International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 5, 8 May 2017
Cover page and Table of Contents: PDF (size: 1069KB)
Adaptive Path Tracking Mobile Robot Controller Based on Neural Networks and Novel Grass Root Optimization Algorithm
Full Text (PDF, 1069KB), PP.1-9
Views: 0 Downloads: 0
Author(s)
Index Terms
Artificial Neural Networks, Metaheuristic algorithms, Grass Root Algorithm, Trajectory tracking controller, Wheeled mobile robot
Abstract
This paper proposes a novel metaheuristic optimization algorithm and suggests an adaptive artificial neural network controller that based on the proposed optimization algorithm. The purpose of the neural controller is to track desired proposed velocities and path trajectory with the minimum error, in the presence of mobile robot parameters time variation and system model uncertainties. The proposed controller consists of two sub-neural controllers; the kinematic neural feedback controller, and the dynamic neural feedback controller. The external feedback kinematic neural controller was responsible of generating the velocity tracking signals that track the mobile robot linear and angular velocities depending on the robot posture error, and the desired velocities. On the other hand, the internal dynamic neural controller has been used to enhance the mobile robot against parameters uncertainty, parameters time variation, and disturbance noise. However, the proposed grass root population-based metaheuristic optimization algorithm has been used to optimize the weights of the neural network to have the behavior of an adaptive nonlinear trajectory tracking controller of a differential drive wheeled mobile robot. The proposed controller shows a very good ability to prepare an appropriate dynamic control left and right torque signals to drive various mobile robot platforms using the same offline optimized weights. Grass root optimization algorithms have been used due to their unique characteristics especially, theirs derivative free, ability to optimize discretely and continuous nonlinear functions, and ability to escape of local minimum solutions.
Cite This Paper
Hanan A. R. Akkar, Firas R. Mahdi, "Adaptive Path Tracking Mobile Robot Controller Based on Neural Networks and Novel Grass Root Optimization Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.5, pp.1-9, 2017. DOI:10.5815/ijisa.2017.05.01
Reference
[1]K. H. Teng, T. Wu, Z. Yang, C. H. Heng and X. Liu, "A 400-MHz wireless neural signal processing IC with 625 on-chip data reduction and reconfigurable BFSK/QPSK transmitter based on sequential injection locking," 2015 IEEE Asian Solid-State Circuits Conference (A-SSCC), Xiamen, pp. 1-4, 2015.
[2]Neha Kapoor, Jyoti Ohri,"Evolutionary Optimized Neural Network (EONN) Based Motion Control of Manipulator", International Journal of Intelligent Systems and Applications IJISA, vol.6, no.12, pp.10-16, 2014.
[3]Yevgeniy Bodyanskiy, Olena Vynokurova, Volodymyr Savvo, Tatiana Tverdokhlib, Pavlo Mulesa,"Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.8, pp.1-9, 2016.
[4]R. Murugadoss and M. Ramakrishnan, "Universal approximation of nonlinear system predictions in sigmoid activation functions using artificial neural networks," 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, pp. 1-6, 2014.
[5]S. Chai and Y. Zhou, "A Study on How to Help Back-propagation Escape Local Minimum," Third International Conference on Natural Computation (ICNC 2007), Haikou, pp. 64-68, 2007.
[6]Koffka Khan,Ashok Sahai,"A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context", International Journal of Intelligent Systems and Applications IJISA, vol.4, no.7, pp.23-29, 2012.
[7]Hanan A. R. Akkar, Firas R. Mahdi, “Training ArtificialNeural Networks by PSO to Perform Digital Circuits Using Xilinx FPGA,” Eng. & Tech. Journal, Vol. 29, No. 7 pp. 1329-1344, 2011.
[8]R. Fierro and F. Lewis, “Control of a Nonholonomic Mobile Robot Using Neural Networks,” IEEE Transactions on neural networks, vol. 9, pp. 589–600, 1998.
[9]X. Jiang, Y. Motai and X. Zhu, "Predictive Fuzzy Logic Controller for Trajectory Tracking of a Mobile Robot," Proceedings of the IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications. SMCia/05., pp. 29-32, 2005.
[10]R. Martinez-Soto, O. Castillo, L. T. Aguilar and I. S. Baruch, "Bio-Inspired Optimization of Fuzzy Logic Controllers for Autonomous Mobile Robots," Fuzzy Information Processing Society (NAFIPS), Annual Meeting of the North American, Berkeley, CA, pp. 1-6, 2012.
[11]Raimúndez and A. B. Blas, "Adaptive Tracking in Mobile Robots with Input-Output Linearization," Industrial Electronics Society, IECON 39th Annual Conference of the IEEE, Vienna, pp. 3299-3304, 2013.
[12]Stickler. Grass Growth and Development. Texas Cooperative Extension, Texas A&M University.
[13]Gaurang Panchal, Amit Ganatra, Y P Kosta and Devyani Panchal, "Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers," International Journal of Computer Theory and Engineering, Vol. 3, No. 2, April 2011.
[14]T. Fukao, H. Nakagawa, “Adaptive Tracking Control of a Nonholonomic Mobile Robot,” IEEE transactions on Robotics and Automation, vol. 16, pp. 609–615, 2000.
[15]Kolmanovsky and N. H. McClamroch, "Developments in nonholonomic control problems," in IEEE Control Systems, vol. 15, no. 6, pp. 20-36, Dec 1995.