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.

This paper proposes a new structure for cross-coupling synchronous controller using an H _∞ control scheme to shape position commands for precise tracking in multiaxial motion control, together with its stability analysis. This proposed structure has the advantage that the controller has a simpler design process and more robust performance than conventional ones. The proposed controller is evaluated and compared simulated and experimentally with an unsynchronous controlled system and a conventional system. The simulated and experimental results show that the new structure reduces the position synchronous error remarkably. In addition, this new controller can easily be implemented on most currently used motion systems used by reprogramming the reference position command subroutine.

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.

Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human–machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.