Adaptive neuron for moment of inertia and torque control based on state observer

Abstract

In this article, a simple self-adaptive neuron based on a position state observer has been proposed to inhibit the influence of load disturbances and accomplish a self-adaptive correction of the moment of inertia. In the process of inertial correction, the error of the position state observer is gradually reduced that facilitates the simultaneous optimisation of the position observer and moment of inertia. Furthermore, it allows accurate prediction of the position value by the position state observer in the subsequent control cycle. Simulations and experiments indicate that the designed controller performs with better adaptive response and robustness compared to the controller that does not implement the proposed method, and that the position state observer estimates the position accurately, a response associated with numerous advantages for practical applications.