Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning
Northwestern Polytechnical University · Swansea University · +2 more institutions
Abstract
In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV's control input nonlinearities, a compensation item is also designed…
Citation impact
- FWCI
- 47.59
- Percentile
- 100%
- References
- 74
Authors
4Topics & keywords
- Control theory (sociology)
- Artificial neural network
- Robustness (evolution)
- Adaptive control
- Computer science
- Reinforcement learning
- Compensation (psychology)
- Control engineering
- Life below water