Neurofuzzy control applied to multiple cooperating robots

• Published in: Industrial robot 2005-06, Vol.32 (3), p.234-239
• Main Authors: Kumar, Manish, Garg, Devendra P.
• Format: Article
• Language: English
• Subjects: Fuzzy control; Neural nets; Robotics
• ISSN: 0143-991X
• DOI: 10.1108/01439910510593929

Purpose The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neurofuzzy scheme to control two cooperating robots. Designmethodologyapproach The paper presents a special neural network architecture that can be converted to fuzzy logic controller. Concepts of model predictive control MPC have been used to generate optimal signal to be used to train the neural network via backpropagation. Subsequently, a trained neural network is used to obtain fuzzy logic controller parameters. Findings The proposed neurofuzzy scheme is able to precisely learn the control relation between inputoutput training data generated by the learning algorithm. From the experiments performed on the industrial grade robots at Robotics and Manufacturing Automation RAMA Laboratory, it was found that the neurofuzzy controller was able to learn fuzzy logic rules and parameters accurately. Research limitationsimplications The backpropagation method, used in this research, is extremely dependent on initial choice of parameters, and offers no mechanism to restrict the parameters within specified range during training. Use of alternative learning mechanisms, such as reinforcement learning, needs to be investigated. Practical implications The neurofuzzy scheme presented can be used to develop controller for plants for which it is difficult to obtain analytical model or sufficient information about inputoutput heuristic relation is not available. Originalityvalue The paper presents the neural network architecture and introduces a learning mechanism to train this architecture online.