Control of underactuated mechanical systems via energy shaping is a well-established, robust design technique. Unfortunately, its application is often stymied by the need to solve partial differential equations (PDEs). In this paper a new, fully constructive, procedure to shape the energy for a class of mechanical systems that obviates the solution of PDEs is proposed. The control law consists of a first stage of partial feedback linearization followed by a simple proportional plus integral controller acting on two new passive outputs. The class of systems for which the procedure is applicable is identified imposing some (directly verifiable) conditions on the systems inertia matrix and its potential energy function. It is shown that these conditions are satisfied by three benchmark examples. Index Terms-Nonlinear systems, passivity-based control, mechanical systems, stabilization.0018-9286 (c)

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The strength of the muscle contraction can be easily measured by the muscle activity extracted at the skin surface. Analysis of surface Electromyogram (sEMG) is one of the standard procedures to identify posture, gesture and actions (i.e. control of prosthesis via learnt body actions). sEMG signals are usually complex in nature. It can be easily classified into differentiated muscular activities with appropriate signal processing tools. In order to analyze its complexity, various studies have been carried out but have proved unsuccessful, due to huge differences in muscular activities of some muscles over the other. This paper presents a new technique to identify low level hand movement by classifying the single channel sEMG. Single channel sEMG analysis is preferred over multi-channel due to its simplicity, computational cost and efficiency. Wavelet transformation and artificial neural network (ANN) classifier are utilized to classify and analyze the sEMG signal in a better way. c 2014 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the 4th International Conference on Advances in Computing, Communication and Control (ICAC3'15).

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Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially expensive maintenance program. Accordingly, Remaining Useful Life (RUL) prediction based on artificial intelligence is an attractive methodology for several researchers. In this study, data-driven condition monitoring approach is implemented for predicting RUL of bearing under a certain load and speed. The approach demonstrates the use of ensemble regression techniques like Random Forest and Gradient Boosting for prediction of RUL with time-domain features which are extracted from given vibration signals. The extracted features are ranked using Decision Tree (DT) based ranking technique and training and testing feature vectors are produced and fed as an input to ensemble technique. Hyper-parameters are tuned for these models by using exhaustive parameter search and performance of these models is further verified by plotting respective learning curves. For the present work FEMTO bearing data-set provided by IEEE PHM Data Challenge 2012 is used. Weibull Hazard Rate Function for each bearing from learning data set is used to find target values i.e. projected RUL of the bearings. Results of proposed models are compared with well-established data-driven approaches from literature and are found to be better than all the models applied on this data-set, thereby demonstrating the reliability of the proposed model.