A Self-Tuning Regulator for the High-Precision Position Control of a Linear Switched Reluctance Motor

In the high-technology mass manufacturing industry, high-speed and high-precision motion is an indispensable element in the automated production machines. In recent years, there has been a growing tendency to employ direct drive permanent magnet linear synchronous motors in demanding motion applications. Although the overall performance is good, its implementation cost remains high. This is mostly due to the cost of the Neodymium-Boron magnets, the manufacturing of the magnetic rails, and the precision of the overall mechanics. In this paper, a much cheaper alternative is proposed-to use a low-cost linear switched reluctance motor (LSRM) and an adaptive control strategy to overcome the tolerances and difficult control characteristics inherent in the motor. The LSRM has simple and robust structure, and it does not contain any magnets. However, its force is solely drawn from the reluctance change between the coil and the steel plates. Variations on the behavior of these two elements due to different operating conditions will change the motion behavior of the motor. Also, to keep the overall cost low, the LSRM sets a marginal mechanical tolerance during its mass production. This leads to characteristic variations in the final product. Finally, since the LSRM is a direct drive motor, any variations on the motor characteristics will directly reflect on the control system and the motion output. In this paper, a self-tuning regulator (STR) is proposed to combat the difficulties and uncertain control behaviors of the LSRM. This paper first introduces the motor winding excitation scheme, the model of the LSRM, and the current control method. The LSRM system is modeled as a single-input single-output discrete model with its parameters estimated by the recursive least square (RLS) algorithm. Then, an STR based on the pole placement algorithm is applied to the LSRM for high- performance position tracking. Both the simulation investigation and the experimental verification were conducted. In both cases, the results verified that the proposed RLS algorithm can estimate the parameters with fast convergence. The STR can provide quick response and high precision which is robust to the change of system parameters. Combined with STR control, the LSRM is a low-cost solution to fast, accurate, and reliable position tracking for many demanding motion control applications.