Adjustment Strategy for Dynamic Tracking Neuro-Fuzzy Controller

Fuzzy logic, neural networks are two popular artificial intelligence techniques that are widely used in many applications. Due to their distinct properties and advantages, they are currently being investigated and integrated to form new models or strategies in the areas of system control. This paper presents an adjustment strategy for a dynamic tracking neuro-fuzzy controller (DTNC) for steady-state control in complex system. In this method, DTNC consists of two neural network composed of the same structure, one for control, one for learning. Strategies to adjust the DTNC in accordance with the environment dynamics are automatically generated in off-line manner in learning neural network. The generated strategies are stored in a neural network and with the changes of the external environment parameters, the learning part used for adjusting the DTNC in on-line. The other neural network has to be regarded as controller, and now tuned into a learning part. Therefore, the DTNC is automatically adjusted in accordance with the dynamics of complex environment using the generated strategies which are stored in two neural networks.