H ∞ Aerospace Control Design: A VSTOL Flight Application

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Free H ∞ Aerospace Control Design A Vstol Flight Application 1995

Mystkowski, A. Robust control of unmanned aerial vehicle - simulation investigations. For the designed control system, the simulations and hardware-in-the-loop tests were performed. For the micro-aircraft with delta wings configuration the nominal model linearized in the desired operation point was calculated. Next, the uncertainty model was evaluated. The uncertainty model consists with multiplicative plug-in dynamics disturbances and parametric uncertainty. The uncertainty is conducted with the aircraft aerodynamics characteristics and parameters.

Catalog Record: Aerodynamics of V/STOL flight | HathiTrust Digital Library

These uncertainties are bounded in size based on wind tunnel experiments, flight test and analytical calculations. The weighting functions are used to capture the limits on the aileron, elevator and thrust actuators deflection magnitude and rate. The robust control laws were successfully verified during the hardware-in-the-loop simulations. Nr 7 The dynamical model, ignoring aerodynamics effects and gyroscopic moments is given by Castillo et al.

Dissipativity indicates the way energy is stored and dissipated in a nonlinear system around on equilibrium point. So, this is the major problem for practical applications. Consider nonlinear affine system with external disturbance in the form Isidori and Astolfi , Van der Schaft , Bianchini et al.


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The following Lemma helps us to design the u x. Lemma 3. Define the following HJI partial differential inequality.


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  6. Moreover, when 5 is zero-state detectable, its asymptotic stability is satisfied and u x. It is obvious that finding of the storage function V x is the hardest stage of this control approach. In summary, for the system was described in 4 , we want to find a controller such that satisfying. Hence the cost function is defined as follows:.

    Which contains two parts: the first part pertaining to the attitude and altitude control performance, and the other part is the amount of control input. The h x is considered as the following form:. The third part is a combination of translational kinetic energy and linear momentum and potential energy.

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    We have the following relations:. The lemma can be proved by direct calculations and omitted for the sake of brevity.

    Limitations

    To this end, the candidate storage function is considered as follows:. The condition for non-positiveness of A 1 are:. For A 2 to be non-positive, the following relations should be hold:. Hence, to make H v non-positiveness, the inequalities 24 , 25 , 26 , and 27 should be hold. Now, according to lemma 3. Control input, u x is considered as:. Through computer simulation with model uncertainties and external disturbances, we demonstrate that the proposed approach is effective.

    The numerical values for the parameters of flying robot are used for simulation are adopted from Raffo et al. The proposed controller was tested for different disturbances consist of the model uncertainties mass and moment of inertia and moment disturbances. The simulation for PID controller obtained in Comert and Kasnakoglu was also tested for comparative study. Figure 5 shows the angular velocities performance using PID controller and PID controller with the effect of disturbances and parameter uncertainties.

    It can be seen that the proposed controller can reject disturbances and cover the changes in parameters uncertainties and remains asymptotically stable, while the PID controller cannot reject the disturbances and has a big noise. The comparisons between Figure 3 and Figure 5 illustrates that the angular performance using the proposed controller achieves the stability conditions faster than that of using PID controller.

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    An analytical solution of HJI inequality was presented for a quadrotor. Finally, the proposed controller has been evaluated by simulation results to show the stabilization of orientation and altitude, in the presence of the moment of inertia uncertainty and external disturbances. AliAbbasi, M.

    Bianchini, G. Bouabdallah, S.

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    Budiyono, A. Optimal tracking controller design for a small scale helicopter, Journal of Bionic Engineering Castillo, P, Dzul, A. Castillo, P. Modeling and control of mini-flying machines, Springer-Verlag London. Chen, B. Chen, F. Chen, M.

    Comert C. Czyba, R.


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    4. Control structure impact on the flying performance of the multi-rotor VTOL platform-design, analysis and experimental validation, International Journal of Advanced Robotics Systems 10 62 Fernando, HCTE, et al. Guilheme, V. Isidori, A. Islam, S. Robust control of four-rotor unmanned aerial vehicle with disturbance uncertainty, IEEE Transaction on Industrial Electronics Jasim, W. Kang, W. Kugi, A. Li, S. Min, B. Department of a micro quad-rotor UAV for monitoring an indoor environment, Advances in Robotics Mokhtari, A. Navabi, M.