Firefly Algorithm tuned Fuzzy Set-Point Weighted PID Controller for Antilock Braking Systems

A Manivanna Boopathi, A Abudhahir


Antilock Braking Systems (ABSs) are brake controllers designed to maintain the wheel slip in desired level during braking and acceleration. Since the factors causing the wheel slip to change such as nature of road surface, vehicle mass etc are highly uncertain, the task of controlling it has been a challenging one. In this paper, a modified PID controller called Set-Point Weighted PID (SPWPID) Controller has been designed to control the wheel slip. First, a Genetic Algorithm (GA) based fuzzy inference system (GAFSPWPID) is developed to determine the value of the weight that multiplies the set-point for the proportional action, based on the current output error and its derivative. It makes the controller more adaptive to external disturbances. Then, the GA is replaced by Firefly Algorithm (FA). Minimization of Integral Square Error (ISE) has been taken as objective for both cases. The performance of proposed Firefly Algorithm tuned Fuzzy Set-Point Weighted PID (FAFSPWPID) Controller is compared with SPWPID and GAFSPWPID controllers. Also, the performance of proposed controller is assessed for different initial conditions. A comparison has also been made with the controllers presented earlier in literature. Simulation results show that the proposed FAFSPWPID Controller performs better in both set-point tracking and adaptive to external disturbances than the other controllers.


Antilock Brake System; Firefly Algorithm; Fuzzy Logic; PID Controller; Wheel slip control.

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Aidan O’Dwyer, 2006. Handbook of PI and PID Controller tuning rules, 2nd edition. ICP.

Andon V.Topalov, Yesim Oniz, Erdal Kayacan & Okyay Kaynak, 2011. ‘Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm’, Journal of Neuro-computing, 11, 1883-1893.

Antonio Visioli, 1999. ‘Fuzzy Logic Based Set-Point Weight Tuning of PID Controllers’, IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 6, 587-592.

Åström, K.J. & Hägglund, T., 2004. ‘Revisiting the Ziegler-Nichols step response method for PID Control’, Journal of Process control, 635-650.

Åström, K.J., & Tore Hägglund., 1995. PID Controllers - Theory, Design and Tuning, 2nd edition. ISA.

Chidambaram, M., 2000. ‘Set point weighted PI/PID controllers’, Chem. Engg. Communications., 179(1), 1-13.

Choi, S-B., Bang, J-H., Cho, M-S., & Lee, Y-S., 2002. ‘Sliding mode control for anti-lock brake system of passenger vehicles featuring electrorheological valves’, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 897-908.

Dragan Antić, Marko Milojković, Zoran Jovanović & Saša Nikolić., 2010. ‘Optimal Design of the Fuzzy Sliding Mode Control for a DC Servo Drive’, Journal of Mechanical Engineering, 455-463.

Georg F. Mauer., 1995. ‘A Fuzzy Logic Controller for an ABS Braking System’, IEEE Transactions on Fuzzy Systems, 4, 381-388.

Harifi, A., Aghagolzadeh, A., Alizadeh, G., & Sadeghi, M., 2005. ‘Designing a sliding mode controller for antilock brake system’, Proc. Int. Conf. Comput. Tool, Serbia and Montenegro, Europe, 611-616.

Iztok Fister, Iztok Fister Jr, Xin-She Yang & Janez Brest., 2013. ‘A comprehensive review of firefly algorithms’, Swarm and Evolutionary Computation, DOI: 10.1016/ j.swevo.2013.06.001.

Jeffery R. Layne, Kevin M. Passino & Stephen Yurkovich., 1993. ‘Fuzzy Learning Control for Antiskid Braking Systems’, IEEE Transactions on Control Systems Technology, 2, 122-129.

Kumanan, D. & Nagaraj, B., (2013) ‘Tuning of proportional integral derivative controller based on firefly algorithm’, Systems Science & Control Engineering: An Open Access Journal - Taylor & Francis, 1, 52–56.

Lin, C.M., & Hsu, C.F., 2003. ‘Self-learning fuzzy sliding-mode control for antilock braking systems’, IEEE Trans. Control Syst. Technol., 2, 273-278.

Radac, M.B., Precup, R.E., Preitl, S., Tar, J.K., & Petriu, E.M., 2008. ‘Linear and Fuzzy Control Solutions for a Laboratory Anti-lock Braking System’, 6th International Symposium on Intelligent Systems and Informatics, Subotica, 1-6.

Unsal, C., & Kachroo, P., 1999. ‘Sliding mode measurement feedback control for antilock braking systems’, IEEE Trans. Control Syst. Technol., 2, 271-281.

User’s manual, 2006. ‘The Laboratory Antilock Braking System Controlled from PC’, Inteco Ltd., Crakow, Poland.

Xin-She Yang, 2010. ‘Nature-Inspired Metaheuristic Algorithms’, 2nd edition, Luniver Press, UK.

Yesim Oniz, 2007. ‘Simulated and experimental study of antilock braking system using Grey Sliding mode control’, M.S.Thesis, Boğaziҫi University, Turkey.

Yesim Oniz, Erdal Kayacan & Okyay Kaynak, 2009. ‘A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation’, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 2, 551-560.

Yonggon Lee & Stanislaw H.Żak, 2002. ‘Designing a Genetic Neural Fuzzy Antilock-Brake-System Controller’, IEEE Transactions on Evolutionary Computation, 2, 198-211.

Ziegler, J.G. & Nichols, N.B., 1942. ‘Optimum setting for automatic controllers’, ASME Transactions,759–768.


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