To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

In this paper two smooth robust tracking controllers for uncertain robot manipulators are presented. Ihe proposed controllers yield smooth acceleration response. A deteiministic approach is adopted in developing the controllers. Lyapunov direct method is used and a nonlinear Lyapunov function is employed to prove the stability of the system. Ihe first controller is shown to render the closed-loop system practically stable, forcing the state tracking error to converge exponentially to a small neighborhood of the origin. On the other hand, the second controller is shown to render the closed-loop system asymptotically stable, forcing the state tracking error to converge to the origin. n2e eflcacy of the proposed controllers is verified by application to a planar two-link manipulator.

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