Contour error control for the biaxial system based on active disturbance rejection generalised prediction

ABSTRACT

For the purpose of contour error control in two-axis systems, a cross-coupled controller utilising auto-disturbance rejection generalised predictive control and nonlinear proportional differentiation is proposed. The contour error components along each axis are estimated by utilising the information from the nearest reference point and its adjacent reference points. The combination of ADRC and GPC is utilised for uniaxial tracking control. The LESO is utilised for estimating and compensating for the total disturbance of the system while simplifying it into two cascaded integrators. Because GPC is designed only for the series form of the integrator, the dependence on mathematical models is reduced. A nonlinear cross-coupled controller is designed utilising the fal function, with compensation control quantities for each axis directly obtained from their respective contour error components. Finally, the results obtained from the simulation demonstrate that the control strategy can availably enhance the performance of single-axis tracking and contour processing.

Highlights

• Innovative control strategy: This abstract introduces a new controller that combines ADRC-GPC and nonlinear proportional differentiation to enhance contour error control in two-axis systems.

• Data-driven estimation: Our controller estimates contour errors along each axis using nearby reference points, reducing the need for complex mathematical models.

• Simplified disturbance compensation: By utilizing LESO, disturbance estimation and compensation are simplified, resulting in a more robust control system with two cascaded integrators.

• Enhanced performance: Simulation results confirm the effectiveness of this strategy in improving single-axis tracking and contour processing, making it valuable for precision control applications.

KEYWORDS:

• Contour error control
• ADRC
• GPC
• nonlinear PD
• cross-coupled control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The authors will provide the raw data that supports the conclusions of this article without any undue reservation.

Additional information

Funding

This article is supported by Key R & D Program in Shaanxi Province [grant number 2020GY-129]; Key Scientific Research Project of Shaanxi Provincial Department of Education [grant number 21JS033]; Xi’an Science and Technology Planning project [grant number 22FWQY06].

Notes on contributors

Guozhu Li

Guozhu Li is an associate professor in the School of Mechanical and Material Engineering at Xi'an University, received the Master in control Theory and Control Engineering from China Hohai University in 2004. His research interests include intelligent manufacturing and motion control.

Junhui Liu

Junhui Liu received the B.S. degrees in Automation from Tianjin University of Commerce, Tianjin, China, in 2008. She received the M.S. degrees in Mechanical Design and Theory from Xidian University, Xi'an, China, in 2013. She received the Ph.D. degree in control theory and control engineering from Xidian University, Xi'an, China, in 2020. She joined the School of Mechanical and Material Engineering, Xi'an University, Xi'an, China, in 2021, where she is currently an Associate Professor. Her main research interests include intelligent manufacturing and energy management control of mechatronic systems.