Abstract
Linear motors (LMs) are widely used in numerous industry automation where precise and fast motions are required to convert electric energy into linear actuation without the need of any switching mechanism. This study aims to develop a control strategy of auto-tuning cross-coupled two-degree-of-freedom proportional-integral-derivative (ACC2PID) to achieve extremely high-precision contour control of a LMs-driven X-Y-Y stage. Three 2PID controllers are developed to control the mover positions in individual axes while two compensators are designed to eliminate the contour errors in biaxial motions. Furthermore, an improved artificial bee colony algorithm is employed as a powerful optimization technique so that all the control parameters can be concurrently evaluated and optimized online while ensuring the non-fragility of the proposed controller. In this way, the tracking error in each axis and contour errors of the biaxial motions can be concurrently minimized, and further, satisfactory positioning accuracy and synchronization performance can be achieved. Finally, the experimental comparison results confirm the validity of the proposed ACC2PID control system regarding the multi-axis contour tracking control of the highly uncertain and nonlinear LMs-driven X-Y-Y stage.
Highlights
Linear motors (LMs) have been widely applied in extremely high-precision manufacturing occasions, such as lithography machines [1], machine tools [2], and industry gantry [3,4]
The control performance especially in terms of high-precision of LMs is potentially affected by various nonlinear factors, such as force ripple and friction of the LMs [7,8,9]
The proposed auto-tuning cross-coupled 2PID (ACC2PID) control scheme is tested on a LMs-driven X-Y-Y stage, as shown in attaching a floating-point PowerPC 400-MHz processor and a VxWorks real-time operating system was used as the control core
Summary
Linear motors (LMs) have been widely applied in extremely high-precision manufacturing occasions, such as lithography machines [1], machine tools [2], and industry gantry [3,4]. Due to the prominent advantages of high speed, large pushing force, and high precision, the studies of LMs always attract much attention from various fields, such as control engineering and industry automation. The control performance especially in terms of high-precision of LMs is potentially affected by various nonlinear factors, such as force ripple and friction of the LMs [7,8,9]. Heydarzadeh et al use neural networks to estimate the friction and force ripple of LMs [12]. Implementing the above-mentioned control approach requires proper tuning for the controller gains to achieve the good performance
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