Enhanced sensitivity shaping by data-based tuning of disturbance observer with non-binomial filter. (February 2019)

• Record Type: Journal Article
• Authors: Li, Xiaocong; Chen, Si-Lu; Teo, Chek Sing; Tan, Kok Kiong
• Is Part Of: ISA transactions. Volume 85(2019)
• Page Start: 284
• Page End: 292
• Publication Date: 2019-02
• Subjects: Data-based tuning -- Disturbance observer -- Robust control -- Autotuning -- Gradient-based optimization -- Vibration suppression -- Loop-shaping; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2018.10.023
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This work presents a new method of sensitivity shaping for motion control systems with disturbance observer (DOB). Although the traditional DOB design has been proved to be an effective means of rejecting disturbances in many applications, shaping of the closed-loop sensitivity is constrained since the Q -filter in DOB is usually restricted to a standard Butterworth or binomial form, where the cutoff frequency is the only tunable parameter. To overcome this limitation and achieve better performance for more demanding motion systems, a general form of the Q -filter is employed in this work. An data-based iteratively tuning procedure is subsequently developed according to a pre-defined cost function, so that the disturbance rejection in the low-frequency band is improved while the high-frequency noise attenuation and 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 advantage of this data-based sensitivity shaping approach. Highlights: Additional freedom is given to the design of disturbance observer by importing non-binomial filter. A cost function is proposed to reflect both performance and robustness requirements. Parameters are iteratively tuned based on the unbiased estimate of the gradient. Performance improvement can be clearly observed in both frequency and time domains as validation.