Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods
Szilveszter Pletl* and Bela Lantos**
*Institute of Electro-Mechanical Systems Neuro-Fuzzy-Genetic Intelligent Control Research Center, 24000 Subotica, Marka Oreskovica 16, Yugoslavia
**Department of Control Engineering and Information Technology Budapest University of Technology and Economics H-1117 Budapest, Pazmany Peter setany 1/D, Hungary
Received:January 14, 2001Accepted:February 20, 2001Published:March 20, 2001
Keywords:Genetic algorithms, Fuzzy logic, Neuro-fuzzy modeling, Flexible joint robots
Soft computing (fuzzy systems, neural networks and genetic algorithms) can solve difficult problems, especially non-linear control problems such as robot control. In the paper two algorithms have been presented for the nonlinear control of robots. The first algorithm applies a new neural network based controller structure and a learning method with stability guarantee. The controller consists of the nonlinear prefilter, the feedforward neural network and feadback PD controllers. The fast learning algorithm of the neural network is based on Moore-Penrose pseudoinverse technique. The second algorithm is based on a decentralized hierarchical neuro-fuzzy controller structure. New approach to evolutionary algorithms called LEGA optimizes the controller during the teaching period. LEGA combines the standard GA technique with numerical optimum seeking for a limited number of elite individuels in each generation. It can lead to global optimum in few generations. The soft computing based nonlinear control algorithms have been applied for the control of a rigid link flexible joint (RLFJ) 4 DOF SCARA robot in order to prove the effectiveness of the proposed methods.
Cite this article as:S. Pletl and B. Lantos, “Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods,” J. Adv. Comput. Intell. Intell. Inform., Vol.5 No.2, pp. 81-89, 2001.Data files: