Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.

I. Introduction

In modern manufacturing systems, the computer numerical controlled (CNC) machines have become important elements [1]–[3]. The CNC machines, generally, can be divided into two parts—the mechanical part with servo drive systems and the servo controller part that controls the multiaxis motion of the mechanical parts. In general, the CNC machine is composed of an – table and a -axis motion mechanism, where each motion axis is driven by an individual actuator system, such as dc or ac motors. However, the motion mechanisms using motor drives usually lead to the existence of the unmodeled dynamics, coupled interferences, and unmeasured friction, which often significantly deteriorate the system performance in machining processes [1]. Therefore, to improve the tracking performance in machining processes, many studies have been presented [4]–[6]. Moreover, the design of two-axis motion control with high-performance and high-precision machining is required in modern manufacturing. Since the direct-drive design of mechanical applications based on a permanent-magnet linear synchronous motor (PMLSM) is a viable candidate to meet the increasing demands for higher contouring accuracy at high machine speeds [7]–[10], the motion control of an – table composed of two PMLSMs is considered in this paper. Although the direct drive design based on a PMLSM has many advantages over its indirect counterpart [7], the servo performance of the PMLSM is greatly affected by the uncertainties, including parameter variations, external disturbances, and unmodeled dynamics in the drive system because no auxiliary mechanisms, such as gears or ball screws, were equipped. Furthermore, since the operation of the PMLSM involves two contacting bodies, nonlinear friction force is inevitably among the forces of motion. The friction characteristic may be easily varied due to the change of normal forces in contact, temperature, and humidity [11], [12]. Furthermore, in a closed-loop control system, the nonlinear friction force often results in a steady-state error, a limit cycle, and a low bandwidth.