Title :
Adaptive optimal tracking control applied for a humanoid robot arm
Author :
David Hemmi;Guido Herrmann;Jing Na;Muhammad Nasiruddin Mahyuddin
Author_Institution :
National ICT Australia (NICTA) and Faculty of IT, Monash University, Australia
Abstract :
In this paper, a recently suggested adaptive online optimal control algorithm for the infinite-horizon tracking problem of continuous-time non-linear systems with partially unknown system dynamics is modified and empirically evaluated. Since we lack complete systems knowledge a parameter identifier, which works simultaneously with the updating of the online optimal control algorithm, is introduced. We maintain tracking performance by employing an adaptive steady-state controller based on the identified system parameters and a complementary self optimizing adaptive controller, designed to stabilize the plant. To approximate the optimal value function of the Hamilton-Jacobi-Bellman equation, which is required to construct the adaptive optimal stability controller, a single layer neural network is utilized. Both the findings obtained in practice by controlling a humanoid robot-arm , as well as the results produced in simulation, demonstrate the applicability of the introduced control scheme.
Keywords :
"Optimal control","Artificial neural networks","Adaptive systems","Steady-state","Robots","Torque","Heuristic algorithms"
Conference_Titel :
Intelligent Control (ISIC), 2015 IEEE International Symposium on
Electronic_ISBN :
2158-9879
DOI :
10.1109/ISIC.2015.7307276