A novel nearo-based model reference adaptive control for a two link robot arm

This paper presents a novel neuro-adaptive control for a two link robot arm. The new learning algorithm guarantees the stability of a class of closed-loop neural network control systems. The underlying control system, here a robot aim, represents a non-linear system. The neuro-controller, which indeed represents a direct adaptive controller, guarantees the closed loop stability for any arbitrary initial values of´ states, neural network parameters and any unknown-but-bounded disturbances, provided that some soft conditions are satisfied. No additional controllers or robustifying terms are needed. Neural network weight matrices are adapted online with no initial offline training. Extensive simulation investigations demonstrate the excellent performance of the novel neuro-adaptive control scheme for robot arm system.