A Comparative Study of Various Intelligent Algorithms Based Nonlinear PID Neural Trajectory Tracking Controller for the Differential Wheeled Mobile Robot Model
محتوى المقالة الرئيسي
الملخص
This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; this is demonstrated by the minimized tracking error and obtained smoothness of the velocity control signal, especially when external disturbances are applied.
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Al-Araji, A., Abbod, M. and Al-Raweshidy, H., 2013, Applying Posture Identifier in Designing an Adaptive Nonlinear Predictive Controller for Nonholonomic Mobile Robot. Neurocomputing, Vol. 99, pp. 543-554.
Al-Araji, A., 2005, Genetic Algorithm and Elman Neural Network Used for Tuning the Parameters of the PID Neural Controller Based Model Reference. Iraqi journal of computers, communication, control and systems engineering by University of Technology Vol. 5, No.1, pp.111-126.
Ali, O. M., Koh, S. P., Chong, K. H., Hamoodi, A. S., 2011, Design a PID Controller of BLDC Motor by Using Hybrid Genetic-Immune, Modern Applied Science .Vol. 5, No. 1, pp. 56-63.
Al-Shibaany, Z., Hedley, J. and Bicker, R., 2012, Design of an Adaptive Neural Kinematic Controller for a National Instrument Mobile Robot System, IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, pp. 623-628.
Chang, Y-C, Lwin, Y.Y. and Yamamoto, Y., 2009, Sensor-Based Trajectory Planning Strategy for Nonholonomic Mobile Robot with Laser Range Sensors, IEEE International Symposium on Industrial Electronics, Seoul Olympic Parktel, Seoul, Korea. pp. 1755-1760.
Derrac, J., Garc, S., Molina, D. and Herrera, F., 2011, A Practical Tutorial on the Use of Nonparametric Statistical Tests as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms, Journal of Swarm and Evolutionary Computation, Vol.1, pp. 3–18.
Han, S., Choi, B. and Lee, J., 2008, A Precise Curved Motion Planning for a Differential Driving Mobile Robot. Mechatronics, Vol. 18, pp. 486-494.
Klancar, G. and Skrjanc, I., 2007, Tracking Error Model-Based Predictive Control for Mobile Robots in Real Time, Robotics and Autonomous Systems, Vol. 55, pp. 460-469.
Mnif, F. and Touati, F., 2005, An Adaptive Control Scheme for Nonholonomic Mobile Robot with Parametric Uncertainty, International Journal of Advanced Robotic Systems, Vol. 2, No. 1, pp. 59-63.
Muhammet, U., 2010, Trajectory Tracking Performance Comparison Between Genetic Algorithm and Ant Colony Optimization for PID Controller Tuning on Pressure Process, Wiley Periodicals Inc.
Omatu, S., Khalid, M. and Yusof, R., 1995, Neuro-Control and its Applications. London: Springer-Velag.
Park, B. S., Yoo, S. J., Park, J. B. and Choi, Y. H., 2010, A Simple Adaptive Control Approach for Trajectory Tracking of Electrically Driven Nonholonomic Mobile Robots, IEEE Transactions on Control Systems Technology, Vol. 18, No. 5, pp. 1199-1206.
Righeto, E., Grassi, L-H M. and Pereira, J. A., 2004, Nonlinear Plant Identification by Wavelet, Proceeding of ABCM Symposium Series in Mechatronics, Vol. 1, pp. 392-398.
Sahin, T. and Zergeroglu, E., 2007, A Computationally Efficient Path Planner for a Collection of Wheeled Mobile Robots with Limited Sensing Zones, IEEE International Conference on Robotics and Automation, Roma, Italy, pp. 1074-1079.
Su, K-H., Chen, Y-Y. and Su, S-F., 2010, Design of Neural-Fuzzy-Based Controller for Two Autonomously Driven Wheeled Robot, Neurocompting. Vol. 73, pp. 2478-2488.
Takanori, F., Kiroshi, N. and Norihiko, A., 2000, Adaptive Tracking Control of a Nonholonomic Mobile Robot, IEEE Trans Robotic Autonomous, Vol. 16, No. 2, pp. 609-615.
Wai, R-J and Liu, C-M, 2009, Design of Dynamic Petri Recurrent Fuzzy Neural Network and its Application to Path-Tracking Control of Nonholonomic Mobile Robot, IEEE Transactions on Industrial Electronics. Vol. 56, No. 7, pp. 2667-2683.
Yang, S. X., Yang, H., Max, Q. and Meng, H., 2004, Neural Dynamics Based Full-State Tracking Control of a Mobile Robot, IEEE International Conference on Robotics & Automation, New Orleans, LA, pp. 4614-4619.
Zhong, Q., 2006, Robust Control of Time-delay Systems, Springer – Verlag London Limited.