This paper proposes an adaptive TS recurrent fuzzy CMAC (TS-RFCMAC) model based control of uncertain time-delay nonlinear systems. First, we introduce a TS-RFCMAC network and its application on system modeling. Next, a TS-RFCMAC controller is developed based on parallel distributed compensation and adaptive control laws. Even if uncertain local subsystem matrices and fuzzy sets exist in the mod...

A kind of recurrent fuzzy cerebellar model articulation controller (RFCMAC) model is presented. The recurrent network is embedded in the RFCMAC by adding feedback connections on the first layer to embed temporal relations in the network. A nonconstant differentiable Gaussian basis function is used to model the hypercube structure and the fuzzy weight. A gradient descent learning algorithm is us...

A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a si...

Neural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent...

An improved universal parallel recurrent neural network canonical architecture, named Recurrent Trainable Neural Network (RTNN), suited for state-space systems identification, and an improved dynamic back-propagation method of its learning, are proposed. The proposed RTNN is studied with various representative examples and the results of its learning are compared with other results,, given in t...

An improved universal parallel recurrent neural network canonical architecture, named Recurrent Trainable Neural Network (RTNN), suited for state-space systems identification, and an improved dynamic back-propagation method of its leaming, are proposed. The proposed R T " is studied with various representative examples and the results of its learning are compared with other results,, given in t...

In this paper, existence and uniqueness of the solution of interval fuzzy Cohen-Grossberg neural networks with piecewise constant argument are discussed. Based on the comparison principle, it presents new theoretical results on the global robust exponential stability of interval fuzzy Cohen-Grossberg networks with piecewise constant argument. As a special case, the corresponding results of inte...