Unfalsified Adaptive Control of a Robotic System

The paper presents a data-driven control method based on the concept of unfalsified control theory. The controller design presented is an adaptive learning-based technique. In this paper, an effective parameter update method is presented by which the system can quickly adapt to parametric changes and uncertainties while fulfilling the desired performance criteria. The method is applied to a robotic arm with unknown parameters for trajectory tracking. The design is tested through simulation in MATLAB/SIMULINK environment, and the effectiveness can be proved by the results presented in the paper.