Realization of a Control IC for PMLSM Drive Based on FPGA Technology

The advantages of superior power density, high-performance motion control with fast speed and better accuracy, are such that PMLSM (Permanent Magnet Linear Synchronous Motor) are being increasingly used in many automation control fields as actuators (McLean, 1988; Gieras & Piech, 2000; Budig, 2000;), including computer-controlled machining tools, X-Y driving devices, robots, semiconductor manufacturing equipment, etc. However, the PMLSM does not use conventional gears or ball screws, so the payload upon the mover greatly affects the positioning performance (Liu et al., 2004). To cope with this problem, many advanced control techniques (Qingding et al., 2002; Lin et al., 2007; Wai & Chu, 2007), such as fuzzy control, neural networks control and robust control have been developed and applied to the position control of the PMLSM drive to obtain high operating performance. However, the execution of a neural network or fuzzy controller requires many computations, so implementing these highly complex control algorithms depend on the PC systems in most studies before (Qingding et al., 2002; Liu et al., 2004). In recent years, the fixed-point DSP (Digital Signal Processor) and the FPGA (Field Programmable Gate Array) provide a possible solution in this issue (Lin et al., 2005; Kung, 2008). Comparing with FPGA, although the intelligent control technique using DSP provides a flexible skill, it suffers from a long period of development and exhausts many resources of the CPU. Nowadays, the FPGA has brought more attention before. The advantages of the FPGA includes their programmable hard-wired feature, fast time-to-market, shorter design cycle, embedding processor, low power consumption and higher density for the implementation of the digital system (Cho, et al., 2009; Monmasson & Cirstea, 2007; Naouar et al., 2007; Kung & Tsai, 2007; Jung & Kim, 2007; Huang & Tsai, 2009; Kung, et al., 2009). FPGA provides a compromise between the special-purpose ASIC (application specified integrated circuit) hardware and general-purpose processors (Wei et al., 2005). Recently, Li et al. (2003) utilized an FPGA to implement autonomous fuzzy behavior control on mobile robot. Lin et al., (2005) presented a fuzzy sliding-mode control for a linear induction motor drive based on FPGA. But, due to the fuzzy inference mechanism module adopts parallel processing circuits, it consumes much more FPGA resources; therefore limited fuzzy rules are used in their proposed method. To solve the aforementioned problem, this work firstly proposed to use an FPGA and an embedded NiosII processor to develop a control IC for PMLSM. The control IC has two modules. One module performs the functions of the PTP motion trajectory for PMLSM. The