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This paper presents a robust control system that addresses two key challenges in redundant actuators using Permanent Magnet Synchronous Motors (PMSM) for an aircraft nose wheel steering system: the elimination of force-fighting phenomena and the ability to respond effectively to unexpected disturbances. In detail, a control method was devised to enhance the mitigation of force-fighting phenomena and disturbances by accurately observing and compensating for the torque-induced load applied to the PMSM. This was achieved through the utilization of a Q-filter-based Disturbance Observer (DOB). The proposed control approach was implemented and evaluated on a redundant system consisting of the PMSM and the nose wheel steering system. The performance of the proposed method was verified through extensive simulation studies. The simulation results confirmed the effectiveness and reliability of the method in accurately observing and responding to the force-fighting phenomenon that occurs in the redundant driving device. By subjecting the system to various scenarios and disturbances, the simulation provided a comprehensive evaluation of the proposed method’s ability to handle force-fighting phenomena. The results demonstrated that the method successfully observed and responded to the force-fighting phenomenon, thereby mitigating its adverse effects on the system’s performance. Therefore, these outcomes serve as empirical evidence supporting the validity and efficiency of the proposed method in addressing the force-fighting phenomenon encountered in the redundant driving device. These findings substantiate the effectiveness of the proposed approach and its potential for practical implementation in real-world systems.

At a disaster site, unforeseen circumstances can severely limit the activities of rescue workers. The best solution is for a cooperative team of robots and rescue workers to complete the rescue work. Therefore, in this paper, we propose a simple and low-cost sensing and control architecture for a search and rescue assistant robot using a thermal infrared sensor array, an ultrasonic sensor, and a three-axis accelerometer. In the proposed architecture, we estimate the location of human survivors using a low-cost thermal IR sensor array and generate and control the trajectory of approaching the searched human survivors. Obstacle avoidance and control are also possible through 3D position estimation of obstacles using 1D ultrasonic sensor integration. In addition, a three-axis accelerometer is used to estimate the tilt angle of the robot according to terrain conditions, and horizontal control of the storage box angle is performed using this feature. A prototype robot was implemented to experimentally validate its performance and can be easily constructed from inexpensive, commonly available parts. The implementation of this system is simple and cost-effective, making it a viable solution for search and rescue operations. The experimental results have demonstrated the effectiveness of the proposed method, showing that it is capable of achieving a level storage box and identifying the location of survivors while moving on a sloped terrain.

In this paper, we propose a conceptual framework for a sensor fusion system that can detect objects in a dense smoke environment with a visibility of less than 1 m. Based on the review of several articles, we determined that by using a single thermal IR camera, a single Frequency-Modulated Continuous-Wave (FMCW) radar, and multiple ultrasonic sensors simultaneously, the system can overcome the challenges of detecting objects in dense smoke. The four detailed methods proposed are as follows: First, a 3D ultrasonic sensor system that detects the 3D position of an object at a short distance and is not affected by temperature change/gradient; Second, detecting and classifying objects such as walls, stairs, and other obstacles using a thermal IR camera; Third, a 2D radial distance measurement method for a distant object using an FMCW radar; Fourth, sensor fusion for 3D position visualization of multiple objects using a thermal IR camera, 3D ultrasonic sensor system, and FMCW radar. Finally, a conceptual design is presented based on the proposed methodologies, and their theoretical usefulness is discussed. The framework is intended to motivate future research on the development of a sensor fusion system for object detection in dense smoke environments.

The capacitance and rotor angle of a MEMS top-drive electrostatic rotary actuator do not have a linear relationship due to the non-ignorable fringe effect and low aspect ratio of the electrodes. Therefore, the position estimation is not as straightforward as that for a comb-drive linear actuator or a side-drive rotary actuator. The reason is that the capacitance is a nonlinear and periodic function of the rotor angle and is affected by the three-phase input voltages. Therefore, it cannot be approximated as a simple two-plate capacitor. Sensing the capacitance between a rotor and a stator is another challenge. The capacitance can be measured in the electrodes (stators), but the electrodes also have to perform actuation, so a method is needed to combine actuation and sensing. In this study, a nonlinear capacitance model was derived as a data-driven model that effectively represents the nonlinear capacitance with sufficient accuracy. To measure the capacitance accurately, the stator parts for actuation and those for sensing are separated. Using the nonlinear model and the capacitance measurement, an unscented Kalman filter was designed to mitigate the large estimation error due to the periodic nonlinearity. The proposed method shows stable and accurate estimation that cannot be achieved with a simple two-plate capacitor model. The proposed approach can be applied to a similar system with highly nonlinear capacitance.