Neuro Adaptive Control For Aerospace And Distributed Systems

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Title: Neuro Adaptive Control For Aerospace And Distributed Systems
Author: Das, Abhijit
Abstract: Nonlinear and adaptive control is generally considered one of the most effective techniques for stabilizing complex nonlinear systems , where linear control techniques may fail completely . Thousands of research papers are published on either theory or applications of nonlinear and adaptive control . But often one obvious question arises how to implement these techniques in real life model ? The best answer that one can think of is to develop simple nonlinear control laws which are easy to implement . Moreover for controlling multi -agent systems , it is often required to distribute the control laws based on limited information available among the agents . This research provides some of these issues in the following way . Autopilot design for Aerospace systems : this research developes adaptive backstepping and dynamic inversion methods with internal dynamics stabilization for the quadrotor . Quadrotor helicopter models usually show two main characteristics . First , strong coupling among the system states and second , underactuation where many states are to be controlled with few control inputs . Due to these unique characteristics , the design of stabilizing control inputs is always challenging for quadrotor models . To confront these problems , first , a dynamic inversion technique with zero dynamics stabilization loop is introduced to a practical quadrotor model , second , an adaptive -backstepping technique is developed to a lagrangian quadrotor model . The stabilizing control laws for both of these techniques are developed using on Lyapunov based method ; and Coordination of multi -agent systems : coordination among multiple agents is generally done based on balanced or bi -directed communication graph models . If the agents are nonlinear and passive then for a balanced graph model synchronization is possible . But , for other than balanced and bi -directed graph models , it is difficult to synchronize nonlinear systems . Moreover , the performance of synchronization is normally dependent on the second largest eigenvalue of the laplacian matrix of the network graph . This eigenvalue is also known as the Fiedler eigenvalue . This research shows how to implement distributed nonlinear and adaptive controllers using pinning techniques for generalized directed communication graph models . The dynamics of the agent at each node are non -identical and unknown . A Lyapunov based technique is adopted to show the tracking performance when the tracker dynamics are also considered unknown . It is also shown using pinning adaptive control that the synchronization speed no longer depends on Fiedler eigenvalue of the graph laplacian matrix . The research also develops duality properties of linear controllers and observers for general cooperative di -graph systems . The choice of gains for controller and observer using riccati equations ensures stable synchronization on general di -graphs . Finally the research is extended to decentralized control of HVAC systems using pinning control methodology .
URI: http : / /hdl .handle .net /10106 /5139
Date: 2010-11-01

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Neuro Adaptive Control For Aerospace And Distributed Systems. Available electronically from http : / /hdl .handle .net /10106 /5139 .

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