The knowledge-based fuzzy rules emulated network and its applications on direct adaptive on nonlinear control systems

This paper proposes an adaptive network architecture, which can emulate the human knowledge as the fuzzy logic rule, and its applications as the controller for nonlinear systems. The structure of this proposed network, multi-input Fuzzy Rule Emulated Network or FREN, is derived based on human knowledge in the form of fuzzy IF-THEN rules. The initial setting of its parameters can be intuitively chosen from expert's experience. During the learning phase based on the gradient search, the learning rate can be adapted itself to remain the stability with the Lyapunov method. The performance of our network is presented by using this network as controller for the single invert pendulum plant and the water bath temperature control system. The comparison results with other conventional control algorithms such as artificial neural networks and PID controllers can be illustrated in each example.