High accuracy motion control is a subtle subject, and many critical aspects are related to the non-idealities of both the actuators and the mechanical device to be moved. Based on the multi-year experience gained in a hands-on laboratory, involving tens of Mechatronics' master students at the University of Padova, it has been possible to set up (in a Matlab/Simulink environment) a realistic simulator of a servo positioner, accounting for most of the characteristics of an actual system. The typical laboratory experience starts with the identification of the relevant physical parameters of the system to be controlled (each student receives a different encrypted model of the system to be controlled), followed by the design of controllers and disturbance observers in a deterministic setting. Then, a stochastic approach is applied, with the design and experimental tuning of a Kalman filter. To improve the tracking and disturbance rejection of the controlled system, it is also studied the application of advanced techniques like Repetitive Control, Zero-Phase Tracking Error Control, Iterative Learning Control. As a result, it has been possible to get the full involvement of each student even in Covid-19 time, with results that closely matched those obtained in the previous hands-on experience.
E-Teaching High Accuracy Motion Control Techniques in Covid-19 time
Oboe R.;Michieletto G.
2021
Abstract
High accuracy motion control is a subtle subject, and many critical aspects are related to the non-idealities of both the actuators and the mechanical device to be moved. Based on the multi-year experience gained in a hands-on laboratory, involving tens of Mechatronics' master students at the University of Padova, it has been possible to set up (in a Matlab/Simulink environment) a realistic simulator of a servo positioner, accounting for most of the characteristics of an actual system. The typical laboratory experience starts with the identification of the relevant physical parameters of the system to be controlled (each student receives a different encrypted model of the system to be controlled), followed by the design of controllers and disturbance observers in a deterministic setting. Then, a stochastic approach is applied, with the design and experimental tuning of a Kalman filter. To improve the tracking and disturbance rejection of the controlled system, it is also studied the application of advanced techniques like Repetitive Control, Zero-Phase Tracking Error Control, Iterative Learning Control. As a result, it has been possible to get the full involvement of each student even in Covid-19 time, with results that closely matched those obtained in the previous hands-on experience.
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