Coordinated Control of a Dual-Stage Positioning System Using Constrained Model Predictive Control

Abstract

Dual-stage positioning systems have been widely used in factory automation, robotic manipulators, high-density data storage systems, and manufacturing systems. Trajectory generation and control of dual-stage positioning systems is of great importance and is made complicated by the presence of physical and operational constraints. In this work, we describe how to generate feasible reference trajectories for a dual-stage positioning system consisting of a fine stage and a coarse stage, and how to use them in a model predictive control algorithm for which recursive feasibility is guaranteed. The reference generation algorithm is guaranteed to generate trajectories that satisfy all the constraints for the fine and coarse stages. We also describe a constrained model predictive control algorithm used to control the coarse stage. The simulation results of applying the developed methodology to track a pre-determined pattern is presented. ASME Proceedings -Advances in Motion ControlThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTDual-stage positioning systems have been widely used in factory automation, robotic manipulators, high-density data storage systems, and manufacturing systems. Trajectory generation and control of dual-stage positioning systems is of great importance and is made complicated by the presence of physical and operational constraints. In this work, we describe how to generate feasible reference trajectories for a dual-stage positioning system consisting of a fine stage and a coarse stage, and how to use them in a model predictive control algorithm for which recursive feasibility is guaranteed. The reference generation algorithm is guaranteed to generate trajectories that satisfy all the constraints for the fine and coarse stages. We also describe a constrained model predictive control algorithm used to control the coarse stage. The results of applying the developed methodology to track a pre-determined pattern is presented.

Reference and command governors for systems with constraints: A survey on theory and applications

“…Recently, reference governors have been applied to motion control of dual-stage processing machines for precision manufacturing [70,119]. Dual-stage machines feature for each processing axis a fast stage, with large bandwidth and high accelerations but limited processing range, and a slow stage, with small bandwidth and limited accelerations, but large range.…”

Repetitive model predictive control for precise control of complex trajectory tracking in dual-stage actuator systems

“…13,14 MPC is growing in importance as a design alternative for servo control in fast mechatronic systems, due to rapid development of microprocessors. 15–18 The MPC systems can be used in DSA control applications, such as set-pointing tracking 19,20 or contour tracking control 21 to improve the synchronization of motion in DSAs. In Chang and Tsao, 22 the researchers adopted a feedforward model predictive controller that takes into account practical constraints and actuator dynamic capabilities.…”

Model predictive control with tracking error bound and an influence function approach to moving horizon estimation

“…The potential application of the trajectory advisor is dual-stage automated tooling machines [23,45] where a slow stage with large workspace (also called operating range) moves a stage with much faster dynamics and limited workspace that actuates the tool. The fast stage stroke constraint requires the difference between the target position and the slow stage position to be within the range of the fast stage.…”

“…Consider a dual-stage automated tooling machine with the following continuous model for the x-axis of the slow stage [23,45].…”

“…In addition, we highlight that to achieve offset free tracking in the presence of unknown constant disturbance, incremental model should be used and the cost function suitably modified. The potential application of the trajectory advisor is dual-stage automated tooling machines [23,45] where the difference between the reference signal and the trajectory of the slow stage is required to be within a range.…”