic using Xilinx Simulink blocks. The VHDL code is then generated, synthesized, implemented, and made ready for physical implementation on Kintex-7 evaluation board.
Similar content being viewed by others
Data availability
The data used for this research findings are available from the corresponding author upon reasonable request.
Abbreviations
- DSP:
-
Digital signal processor
- FPGA:
-
Field programmable gate arrays
- HIL:
-
Hardware in the loop
- IDE:
-
Integrated development environment
- PI:
-
Proportional plus integral
- VHDL:
-
Very high-speed integrated circuit hardware description language
- XSG:
-
Xilinx system generator
- A :
-
Area of base of tank
- \(A_t\) :
-
Total area of heat transfer
- \(C_p\) :
-
Specific heat capacity
- \(\Delta e\) :
-
Rate of change of error
- e :
-
Error between set value and actual value
- F :
-
Flow rate of liquid substance flowing out of the tank
- \(F_1\) :
-
Flow rate of liquid substance flowing into the tank
- \(F_{\text {st}}\) :
-
Flow rate of steam
- \(G_{\Delta e}\) :
-
Normalizing gain for \(\Delta e\)
- \(G_{\text {du}}\) :
-
Normalizing gain for controller output signal
- \(G_e\) :
-
Normalizing gain for e
- \(K_i\) :
-
Integral constant
- \(K_p\) :
-
Proportional constant
- Q :
-
The rate of heat added to the steam
- \(\rho \) :
-
Density of substance
- T :
-
Temperature of liquid substance flowing out of the tank
- \(T_1\) :
-
Temperature of liquid substance flowing into the tank
- \(T_{\text {st}}\) :
-
Temperature of steam
- U :
-
Overall heat transfer coefficient
References
Abbas G, Nazeer MQ, Balas VE, Lin TC, Balas MM, Asad MU, Raza A, Shehzad MN, Farooq U, Gu J (2019) Derivative-free direct search optimization method for enhancing performance of analytical design approach-based digital controller for switching regulator. Energies 12:1–18. https://doi.org/10.3390/en12112183
Aguilar-López R, Mata-Machuca JL, Godinez-Cantillo V (2021) A TITO control strategy to increase productivity in uncertain exothermic continuous chemical reactors. Processes 9(5):873. https://doi.org/10.3390/pr9050873
Alshammari OS, Mahyuddin MN, Jerbi H (2018) A survey on control techniques of a benchmarked continuous stirred tank reactor. https://api.semanticscholar.org/CorpusID:221670512
Ammar A (2019) Performance improvement of direct torque control for induction motor drive via fuzzy logic-feedback linearization: simulation and experimental assessment. COMPEL 38:672–692. https://doi.org/10.1108/COMPEL-04-2018-0183
Chakraverty S, Sahoo DM, Mahato NR (2019) Concepts of soft computing: fuzzy and ann with programmingd. Concepts Soft Comput. https://doi.org/10.1007/978-981-13-7430-2
Dale ES, Edgar TF, Mellichamp DA, Doyle FJ (2016) Process dynamics and control, 4th edn. John Wiley and Sons, Inc., p 512
Das A, Krishnakumari T (2018) Comparison of PI controller and fuzzy logic controller for the improvement of power factor in SMPS. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp 1597–1602. IEEE, Coimbatore. https://doi.org/10.1109/ICICCT.2018.8473152
Hanafi D, Ribuan MN, Abas WHW, Hidayat Johana E, Wahid H, Ghazali R, Rahman HA (2018) Online position control performance improving applying incremental fuzzy logic controller. MATEC Web Conf. https://doi.org/10.1051/matecconf/201824802005
Husain S, Ahmad Y, Sharma M, Ali S (2017) Comparative analysis of defuzzification approaches from an aspect of real life problem. IOSR J Comput Eng 19:19–25. https://doi.org/10.9790/0661-1906031925
Jantzen J (2013) Foundations of fuzzy control: a practical approach, 2nd edn. John Wiley and Sons, Ltd, p 325. https://doi.org/10.1002/9781118535608
Jemaa A, Zarrad O, Hajjaji MA, Mansouri MN (2018) Hardware implementation of a fuzzy logic controller for a hybrid wind-solar system in an isolated site. Int J Photoenergy 2018. https://doi.org/10.1155/2018/5379864
