This is the Github repository for a neural control architecture that employs input-output information to compensate for the lack of knowledge about the robot model to achieve prescribed tracking performance in the presence of joint constraints described in the paper title "Approximation-Free Robust Tracking Control of Unknown Redundant Manipulators With Prescribed Performance and Input Constraints". The paper can be found at https://ieeexplore.ieee.org/abstract/document/10649593
If you use the code in the academic context, please cite:
- R. Singh and J. Keshavan, "Approximation-Free Robust Tracking Control of Unknown Redundant Manipulators With Prescribed Performance and Input Constraints," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 11, pp. 6743-6755, Nov. 2024, doi: 10.1109/TSMC.2024.3444030.