A novel control for a nonlinear two-dimensional (2-D) overhead crane is proposed. Instead of the complex design procedures used in classic methods, the proposed scheme combines the principles of neural networks (NNs) and variable structure systems (VSS) to derive control signals needed to drive the cart smoothly, rapidly and with limited payload swing. The merits include the robustness and model-free properties of the sliding mode and neural based controllers, respectively. Simulations performed using a scaled 2-D mathematical model of the crane confirm the effectiveness of the proposed method.

Rotating machines such as motors, generate noise at the fundamental frequency and its harmonics. The narrowband active noise control (NANC) algorithm is widely used to cancel such noise. Traditional feedforward NANC systems use non-acoustic sensors to measure rotation speeds, and then a bank of signal generators produce synchronized tonal signals as the reference signals according to the fundamental frequency of the undesired noise. However, the non-acoustic sensors such as encoders usually cost a lot and are not reliable. This study proposes using the feedback ANC structure and incorporates with several adaptive line enhancers (ALEs) to generate the reference signals. Without using non-acoustic sensors, the proposed system only updates the center frequency of the first ALE to track the fundamental frequency change, which greatly reduces complexity of the proposed ANC system. The frequency mismatch (FM) problem is overcome by properly setting the bandwidths of the ALE. Performance were verified by using both simulations and real-time experiments.

This paper presents the development of active noise control (ANC) for light-weight earphones, and proposes using music or natural sound to estimate the critical secondary path model instead of extra random noise. Three types of light-weight ANC earphones including in-ear, earbud, and clip phones are developed. Real-time experiments are conducted to evaluate their performance using the built-in microphone inside KEMAR’s ear and to compare with commercially-available ANC headphones and earphones. Experimental results show that the developed light-weight ANC earphones achieve higher noise reduction than the commercial ANC headphones and earphones, and the in-ear ANC earphone has the best noise reduction performance.

Snake robots have come to represent a new subfield of bionic robot research in recent years. A snake robot comprises many modules and performs various movements in arranged connections. The structure of a snake body enables it to move smoothly in narrow spaces or pipes with high stability and reliability. This article studies the application of a snake robot on a large-scale nuclear power facility to sense in pipe components. Therefore, a snake robot must move in pipes in which high radiation is present to explore the surrounding environment and take samples. A simple but effective method of locomotion is developed and executed to confirm feasibility of motion, especially in narrow space. A sampling mechanism with a storage box is designed at the tail of the snake to take and keep the samples well at designated locations. We built a pipe system which has two right-angled turns to simulate the pipes of a large-scale nuclear power facility. A user interface helps operators to manipulate the snake robot.