Krim S, Gdaim S, Mtibaa A, Mimouni MF (2017) Modeling and hardware implementation on the FPGA of a variable structure control associated with a DTC-SVM of an induction motor. Electr Power Compon Syst 45(16):1806–1821. https://doi.org/10.1080/15325008.2017.1351010
Krim S, Gdaim S, Mtibaa A, Mimouni MF (2019) Control with high performances based DTC strategy: FPGA implementation and experimental validation. EPE J 29(2):82–98. https://doi.org/10.1080/09398368.2018.1548802
Krim S, Gdaim S, Mtibaa A, Mimouni MF (2020) Fpga-based real-time implementation of a direct torque control with second-order sliding mode control and input-output feedback linearisation for an induction motor drive. IET Electr Power Appl 14:480–491. https://doi.org/10.1049/iet-epa.2018.5829
Mahmood QA, Nawaf AT, Esmael MN, Abdulateef LT, Dahham OS (2018) PID temperature control of demineralized water tank. IOP Conf Ser 454:012031. https://doi.org/10.1088/1757-899X/454/1/012031
Mishra E, Tiwari S (2017) Comparative analysis of fuzzy logic and pi controller based electronic load controller for self-excited induction generator. Adv Electric Eng 2017:1–9. https://doi.org/10.1155/2017/5620830
Mohanaprasad K, Murugan MSB, Subhashini N, Thalmann D (eds) (2018) Intelligent embedded systems: select proceedings of ICNETS2, Volume II, 1st ed. 2018 edn. Lecture Notes in Electrical Engineering, vol. 492. Springer, Singapore. https://doi.org/10.1007/978-981-10-8575-8
Quynh NV, Llopis-Albert C (2020) The fuzzy pi controller for pmsm’s speed to track the standard model. Math Prob Eng. https://doi.org/10.1155/2020/1698213
Rajagopal K, Laarem G, Karthikeyan A, Srinivasan A (2017) Fpga implementation of adaptive sliding mode control and genetically optimized pid control for fractional-order induction motor system with uncertain load. Adv Differ Equ. https://doi.org/10.1186/s13662-017-1341-9
Ramadhan EF, Ariyanto M, Iskandar N, Amirullah MA, Julianti HP, Setiawan R (2018) Design and simulation of fuzzy logic based temperature control for a mixing process in therapeutic pool. https://api.semanticscholar.org/CorpusID:187331378
Somwanshi D, Bundele M, Kumar G, Parashar G (2019) Comparison of fuzzy-PID and PID controller for speed control of DC motor using LabVIEW. Procedia Comput Sci 152:252–260. https://doi.org/10.1016/j.procs.2019.05.019. (Accessed 2023-07-31)
Suprapto BY, Bayusari I, Muhammad C (2018) Comparison of cascade and feedforward-feedback controllers for temperature control on stirred tank heater systems. In: 2018 International Seminar on Application for Technology of Information and Communication, pp 166–170. IEEE, Semarang. https://doi.org/10.1109/ISEMANTIC.2018.8549782
Tang X, Xu B, Xu Z (2023) Reactor temperature control based on improved fractional order self-anti-disturbance. Processes 11(4):1125. https://doi.org/10.3390/pr11041125
Toledo A, Vicente-Chicote C, Suardáz J, Cuenca S (2005) Xilinx system generator based hw components for rapid prototyping of computer vision sw/hw systems. Lect Notes Comput Sci 3522:667–674. https://doi.org/10.1007/11492429_80
Zhang L (2017) System generator model-based FPGA design optimization and hardware co-simulation for Lorenz chaotic generator. In: 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp 170–174. IEEE, Wuhan, China. https://doi.org/10.1109/ACIRS.2017.7986087
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All the authors have read and approved this manuscript and they have also contributed equally.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix A: System and controller parameters
Appendix A: System and controller parameters
See Table 4
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Fetene, Y., Ayenew, E. Design and FPGA realization of incremental fuzzy controller for stirred tank heater. Soft Comput 27, 16511–16522 (2023). https://doi.org/10.1007/s00500-023-09149-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-09149-x