Question

Biogen Aeronautic Defence and Space Systems makes Biofuel-powered jet airplanes For their new model, the Bein' Green-17, they placed the greener jet forward of the wing; but, as its nacelle (or inlet cowl) generates lift at high angles of attack (such as those during take-off), it is necessary to compensate for the positive feedback mode this causes in the plane's pitch. Note, this is particularly the case when the air is a little thinner, such as at "hot or high" airports. Also, other design changes (a new inlet, larger landing gear, etc.) would be less fuel efficient and more costly.] In response, a control program is added to automatically pitch the nose down just before a stall (i.e. a condition in which lift is lost due to airflow separation at large angles of attack). This, in turn, requires an estimate of the airplane's angle of attack. Four engineers are debating what strategy to take to observe this value Engineer W suggests that median value from three of the same sensors is taken. . Engineer X suggests that only two of the most accurate and robust sensors are needed. Engineer X recommends a sensor that has an MTBF (mean time between failures) of 20,000 hours (2.2 years continuous operation) and to take the maximum value of the two sensors Engineer Y suggests state estimation of the value from different two different types of sensors (of which, at least one of each is needed, though more is nice). Engineer Y's initial suggestion is an Extended Kalman Filter, to get the average value (μ) and its variance (σ) · Engineer Z suggests an integrated Bayesian estimation and control strategy that considers the value,its likelihood, and the next controlled action, States would be conditioned on the sequence of flight motions, state of the overall flight (e.g., taxi, take-off, etc.), etc. Engineer Z adds that this approach is more robust and even allows for active-sensing (e.g. self-diagnosis and calibrating the sensor when on (presumably level) ground, etc.) Who (if any, some, or all) is right? Please discuss.

Please give explanations to the correct answer. Thank you very much in advance, will rate!

Answer 1

Engineer W It is not used since if one more sensor is added or removed out of 3 sensors the it is difficult to find median The interpolation formula applied in the continuous series is based on the unrealistic assumption that the frequency of the median class is evenly spread over the magnitude. Thus, cost is high for 3 sensors Engineer X: He uses the sensor that has a MTBF of 20000 hours Failure rate 1/MTBF=R/T. R-Rate of failure. T-total time But the probability of any hardware that can survive to its calculated MTBF is 36%(nearly) As it takes the maximum value of 2 sensors if anyone fails The maximum value would be the other sensor value only

Engineer Y He uses the 2 sensors from these values mean and variance is calculated by using extended Kalman Filter The Extended Kalman Filter is the extension of the Kalman filter, and it is used for the nonlinear systems with a linear model But it fails to perform well during some cases. In those cases, estimator designers must add some noise to guarantee numerical stability It doesn't work if perfect value(noise) is given Engineer Z: Bayesian estimation uses the probability to model the uncertain things Pitching the noise down is uncertain So, this model better to estimate these conditions. This model has better average accuracy The control strategy is based on its likelihood values The point estimation is made for the parameters obtained to maximize the likelihood We have the data (D)and the parameters(Q) and need to find set of parameters(Q* to make a prediction about the future values In Bayesian estimation P(Q/D){PD/Q) P(Q)}/P(D) Where P(Q) prior To predict the future events, we need to integrate it over the distribution of Q