DOI : https://doi.org/10.18517/ijaseit.9.4.9485

Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot

Nurul Hasanah (1), - Alrijadjis (2), Bambang Sumantri (3)
(1) Electronic Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
(2) Electronic Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
(3) Electronic Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
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How to cite (IJASEIT) :
Hasanah, Nurul, et al. “Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, Aug. 2019, pp. 1154-62, doi:10.18517/ijaseit.9.4.9485.
Citation Format :
Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO - PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control.

D. Pratama, F. Ardilla, E. H. Binugroho, and D. Pramadihanto, “Tilt set-point correction system for balancing robot using PID controller,” in ICCEREC 2015 - International Conference on Control, Electronics, Renewable Energy and Communications, 2015, pp. 129-135.

D. Pratama, E. H. Binugroho, and F. Ardilla, “Movement control of two wheels balancing robot using cascaded PID controller,” in Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015, 2016, pp. 94-99.

E. H. Binugroho, D. Pratama, A. Z. R. Syahputra, and D. Pramadihanto, “Control for balancing line follower robot using discrete cascaded PID algorithm on ADROIT V1 education robot,” in Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015, 2016, pp. 245-250.

E. Sariyildiz, H. Yu, and K. Ohnishi, “A Practical Tuning Method for the Robust PID Controller with Velocity Feed-Back,” Machines, vol. 3, no. 3, pp. 208-222, 2015.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.

N. Jain, R. Gupta, and G. Parmar, “Intelligent Controlling of an Inverted Pendulum Using PSO-PID Controller,” Int. J. Eng. Res. Technol., vol. 2, no. 12, pp. 3712-3716, 2013.

D. Yang, J. Chen, and N. Matsumoto, “Particle Swarm Optimization with Adaptive Parameters,” in Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007, pp. 616-621.

M. Li, W. Du, and F. Nian, An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex network, vol. 2014. 2014.

H. Mehdi and O. Boubaker, “Stabilization and tracking control of the inverted pendulum on a cart via a modified PSO fractional order PID controller,” in The inverted pendulum in control theory and robotics, 2017, pp. 93-115.

Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in IEEE Congress on Evolutionary Computation, 1998, pp. 69-73.

Y. Shi and R. Eberhart, “Empirical Study of Particle Swarm Optimization,” in IEEE Congress on Evolutionary Computation (CEC), 1999, pp. 1945-1950.

R. F. Malik, T. A. Rahman, S. Z. M. Hashim, and R. Ngah, “New Particle Swarm Optimizer with Sigmoid Increasing Inertia Weight,” Int. J. Comput. Sci. Secur., vol. 1, pp. 35-44, 2007.

Y. Gao, X. An, and J. Liu, “A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation,” Int. Conf. Comput. Intell. Secur., vol. 1, pp. 61-65, 2008.

K. Lei, Y. Qiu, and Y. He, “A New Adaptive Well-chosen Inertia Weight strategy to Automatically Harmonize Global and Local Search Ability in Particle Swarm Optimization,” in First International Symposium on Systems and Control in Aerospace and Astronautics, Harbin, 2006, pp. 977-980.

Z. Li, C. Yang, and L. Fan, Advanced control of wheeled inverted pendulum systems, vol. 9781447129. 2013.

J. Wu, X. Wang, and H. Wang, “Research on Two-Wheeled Self-Balancing Robot Control Strategy Based on LQR-Fuzzy Algorithm,” Int. J. Control Autom., vol. 9, pp. 31-40, 2016.

K. Singh, “Modified PSO based PID Sliding Mode Control using Improved Reaching Law for Nonlinear systems,” 2017, no. April.

N. Hasanah, A. H. Alasiry, and B. Sumantri, “Two Wheels Line Following Balancing Robot Control using Fuzzy Logic and PID on Sloping Surface,” in 2018 International Electronics Symposium on Engineering Technology and Applications, IES-ETA 2018 - Proceedings, 2019, pp. 210-215.

I. Dwisaputra, I. Sulistijono, and M. Nugraha, “Two Wheels Balancing Line Tracer Robot Using Fuzzy Logic Control,” in The 13th Industrial Electronics Seminar (IES), 2011, pp. 188-194.

S. Kessentini and D. Barchiesi, “Particle Swarm Optimization with Adaptive Inertia Weight,” Int. J. Mach. Learn. Comput., vol. 5, no. 5, pp. 368-373, 2015.

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