Data-Driven Robust Iterative Learning Predictive Control for MIMO Nonaffine Nonlinear Systems With Actuator Constraints

Zhang, Chong; Hu, Yunfeng; Xiao, Lin; Gong, Xun; Chen, Hong

doi:10.1109/tii.2024.3390444

Cited by 1 publication

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Presently, ball screw (BS) feed drives are the predominant choice in most industrial feed drive systems. A typical ball screw feed drive in CNC machine tools and its corresponding two-mass model are illustrated in Figure 2 a,b [ 25 ]. Due to the flexible coupling and the long shaft between the motor and the table, this mechanism can be modeled as a flexible two-mass system [ 37 ].…”

Section: Flexible Feed Drive and Problem Formulationmentioning

confidence: 99%

“…Over the past few years, the ILC field has witnessed significant advancement. For instance, Zhang et al [ 25 ] proposed a data-driven ILC combined with predictive control to address the multivariable tracking problem with actuator constraints. This approach transforms the problem into an iteration-varying quadratic programming problem using only the system’s I/O data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multivariable Iterative Learning Control Design for Precision Control of Flexible Feed Drives

Wang,

Hsiao

2024

Sensors

View full text Add to dashboard Cite

Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position loop, aiming to prevent insufficient stability and premature wear and damage of components. This paper introduces a multivariable iterative learning control (MILC) method tailored for flexible feed drive systems, focusing on enhancing dynamic positioning accuracy. The MILC employs error data from both the motor and table sides, enhancing precision by injecting compensation commands into both the reference trajectory and control command through a norm-optimization process. This method effectively mitigates conflicts between feedback control (FBC) and traditional iterative learning control (ILC) in flexible structures, achieving smaller tracking errors in the table side. The performance and efficacy of the MILC system are experimentally validated on an industrial biaxial CNC machine tool, demonstrating its potential for precision control in modern machining equipment.

show abstract

Section: Flexible Feed Drive and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%