Abstract

In this paper, mobile robot control system for dynamic model is implemented by using wavelet neural network and optimized by depending on PSO algorithm. The work is divided into two sections. In the1^stsection, the best structure of wavelet neural network controller is selected among different tested structures (by changing the number of neurons in hidden layer). In the 2^ndsection, the best wavelet neural network controller is selected among different tested controllers by depending on the type of wavelet filter. The comparing is done by depending on the MSE values. The simulation is done by using MATLAB which reveals a good performance for the proposed control system.

References

• P. ŠUSTER and A. JADLOVSKÁ, "TRACKING TRAJECTORY OF THE MOBILE ROBOT KHEPERA II USING APPROACHES OF ARTIFICIAL INTELLIGENCE," Acta Electrotechnica et Informatica, vol. 11, no. 1, pp. 38-43, 2011.
• J. R. Ferreira and A. P. Moreira, "Non-Linear Model Predictive Controller for Trajectory of an Omni-Directional Robot Using a Simplified Model," in 9th Portuguese Conference on Automatic Control, Controlo, Coimbra, Portugal , 2010
• W. Bellil, C. B. Amar and A. M. Alimi, "Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Approximation for 1D, 2D Functions Approximation," PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY VOLUME, pp. 102-107, 2006.
• P. Cristea, R. Tuduce and A. Cristea, "Time Series Prediction with Wavelet Neural Networks," 5th Seminar on Neural Network Applications in Electrical Engineering, pp. 5-10, 2000.
• M. M. M. S and D. P. Satyanarayana, "FPGA Implementation of Hybrid Architecture for Image Compression Optimized for Low Power and High Speed applications," International Journal of Scientific & Engineering Research, vol. 4, no. 5, pp. 1931-1940, 2013.
• H. Chen, Y. Lu and L. Tu, "Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network," Shock and Vibration, vol. 20, no. 2, pp. 247-262, 2013.
• Y. HUANG and S. YU, "Explosive Ordnance Disposal Robot Path Planning Based on Danger Model Immune Wavelet Neural Network vol. 3, no. 11. 18," Advances in information Sciences and Service Sciences(AISS), 2011.
• T. Y. Abdalla and M. I. Hamzah, "Trajectory Tracking Control for Mobile Robot using Wavelet Network," International Journal of Computer Applications , vol. 74, no. 3, pp. 32-37, 2013.
• Q. Bai, "Analysis of Particle Swarm Optimization Algorithm," Computer and Information Science, vol. 3, no. 1, pp. 180-184, 2010.
• Z. A. Bashir and M. E. El-Hawary, "Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 24, no. 1, pp. 20-27, 2009.
• J. Chao, W. Bo and H. Youmin, "Wavelet Neural Network Based on NARMA¬L2 Model for Prediction of Thermal Characteristics in a Feed System," CHINESE JOURNAL OF MECHANICAL ENGINEERING, vol. 23, pp. 1-10, 2010.
• Y. Bodyanskiy, I. Pliss and O. Vynokurova, "ADAPTIVE WAVELET-NEURO-FUZZY NETWORK IN THE FORECASTING AND EMULATION TASKS," International Journal "Information Theories & Applications", vol. 15, pp. 47-54, 2008.
• S. Gholizadeh, "STRUCTURAL OPTIMIZATION FOR EARTHQUAKE LOADING WITH NONLINEAR RESPONSES BY SURROGATE MODELING BASED EVOLUTIONARY ALGORITHMS," ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING), vol. 11, no. 1, pp. 25-42, 2010.
• R. Siegwart, I. R. Nourbakhsh and D. Scaramuzza, Introduction to Autonomous Mobile Robots, London, England: The MIT Press Cambridge, Massachusetts, 2004.
• E. P. Mendes and A. A. D. Medeiros, "IDENTIFICATION OF QUASI-LINEAR DYNAMIC MODEL WITH DEAD ZONE FOR MOBILE ROBOT WITH DIFFERENTIAL DRIVE," in Latin American Robotics Symposium and Intelligent Robotics Meeting, 2010.
• K. D. Do, Z. -P. Jiang and J. Pan, "A global output-feedback controller for simultaneous tracking and stabilization of unicycle-type mobile robots," IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, vol. 2, no. 3, pp. 589-594, 2004.