Adaptive nonlinear flight control

Ph.D.A. J. Calis

Dynamic Neural Networks For Output Feedback Control

A dynamic neural network is designed to estimate velocities from displacement measurements for a nonlinear system. A linear-in-parameters NN is used for state reconstruction. Conditions are provided under which the estimation error is guaranteed to be ultimately bounded. Subsequently, this observer is integrated into an adaptive controller architecture. The controller is based on model inversion and is augmented with a second learning-while-controlling neural network. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined observer-controller feedback system. Open loop and closed loop simulations for a Van Der Pol oscillator are used to illustrate the results. Key words: neural networks, nonlinear observer, adaptive control, output feedback problem 3 Introduction In the case of linear systems with known parameters, there exists vast literature on estimation theory that allows asymptotic tracking of the actual state by its estimate, e.g. see Ref..

Adaptive Model Inversion Flight Control For Tiltrotor Aircraft

Neural network augmented model inversion control is used to provide a civilian tiltrotor aircraft with consistent response characteristics throughout its operating envelope, including conversion flight. The implemented response type is Attitude Command Attitude Hold in the longitudinal channel. Similar strategies can be applied to provide for Rate Command Attitude Hold in the roll channel, and Heading Hold and Turn Coordination for the yaw motion. Conventional methods require extensive gain scheduling with tiltrotor nacelle angle and speed. A control architecture is developed that can alleviate this requirement and thus has the potential to reduce development time, facilitate the implementation of handling qualities, and compensate for partial failures. One of the key aspects of the controller architecture is the accommodation of modeling error. It includes an online, i.e. learningwhile -controlling, neural network with guaranteed stability. The performance of the controller is demons..

Dynamic Neural Networks For Output Feedback Control

A dynamic neural network is designed to estimate velocities from displacement measurements for a nonlinear system. A linear-in-parameters NN is used for state reconstruction. Conditions are provided under which the estimation error is guaranteed to be ultimately bounded. Subsequently, this observer is integrated into an adaptive controller architecture. The controller is based on model inversion and is augmented with a second learning-whilecontrolling neural network. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined observer-controller feedback system. Open loop and closed loop simulations for a Van Der Pol oscillator are used to illustrate the results. Introduction In the case of linear systems with known parameters, there exists vast literature on estimation theory that allows asymptotic tracking of the actual state by its estimate, e.g. [1, 2]. At the opposite end of the spectrum one can find approaches for nonlinear plants with uncertai..