The generator torque is considered as the control input and the control objective is to track the rotor angular velocity in order to achieve the optimum value of power coefficient. A two-mass mechanical model is used and verified by FAST simulations. The proposed control methods are compared based on their power capture and robustness against probable uncertainties in the structural and environmental parameters. In this paper, with the main objective of maximizing the energy capture in the second region, four different control strategies are compared in the presence of uncertainties. In the wind turbine industry, a practical approach is to maximize the energy capture of a wind machine by optimizing the power coefficient in the under-rated situations. Reducing the renewable energy costs is necessary for the competition with the fossil energies and control strategies have great impact on the efficiency of wind machines. It will be shown that all the components of the error vector tend to zero, this fact proving both the proper functioning of the NN and the very good estimation of the state variables. In fact, the motion of the aircrafts with big attack angle is a nonlinear and complex system, which makes difficult the design and the implementation of efficient control and observation laws. The validation of the proposed observer scheme is made through Matlab/Simulink numerical simulation to reconstruct the unavailable state variables of a big attack aircraft longitudinal motion. The neural network is used to parameterize the nonlinearities of the system. The observer also includes a correction term which guarantees the good tracking as well as bounded neural network weights. The good results of the neural networks are due to their capacity of nonlinear functions' approximation. The weights and the biases are permanently modified in order to minimize the mean squared error between the actual outputs and the NN desired outputs in a gradient descent manner. The proposed neuro-observer is a three-layer feedforward neural network (NN), trained by means of the error backpropagation learning algorithm according to this algorithm, the neural network training process becomes a nonlinear function optimization problem. Step 6 adds the two together so both the pressure and temperature difference from ISA are accounted for to arrive at "density altitude".In this paper a neural network observer for nonlinear systems is presented. Steps 3 to 5 deal with temp difference from ISA. Steps 1 to 2 deal with the adjustment due to pressure difference from ISA. For every 1hPA up or down equates to 30 feet. For every 1000 ft up in altitude temp decreases by 1.98☌, but lets call it 2☌. ISA is the international standard atmosphere at AMSL of 15☌ and 1013.2 hPa. Add pressure correction height to the temp correction adjustment to get density altitude e.g. Multiply ISA deviation by 120 to get temp correction adjustment e.g. 20☌ to get ISA deviation = 6° - 11° = -5° ISA devĥ. 20 C at 1380 = 15 - 2 * 2 = 11 (This is what the standard temp should be)Į.g. Given QNH 1009 Elev/Alt 1800 OAT 6 Find Pressure and Density AltitudeĮ.g. This last answer allows for both the pressure and temperature difference from ISA.Įxample 2 has pressure and temp both lower than ISA. Multiply ISA deviation by 120 to get pressure correction height e.g. 20☌ to get ISA deviation = 20° - 12° = 8° ISA devĥ. Take ISA temp away from current OAT to get deviation from ISAĮ.g. 20 C at 1380 = 15 - 2 * 1.5 = 12 (This is what the standard temp should be)Ĥ. Find ISA temp = 15 - 2 x the thousands of feet (at the PH rounded to 1.5 as per above.)Į.g. at 1380 ft, nearest 500 ft is 1500 feet, sol lets use 1.5 in the next part.ģ. For the next part of the calculation ONLY, round pressure height to nearest 500ft (because 500ft will find temp to nearest 1☌).Į.g. Find pressure height = Elevation + (1013-QNH) x 30Į.g. Given QNH 1027 Elev/Alt 1800 OAT 20 Find Pressure and Density Altitudeġ. Example 1 has pressure and temp both higher than ISA.
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