Online Robust Adaptive Dynamic Programming is a M.Tech project topic for Aerospace Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Online Robust Adaptive Dynamic Programming Project Details
| Abstract |
This study provides a solution to the ongoing problems with disturbance suppression for adaptive dynamic programming (ADP) algorithms. It develops a new critic neural network (NN) based robust ADP algorithm for a particular class of continuous-time affine nonlinear systems with unknown bounded external disturbances. The algorithm's main feature is the improved robust cost function (RCF) which is designed to increase the system's disturbance rejection capability and decrease the energy expenditure of the control system. Closely related to this is the proposed dedicated disturbance compensator which is meant to provide asymptotic stability for the closed-loop system. In order to apply this control algorithm in practice a critic-only NN is incorporated to
approximate the optimal cost function with a novel tuning law. Therefore, the critic NN, RCF based optimal control algorithm, RCF based disturbance compensator, and the new NN tuning law together provide uniform ultimate boundedness (UUB) for the system's state trajectory. The proposed adaptive control strategy is thoroughly validated with numerous software simulations.
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| Reference Paper |
Online Robust Adaptive Dynamic Programming Control of a Class of Affine Nonlinear Systems Subject to Unknown Bounded External Disturbances |
| Domain |
Aerospace Engineering |
| Sub-Domain |
Structures & Systems / Guidance Navigation Control / Autopilot Design |
| PDF Download |
Download / View PDF |
| Get Help |
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