New PDF release: Adaptive aeroservoelastic control

By Ashish Tewari

ISBN-10: 1118457633

ISBN-13: 9781118457634

ISBN-10: 1118823494

ISBN-13: 9781118823491

This can be the 1st publication on adaptive aeroservoelasticity and it provides the nonlinear and recursive ideas for adaptively controlling the doubtful aeroelastic dynamics

  • Covers either linear and nonlinear keep an eye on tools in a finished manner
  • Mathematical presentation of adaptive regulate recommendations is rigorous
  • Several novel purposes of adaptive regulate offered listed below are to not be present in different literature at the topic
  • Many practical layout examples are coated, starting from adaptive flutter suppression of wings to the adaptive regulate of transonic limit-cycle oscillations

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Extra resources for Adaptive aeroservoelastic control

Sample text

This implies that as an accurate unsteady aerodynamics plant model is infeasible for aeroservoelastic design, adaptive plant identification in closed loop is the only practical alternative. 5 Adaptive Control Design Following the above discussion, it is logical that the final step in aeroservoelastic system design should be the derivation of adaptive control laws that can fully account and compensate for the parametric uncertainties and variations in the characteristics of the aeroelastic system.

A linear aerodynamic model Eq. 6) combined with the linear structural dynamics Eq. 1) yields the following linear aeroelastic state equations that can be used as a baseline plant of the adaptive ASE control system: . 7) . where {X(t)} = [{q(t)}T , {q(t)}T ]T is the state vector of the aeroelastic system, {u(t)} the vector of generalized control forces generated by a set of control surfaces and {p} the vector of random disturbances called the process noise. In order to derive the constant coefficient matrices [A], [B], [F], an additional step is necessary, even if the generalized aerodynamic forces {Q(t)} are linearly related as follows to the modal displacements {q(t)} and their time derivatives by virtue of Eq.

If the observer gains, [L], are too small, the estimation error, {e}, does not tend to zero fast enough for the feedback to be accurate. On the other hand, if the observer has very large gains, there is an amplification of process and measurement noise by feedback, thereby reducing the overall robustness of the control system. Clearly, a balance must be struck in selecting the Kalman filter design parameters, such that a good robustness is obtained without unduly sacrificing performance. Several linear feedback strategies are in use for striking a compromise between robustness with respect to plant uncertainty, and noise rejection properties.

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Adaptive aeroservoelastic control by Ashish Tewari


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