Adaptive inverse control with IMC structure implementation on robotic arm manipulator

In this paper, an adaptive inverse control with internal model control (IMC) structure is proposed and implemented on a robotic arm manipulator system. The plant is stabilized using a simple lead-lag controller and the inverse of the plant is estimated using normalized least mean square (nLMS) algorithm. Radial base transfer function is used as an input mask to the adaptive algorithm. A delayed version of the reference signal is compared with the plant output to produce the error for the adaptive algorithm. The error signal is masked by a hyperbolic tangent sigmoid transfer function and the learning rate is adjusted automatically. A rate limiter is used in the model identification part to eliminate oscillatory plant output behavior. Comparison between adaptive inverse control and IMC structure is implemented and results are shown to demonstrate the effectiveness of the proposed method.