Generation of efficient adjustment strategies for a fuzzy-neuro force controller using genetic algorithms - Application to robot force control in an unknown environment

This paper presents an effective generation method of adjustment strategies for a fuzzy-neuro force controller (FNFC) of a robot manipulator in an unknown environment. In this method, strategies to adjust the FNFC in accordance with the environment dynamics are automatically generated in off-line manner using genetic algorithms (GA). The generated strategies are stored in a neural network and used for adjusting the FNFC in on-line. Therefore, the FNFC is automatically adjusted in accordance with the unknown dynamics of an environment using the generated strategies which are stored in the neural network. Fuzzy fitness evaluation method is proposed for the effective evolution of the neural network in the GA process. The effectiveness of the generated adjustment strategies of the FNFC has been evaluated by computer simulation with a 3DOF robot manipulator model.