In this work procedures for neuro-fuzzy modelling and control of dynamic systems are reviewed and a new structure is proposed. In this, modelling and closed-loop control are performed simultaneously by using a neuro-fuzzy approach. In the modelling stage the input space of a dynamic system (plant) is initially divided into a number of fuzzy operating regions within which reduced order models are able to represent the system. The complete system model output - the global model - is obtained through the conjunction of the outputs of the local models. A new structure, called Neuro-Fuzzy Controller with Variable Coefficients (NFCVC) is proposed and evaluated. Its main objectives are to improve the system s robustness and to provide automatic generation of the manipulated variable in order to overcome a difficulty of neural and neuro-fuzzy controllers in general. The NFCVC is originated from models proposed by Mellem (1997) and Velloso (1999) and makes use of neuro-fuzzy networks to generate variable coefficients of an ARMA model. Despite combining times series models with a neuro-fuzzy approach, the main function of NFCVC is to perform the control of the plant.In order to evaluate the performance of NFCVC two well-known neuro-fuzzy controllers - FALCON-H (Fuzzy Adaptive Learning Control Network with Hybrid Learning) and the NEFCON (Neuro-Fuzzy Controller) - as well as the traditional PID controller are used as means of comparison.A linear plant (Rotor Winder), a linearized plant (Inverted Pendulum) and a nonlinear plant (%CO2) are used in the experiments. These plants are well-known and generally used in practical applications and/or academic works. The results for the NFCVC are analyzed and compared to those obtained with the others structures. Finally, conclusions and suggestions for future work are presented.