A combined control strategy with an adaptive inverse control (AIC) and an inverse frequency response function (IFRF) equalization technique is proposed for the electrohydraulic shaking table (EST) system. The control purpose is to improve the accuracy of time waveform replication. In contrast to the iterative compensation through repetitive excitations control approach of industrial EST controllers, the proposed control strategy utilizes an IFRF of the EST system for extending the EST system frequency bandwidth and obtaining asymptotic reference tracking, and employs a variable tap-length filtered-x least-mean-squares algorithm to adaptively tune the time-domain drive signal and further improve the position and acceleration tracking performance of the EST system. Thus, the proposed combined control strategy is designed to combine the merits of IFRF and AIC. The procedures of the proposed control strategy are programmed in MATLAB/Simulink, and then compiled to a real-time PC with Microsoft Visual Studio.NET for implementation. The simulated and experimental results show that this control strategy achieves satisfactory position and acceleration waveform replication accuracy.

Visible light technology shows great potential in the underground mine positioning today. Most visible light-based underground positioning technology deploy the receiver, such as photodiodes (PDs), on the mine tunnel ground and consider the miner’s lamp on the ceiling as the transmitter. This incurs the unreliable reception of visible light signals and the difficult implementation of VLP systems due to the complex underground mine environment, such as the receiver being obscured or trampled by objects (e.g., Minecarts and Ore). We present a reliable underground positioning algorithm with visible light considering the actual mine environment. The key innovation is that we design a novel reverse mechanism, i.e., utilizing the existing miner’s headlamp as the transmitter for broadcasting the specific light beacon representing the mine’s identity information, and the PDs deployed on the ceiling of the mine tunnel as the receiver for optical signal reception. We can infer the current locations of all miners using a series of base stations constructed by PDs. If some special mine environment limits the large-scale deployment of base stations and causes the positioning blind spots, the inertial navigation is combined to achieve all miners’ real-time positioning. We overcome some technical challenges for this new mechanism and the underground mine environment, such as optimal frequency selection, robust frequency identification, and accurate base station positioning. We conduct extensive experiments on our algorithm in a simulated mine tunnel, and the experimental results verify that our algorithm exhibits good performance.