Po-Huan Chou

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.

DSP-Based Cross-Coupled Synchronous Control for Dual Linear Motors via Intelligent Complementary Sliding Mode Control

A digital signal processor (DSP)-based cross-coupled intelligent complementary sliding mode control (ICSMC) system is proposed in this paper for the synchronous control of a dual linear motor servo system. The dual linear motor servo system with two parallel permanent magnet linear synchronous motors is installed in a gantry position stage. The dynamic model of single-axis motion control system with a lumped uncertainty, which comprises parameter variations, external disturbances, and nonlinear friction force, is introduced first. Then, to achieve an accurate trajectory tracking performance with robustness, a cross-coupled ICSMC is developed. In this approach, a Takagi-Sugeno-Kang-type fuzzy neural network estimator with accurate approximation capability is implemented to estimate the lumped uncertainty. Moreover, since a cross-coupled technology is incorporated into the proposed intelligent control scheme for the gantry position stage, both the position tracking and synchronous errors of the dual linear motors will simultaneously converge to zero. Furthermore, to effectively demonstrate the control performance of the proposed intelligent control approach, a 32-b floating-point DSP-based control computer is developed for the implementation of the proposed cross-coupled ICSMC system. Finally, some experimental results are illustrated to show the validity of the proposed control approach.

Robust Adaptive Backstepping Motion Control of Linear Ultrasonic Motors Using Fuzzy Neural Network

A robust adaptive fuzzy neural network (RAFNN) backstepping control system is proposed to control the position of an X-Y-Theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. First, an X-Y-Theta motion control stage is introduced. Then, the single-axis dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which includes cross-coupled interference and friction force, is derived. Moreover, a conventional backstepping approach is proposed to compensate the uncertainties occurred in the motion control system. Furthermore, to improve the control performance in the tracking of the reference contours, an RAFNN backstepping control system is proposed to remove the chattering phenomena caused by the sign function in the backstepping control law. In the proposed RAFNN backstepping control system, a Sugeno-type adaptive fuzzy neural network (SAFNN) is employed to estimate the lumped uncertainty directly and a compensator is utilized to confront the reconstructed error of the SAFNN. In addition, the motions at the X axis, Y axis, and Theta axis are controlled separately. The experimental results show that the contour tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained, as well using the proposed RAFNN backstepping control system.

Three-Degree-of-Freedom Dynamic Model-Based Intelligent Nonsingular Terminal Sliding Mode Control for a Gantry Position Stage

A three-degree-of-freedom (3-DOF) dynamic model-based intelligent nonsingular terminal sliding mode control (INTSMC) system is proposed in this study for the precision contours tracking of a gantry position stage. A Lagrangian equation-based 3-DOF dynamic model for the gantry position stage is derived first. Then, to minimize the synchronous error and tracking error in the precision contours tracking, the 3-DOF dynamic model-based INTSMC system is proposed. In this approach, a nonsingular terminal sliding mode control is designed for the gantry position stage to achieve finite time tracking control. Moreover, to increase the robustness and to improve the control performance, an interval type-2 recurrent fuzzy neural network, and asymmetric membership function, which combines the advantages of interval type-2 fuzzy logic system, recurrent neural network, and asymmetric membership function, is developed as an estimator to approximate a lumped uncertainty. Finally, some experimental results of the gantry position stage for optical inspection application are obtained to show the validity of the proposed control approach.

SELF-CONSTRUCTING SUGENO TYPE ADAPTIVE FUZZY NEURAL NETWORK FOR TWO-AXIS MOTION CONTROL SYSTEM

Abstract A self‐constructing Sugeno type adaptive fuzzy neural network (SAFNN) control system is proposed in this study for the contour tracking control of a two‐axis motion control system. The adopted two‐axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The proposed SAFNN combines the merits of a self‐constructing fuzzy neural network (SCFNN) and a TSK‐type fuzzy inference mechanism. Moreover, the structure and the parameter learning phases are performed concurrently and on line in the SAFNN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP‐based control computer. From the simulated and experimental results, the contour tracking performance of the two‐axis motion control system is significantly improved and robustness can be obtained as well using the proposed SAFNN control system.