Observer-based event-triggered and set-theoretic neuro-adaptive controls for constrained uncertain systems

<p>"In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon L1 libration point. This dynamic system is selected to evaluate the performance of the ETNAC techniques in a setting that is highly nonlinear and chaotic in nature. Moreover, factors like restricted controls, response to uncertainties and jittering makes the controller design even trickier for maintaining a tight formation precision. Lyapunov function-based stability analysis and numerical results are presented. Note that most real-world systems involve constraints due to hardware limitations, disturbances, uncertainties, nonlinearities, and cannot always be efficiently controlled by using linearized models. To address all these issues simultaneously, a barrier Lyapunov function-based control architecture called the segregated prescribed performance guaranteeing neuro-adaptive control is developed and tested for the constrained uncertain nonlinear systems, in the third part. It guarantees strict performance that can be independently prescribed for each individual state and/or error signal of the given system. Furthermore, the proposed technique can identify unknown dynamics/uncertainties online and provides a way to regulate the control input"--Abstract, page iv.</p>

Name thesis:degree_name

Ph. D. in Mechanical Engineering

Grantor

Missouri University of Science and Technology

Author dc:creator

• Ghafoor, Abdul

• Adaptive Controls
• Automatic Controls
• Event-Triggered Control
• Neural network Control
• Observer and Estimation Theory
• Set-Theoretic Control
• Artificial Intelligence and Robotics
• Mechanical Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2865

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3870