RBFNN-based Control Design Methodology for a DC Servo Motor with a Lyapunov Stability

The paper presents a methodology for the design of adaptive control using radial basis functions neural networks to compensate for the influence of changes in plant parameters and disturbances. The stability of a closed loop system with a neural network adapter is achieved by choosing the appropriate Lyapunov function. A DC servo motor model that takes into account viscous friction is considered as a plant.