Tracking Error Suppression of Maglev Planar Actuator With FPGA-Based MPC

The precision positioning equipment is essential in many manufacture areas. This paper develops a novel noncontact six-degree-of-freedom maglev planar actuator (MLPA) for applications in precision manufacturing area. To achieve the tracking and positioning of MLPA, a model predictive control (MPC) algorithm is introduced. However, the high frequency feedback control of maglev system up to kilohertz level poses the challenges in the online optimization of MPC. We argue that the field-programmable gate array (FPGA), a high throughput and low latency processor, accelerates the computation of MPC, and the High-Level synthesis (HLS) tool simplifies the implementation of the optimization algorithm, which is the most complex module in MPC. In this work, the dual-gradient projection method is introduced to realize the online optimization with its characteristics of parallelism. Benefiting from the parallel architecture of FPGA, the dual-gradient projection method successfully runs in the millisecond level with the acceleration performance highlighted by comparing with active set and interior point methods. Furthermore, the FPGA-based MPC is deployed for MLPA to inhibit the trajectory tracking error and improve the step response speed. The synthesis of the proposed FPGA-based MPC scheme indicates the prediction horizon of MPC is able to theoretically reach about 20, and the experiments undertaken on the real-time system shows that tracking performance of MLPA is improved with the prediction horizon expanding.