ABSTRACT
This chapter presents a new method of sensitivity shaping for motion control systems with disturbance observer. Although the traditional disturbance observer design has been proved to be an effective approach of rejecting disturbances in many applications, shaping of the closed-loop sensitivity is not optimized since the Q-filter in the disturbance observer is usually restricted to a standard Butterworth or binomial form, where the cut-off frequency is the only adjustable parameter. To overcome this limitation and achieve better performance for industrial motion systems, a general form of the Q-filter is employed in this work. Data-based iterative optimization procedures are subsequently developed according to a pre-defined cost function, such that the disturbance rejection in the low-frequency band is improved while the high-frequency noise attenuation and the robust stability are not compromised. This method is data-based in the sense that the sensitivity shaping process is conducted based on the input-output data collected from the operating motion system, instead of relying on the identified system model. Simulation and experimental results are presented to show the advantages of this data-based sensitivity shaping optimization approach.
