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Radial Bias Neural Network (RBNN), Particle Swarm Optimization (PSO), Evolutionary Neural Network (ENN), Hybrid Intelligent Controller, Non-Linear Control Systems
In this paper, an Evolutionary Optimized Neural Network (EONN) based control scheme is proposed. This control scheme is based on the fact that optimizing values of a few parameters of neural network can enhance its control performance. Radial Biased Neural Network (RBNN) is chosen here and PSO, one of the most emerging global optimizing techniques, is used to optimize the parameters of a RBNN. From hidden to output layer RBNN uses Gaussian function for mapping. Spread factor (s) of this intelligent RBNN is then optimized by a modified PSO to improvise its performance. The proposed controller has been verified by implementing it for position control of a robotic manipulator. For comparison purpose, proposed scheme has been verified with RBNN and the classical PD controller. MATLAB environment has been chosen for simulation study carried out. Robustness of the proposed controller has been checked by applying it to the manipulator for three different paths.
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. DOI:10.5815/ijisa.2014.12.02
Yingqi C., Songyu Y. and Yi Z., “Combination of Evolutionary Computational and Artificial Neural Network”. Infrared and Laser Engineering, 28(4): 6-91, 1999.
Lewis F. L., Jagannathan S. and Yesildirek A., Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor & Francis, 1998.
The Berkeley Institute in Soft Computing. [Online]. Available: http://www-bisc.cs.berkeley.edu.
Yao X., Evolutionary Artificial Neural Networks. Int. J. of Neural Systems, 4(3): 539-567, 1993.
Muhlenbein H., Limitations of Multi-Layer Preceptron Networks-Steps Towards Genetic Neural Networks. Parallel Computing, (14): 249-260, 1990.
Baluja S., Evolution of Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Trans. On SMC. (26): 450-463, 1996.
Kuo T. C. and Huang Y. J., Global Stabilization of Robot Control with Neural Network and Sliding Mode. Engineering Letters, 16(1): EL_16_1_09.
Sudheer C., Maheswaran R., Panigrahi B.K. and Mathur S., A Hybrid SVM-PSO Model for Forecasting Monthly Streamflow. Neural Comput. & Applic., DOI 10.1007/s00521-013-1341-y. Feb, 2013.
Kennedy J. and Eberhart R., Particle Swarm Optimization. IEEE Int. Conf. Neural Networks: 1942-1948, 1995.
Clerc M., The Swarm and the Queen: Toward a Deterministic and Adaptive Particle Swarm Optimization. IEEE Int. Congr. Evolutionary Computation, vol. 3: 1957, 1999.
Clerc M. and Kennedy J., The Particle Swarm- Explosion, Stability and Convergence in a Multi-Dimensional Complex Space. IEEE Trans. Evol. Comput., vol. 6: 58-73, Feb. 2002.
Kapoor N. and Ohri J., Various Methods of a Manipulator Control: Trending Towards Future. National Conference on Advances in Videos, Cyber Learning and Electronics (ADVICE 2012), at NITTTR, Chd.: 19, March 2012.
Kapoor N. and Ohri J., A Neural Network Based Novel Approach for Error Optimization in Path Tracking Control of a Robotic Manipulator. National Conference, AEMDS-2013, at TERII, Kurukshetra: 98-103, 2013.
Kapoor N. and Ohri J., Fuzzy Sliding Mode Controller (FSMC) with Global Stabilization and Saturation Function for Tracking Control of a Robotic Manipulator. Journal of Control and Systems Engineering. 1(2): 50-56, 2013.
Spong M W, Vidyasagar M., Robot Dynamics and Control. Wiley-India Edition. New York.
Ratnaweera A., Halgamuge S. K. and Watson H. C., Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Trans. on Evol. Comput., 8(3): 240-255, June 2004.
Khan K. and Sahai A. “A Glowworm Optimization Method for the Design of Web Services”: I.J. Intelligent Systems and Applications, 10, 89-102,2012.