Mei-Yung Chen

This study proposes applying adaptive incremental sliding mode control (AISMC) to a dual-axis core-type permanent magnet servo synchronous linear motor to establish a high-precision dual-axis motion control platform. First, this study uses the magnetic field-oriented theorem to convert the three-phase control current of the drive motor into the d-q axis control current and integrates it with the magnetic thrust equation and mechanical model of the linear motor to obtain the biaxial linear dynamic equations of the motor platform. However, many external disturbances will be encountered during the operation of the motor, such as friction, ripple effects, and variations in the internal parameters of the system. We collectively refer to these disturbances as system uncertainties and incorporate them into the dual-axis linear motor platform design. Considerations are made within the dynamic equation to establish a more refined dynamic equation. Then, a high-order controller can be designed based on the system dynamic equation established above. In the controller design stage, we will first design the SMC. Due to its simple structure and high robustness, it is very suitable for uncertain systems such as linear motors. In many systems, its shortcoming is the chattering phenomenon in the smooth mode. To improve this phenomenon, we designed AISMC. Its feature is that the past control input will be considered when designing the controller to suppress chattering. The vibration phenomenon and adaptive control are used to compensate for system uncertainty. Finally, experiments prove that this research successfully improves the precision of the dual-axis motion platform.

In this paper, we propose a general method for designing Takagi–Sugeno (T-S) fuzzy model controllers, applicable to a general class of nonlinear systems represented in state-space form. The method is an automated controller design process that introduces the BLOCK concept. Through the automatic division of BLOCKs, the system is divided into more subsystems, and corresponding fuzzy rules and membership functions are automatically generated, significantly shortening the development time for systems with known system models. According to T-S fuzzy theory, nonlinear systems are decomposed into multiple linear subsystems governed by fuzzy rules. Unlike conventional methods that rely on linear matrix inequalities (LMI), which may suffer from infeasibility or excessively large controller gains and generally involve higher computational complexity, we integrate the linear quadratic regulator (LQR) approach to enhance stability and performance. The LQR method offers a more computationally efficient solution while still achieving effective control. The effectiveness of the proposed automated process is demonstrated through its application to a two-link robotic manipulator, showcasing its ability to improve tracking accuracy. Experimental results confirm that the proposed controller outperforms conventional PID control, achieving reduced tracking errors and demonstrating the practicality of the method for broader nonlinear control applications.

The objective of this study is to develop an Augmented Reality (AR) visual aid system to help patients with prosopagnosia recognize faces in social situations and everyday life. The primary contribution of this study is the use of 3D face models as the basis of data augmentation for facial recognition, which has practical applications for various social situations that patients with prosopagnosia find themselves in. The study comprises the following components: First, the affordances of Active Stereoscopy and stereo cameras were combined. Second, deep learning was employed to reconstruct a detailed 3D face model in real-time based on data from the 3D point cloud and the 2D image. Data were also retrieved from seven angles of the subject’s face to improve the accuracy of face recognition from the subject’s profile and in a range of dynamic interactions. Second, the data derived from the first step were entered into a convolutional neural network (CNN), which then generated a 128-dimensional characteristic vector. Next, the system deployed Structured Query Language (SQL) to compute and compare Euclidean distances to determine the smallest Euclidean distance and match it to the name that corresponded to the face; tagged face data were projected by the camera onto the AR lenses. The findings of this study show that our AR system has a robustness of more than 99% in terms of face recognition. This method offers a higher practical value than traditional 2D face recognition methods when it comes to large-pose 3D face recognition in day-to-day life.

A real-time path planning algorithm based on the Markov decision process (MDP) is proposed in this paper. This algorithm can be used in dynamic environments to guide the wheeled mobile robot to the goal. Two phases (the utility update phase and the policy update phase) constitute the path planning of the entire system. In the utility update phase, the utility value is updated based on information from the observable environment. Obstacles and walls reduce the utility value, pushing agents away from these impassable areas. The utility value of the goal is constant and is always only the largest. In the policy update, a series of policies can be obtained by the strategy of maximizing its long-term total reward, and the series will eventually form a path towards the goal, regardless of where the agent is located. The simulations and experiments show that it takes longer to find the first path in the beginning due to the large changes of utility value, but once the path is planned, it requires a small amount of time cost to respond to the environmental changes. Therefore, the proposed path planning algorithm has an advantage in dynamic environments where obstacles move in unpredictable ways.

A navigation algorithm providing motion planning for two-wheeled mobile robots is proposed in this paper. The motion planning integrates path planning, velocity planning and controller design. Bidirectional rapidly-exploring random trees algorithms (RRT) with path pruning and smoothing mechanism are firstly used to obtain a collision-free path to the robot destination with directional continuity. Secondly, velocity planning based on trapezoidal velocity profile is used in both linear and angular velocities, but the position error of the endpoint of the curve appears due to the coupling problem of the nonlinear system. To reduce the error, an approximation method is used to gradually modify several parts of time length of the trapezoidal velocity profile, so the continuity of the path can still be maintained. Thirdly, the controller keeping the robot on the planned path and velocity is designed based on the dynamic model of the robot. The parameters of this controller are estimated by the adaptive low, and the gain of controller is dynamic adjusted by fuzzy logic control to avoid the case that the control value is saturated. The controller stability and the convergence of tracking error is guaranteed by Lyapunov theory. Simulation results are presented to illustrate the effectiveness and efficiency.

In this paper, a hybrid ASMC&VSC high-precision motion controller for positioning systems is proposed. Specifically, in this system, a new structure with an adaptive sliding-mode control (ASMC) algorithm is described, which aims to achieve the following three goals: (1) the adjustable feature of variable speed control (VSC) to effectively improve the transient response in positioning; (2) precise positioning (submicrometer level); (3) excellent positioning convergence performance. Finally, according to the reported experimental results, satisfactory performance, including step regulation and tracking sinusoid, has been achieved. Therefore, the method of integrating a set of effective controllers is shown to be feasible and effective in the high-precision motion control of permanent magnet iron core linear synchronous motors (PMLSM).