Adaptive Model-Based Control of Robotic Dynamic Systems with a Hybrid Approach combining Neural Networks and Fuzzy Logic

摘要

We describe in this paper a new method for adaptive model-based control of robotic manipulators using a new hybrid approach combining neural networks and fuzzy logic. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly non-linear. We describe an intelligent system for controlling robot manipulators to illustrate our new hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic systems.