Optimization of Single Input Fuzzy Logic Controller Using PSO for Unmanned Underwater Vehicle
This paper describes the optimization technique using Particle Swarm Optimization (PSO) are applied to tune parameter of Single Input Fuzzy Logic Controller (SIFLC) for depth control of the Unmanned Underwater Vehicle (UUV). Two parameter SIFLC will be considered to tune the parameter based on off-line results for PSO algorithm to give a best system response in terms of overshoot and rise time. The parameter after look-up table will be fixed because the gain obtained by using the PSO algorithm is almost the same. This paper also investigated the parameter of look-up table for five input rules. Simulation is conducted within MATLAB/Simulink environment to verify the performance of the controller. It is demonstrated that the controller is effective to move the UUV as fast as possible to the desired depth with the best response system in terms of zero overshoot and 5 s rise time performances.
KeywordsParticle swarm optimization, single input fuzzy logic controller Unmanned underwater vehicle
Special appreciation and gratitude to the honorable University (Universiti Teknikal Malaysia Melaka, UTeM and Universiti Teknologi Malaysia, UTM) especially to the both Faculties of Electrical Engineering for providing the financial as well as moral support to complete this project successfully.
- 5.Taeed, Fazel, Salam, Zainal, Ayob, Shahrin M.: FPGA implementation of a single-input fuzzy logic controller for boost converter with the absence of an external analog-to-digital converter. IEEE Trans. Ind. Electron. 59(2), 1208–1217 (2012). https://doi.org/10.1109/TIE.2011.2161250CrossRefGoogle Scholar
- 7.Ishaque, K.: Intelligent control of diving system of an underwater vehicle. Master Thesis. Universiti Teknologi, Malaysia (2009)Google Scholar
- 8.Aras, M.S.M., Azis, F.A., Syed Abdul Hamid, S.M.S., Ali, F.A., Abdullah, S.S.: Study of the effect in the output membership function when tuning a fuzzy logic controller. In: 2011 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2011) Google Scholar
- 11.Aras, M.S., Azis, F.A., Othman, M.N., Abdullah, S.S.: A low cost 4 DOF remotely operated underwater vehicle integrated with IMU and pressure sensor. In: 4th International Conference on Underwater System Technology: Theory and Applications 2012 (USYS’12), pp 18–23. Malaysia (2012)Google Scholar
- 12.Aras, M.S.M., Abdullah, S.S., Rashid, M.Z.A., Rahman, A.A., Aziz, M.A.A.: Development and modeling of underwater remotely operated vehicle using system identification for depth control. J. Theor. Appl. Inf. Technol. 56, 1 (2013)Google Scholar
- 13.Aras, M.A.M.: Adaptive simplified fuzzy logic controller for depth control of underwater remotely operated vehicle, Thesis (2015)Google Scholar
- 14.Jaafar, H.I., Mohamed, Z., Abidin, A.F.Z., Ghan, Z.A.: PSO-tuned PID controller for a nonlinear gantry, crane system. In: IEEE International Conference on Control System, Computing and Engineering, pp. 1–5, 23–25 Nov 2012Google Scholar
- 15.Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
- 16.Pires, E.S., Machado, J.T., de Moura Oliveira, P.B.: Particle swarm optimization: dynamical analysis through fractional calculus, Chapter 24, InTech Publisher (2009)Google Scholar
- 18.Solihin, M.I., Kamal, M.A.S., Legowo, A.: Optimal PID controller tuning of automatic gantry crane using PSO algorithm. In: Proceeding of the 5th International Symposium on Mechatronics and its Applications (ISMA08), pp. 1–5. Amman, Jordan, 27–29 May 2008Google Scholar