Iterative Data-Driven Fractional Model Reference Control of Industrial Robot for Repetitive Precise Speed Tracking

Nowadays, industrial robots have been widely used in manufacturing applications; however, their speed is hard to control precisely due to unknown system dynamics. Instead of struggling to get an accurate explicit model, this paper addresses this challenge by proposing a new iterative data-driven fractional model reference control (FMRC) method, which tunes an adaptive controller to ensure that each robot actuation system behaves closely to a reference model with desirable system behavior to be obtained. This method utilizes input-output measurements without requiring an identified model or accessing the plant through specific experiments. A multiple degrees-of-freedom FMRC method with self-learning ability is designed to iteratively reach the optimal control parameters such that an accurate speed tracking is attained for each actuator. Constraints on the input signal are also considered to enhance the system robustness against external disturbances. The convergence, asymptotic accuracy, and stability of the designed control system are analyzed theoretically. Experimental results indicate that the proposed FMRC method is able to achieve a higher tracking precision and better robustness for the industrial robot compared with conventional methods.