Reinforcement learning-based saturated adaptive robust output-feedback funnel control of surface vessels in different weather conditions
DOI10.1016/j.jfranklin.2023.10.033zbMath1530.93189OpenAlexW4388187647MaRDI QIDQ6136480
Seyed Jalal Sajadian, Khoshnam Shojaei, Omid Elhaki, Iman Moghtaderizadeh
Publication date: 17 January 2024
Published in: Journal of the Franklin Institute (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jfranklin.2023.10.033
observerprescribed performance controladaptive actor-critic neural networkcutting-corner compensationgeneralized saturation function
Artificial neural networks and deep learning (68T07) Sensitivity (robustness) (93B35) Feedback control (93B52) Adaptive control/observation systems (93C40)
Cites Work
- Adaptive fuzzy sliding-mode consensus control of nonlinear under-actuated agents in a near-optimal reinforcement learning framework
- Self-triggered control of probabilistic Boolean control networks: a reinforcement learning approach
- Robust adaptive critic control of nonlinear systems using fuzzy basis function networks: an LMI approach
- Optimal trajectory exploration large-scale deep reinforcement learning tuned optimal controller for proton exchange membrane fuel cell
- A novel adaptive fuzzy reinforcement learning controller for a platoon of off-axle hitching tractor-trailers with a prescribed performance and path curvature compensation
- Composite fuzzy voltage-based command-filtered learning control of electrically-driven robots with input delay using disturbance observer
- Off-policy reinforcement learning for tracking control of discrete-time Markov jump linear systems with completely unknown dynamics
- Adaptive output-feedback formation control for underactuated surface vessels
- Robust Adaptive Control of Feedback Linearizable MIMO Nonlinear Systems With Prescribed Performance
- Prescribed Performance Adaptive Control for Multi-Input Multi-Output Affine in the Control Nonlinear Systems
- Robust adaptive self‐structuring neural networks tracking control of unmanned surface vessels with uncertainties and time‐varying disturbances
- Data-based \(\mathcal{L}_2\) gain optimal control for discrete-time system with unknown dynamics
- Model-based deep reinforcement learning for data-driven motion control of an under-actuated unmanned surface vehicle: path following and trajectory tracking
This page was built for publication: Reinforcement learning-based saturated adaptive robust output-feedback funnel control of surface vessels in different weather conditions
