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An intelligent neural sliding controller is developed for planetary train type inverted pendulum mechanism. The control methodology is based on the sliding mode control. The switching function in the normal control law is replaced with a bipolar sigmoid function. A fuzzy neural network is used to identify the pendulum dynamics. Adaptive tuning law is(More)
This paper aims to study the steady-state response of the practical sliding mode controlled system (SMC). The nonlinearity of the switching term is approximately characterized by using its equivalent describing function. The parasitic dynamics is modeled as a first order lag transfer function, and the possible transport delay is particularly considered.(More)
We have developed a new method for speech decomposition and modeling. The purpose of this approach is to obtain better performance for modeling speech signals corrupted by non-stationary noise. The signal is first divided into frames and then each frame is decomposed into chirp-like partials which are linearly modulated in both amplitude and frequency. The(More)
In and post a fire event, an accurate and real-time evaluation and monitoring of a structure's performance can assist firefighters for efficient survivor rescuing, which significantly improve the fire rescuing safety both for fire fighters and the trapped survivors. However, due to the lack of durable sensors, the structural performance of steel structures(More)
In a pick-and-place task, goal variation occurs because the parts on the conveyor are fed following a certain probability distribution with various random seeds. To make the manipulator system complete the task reliably and efficiently, the robust solution should be obtained against goal variation. In this study, we propose a method (Fig. 1) to obtain a(More)
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