Abstract
This paper presents a neuroadaptive tracking control approach for uncertain robotic manipulators subject to asymmetric yet time-varying full-state constraints without involving feasibility conditions. Existing control algorithms either ignore motion constraints or impose additional feasibility conditions. In this paper, by integrating a nonlinear state-dependent transformation into each step of backstepping design, we develop a control scheme that not only directly accommodates asymmetric yet time-varying motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers, simplifying design process, and making implementation less demanding. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness and benefits of the proposed control method for robotic manipulator are validated via computer simulation.
| Original language | English |
|---|---|
| Article number | 8425067 |
| Pages (from-to) | 15-24 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Cybernetics |
| Volume | 50 |
| Issue number | 1 |
| Early online date | 3 Aug 2018 |
| DOIs | |
| Publication status | Published - Jan 2020 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Funding
This work was supported in part by the Fundamental Research Funds for the Central University under Grant 2018CDYJSY0055, in part by the National Natural Science Foundation of China under Grant 61773081, in part by the Central University Fund under Grant 2018CDJDZ0009, in part by the Graduate Scientific Research and Innovation Foundation of Chongqing under Grant CYB17048, and in part by the China Scholarship Council.
Keywords
- Adaptive neural control
- feasibility conditions
- position and velocity constraints
- robotic manipulator