New PDF release: Adaptive aeroservoelastic control

By Ashish Tewari

ISBN-10: 1118457633

ISBN-13: 9781118457634

ISBN-10: 1118823494

ISBN-13: 9781118823491

This is often the 1st e-book on adaptive aeroservoelasticity and it provides the nonlinear and recursive innovations for adaptively controlling the doubtful aeroelastic dynamics

  • Covers either linear and nonlinear keep an eye on tools in a entire manner
  • Mathematical presentation of adaptive keep watch over innovations is rigorous
  • Several novel purposes of adaptive regulate awarded listed below are to not be present in different literature at the topic
  • Many real looking layout examples are lined, starting from adaptive flutter suppression of wings to the adaptive regulate of transonic limit-cycle oscillations

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Additional info for Adaptive aeroservoelastic control

Example text

For a plant with a linear input–output behaviour and a fair statistical description of the noise inputs that are small in magnitude, the robust control theory (Maciejowski 1989) can be applied to design a linear feedback controller with constant parameters, which will produce an acceptable performance in many applications. However, constant controller gains may either fail to stabilize the system if the plant behaviour is highly uncertain or may have unacceptable performance in the presence of noise inputs.

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. These include LQG compensation with loop-transfer recovery (LTR) (Maciejowski 1989), H2 ∕H∞ control (Glover and Doyle 1988) and structured singular value (SSV) (or ????-) synthesis (Packard and Doyle 1992).

Similarly, the tolerance of a robotic positioning device largely depends upon the number of structural vibration modes considered in modelling the robotic arm. Most mechanical and electrical systems can be modelled to a very high accuracy because their dynamics are well understood, and hence controller design for the systems can be carried out by traditional methods. The same, however, cannot be said of an ASE system, wherein achieving high accuracy may result in the aeroelastic model becoming too unwieldy and complex to be of any benefit in control system design.

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Adaptive aeroservoelastic control by Ashish Tewari


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