This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively. INDEX TERMS Long Short-Term Memory (LSTM), photovoltaic power forecasting, Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN), Tabu Search Algorithm (TSA).

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International audienceIn this paper, a dynamic identification method for a robot manipulator is investigated. A robust adaptive differentiator based on higher order sliding modes is used to estimate the state of the system. A theoretical proof of the convergence of this robust differentiator is given. A frequency analysis in term of filtering is provided and a comparison with a filter based on a classical differentiator is carried out. The dynamic parameters are estimated using the recursive least squares solution of a linear system obtained from the sampling of the dynamic model along a closed loop tracking trajectory. An experimental validation is performed on a robot manipulator and compared to another identification method

The higher order sliding modes is some of recent technique which is used for the derivatives noisy signals estimation. In fact, this technique is well known mostly to elaborate the control laws and is also shown a good results in the synthesis of the r th order robust differentiators. In this paper, an extension of such technique to the two-dimensional case is investigated. In effect, the higher order sliding modes differentiators are used as a novel approach of edge finding in a gray scale image. The proposed algorithm use an adaptive mechanism for tuning up its parameters in real time, in order to increase the efficiency of basic scheme. Finally, to validate the efficiency of this new algorithm, some comparative study with a conventional edge detectors is performed.

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International audienceThis paper deals with online numerical differentiation of a noisy time signal where new higher order sliding mode differentiators are proposed. The key point of these algorithms is to include a dynamic on the differentiator parameters. These dynamics tune-up automatically the algorithm gains in real-time. Convergence properties of the new schemes are derived using a Lyapunov approach. Their effectiveness is illustrated via simulations and experimental tests, where comparative studies are performed between classical schemes and the new ones. Such algorithms are also used in the feedback control of an electrohydraulic system

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