Reinforcement learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system
DOI10.1016/j.ins.2021.10.070OpenAlexW3213059358MaRDI QIDQ6188171
Radu-Emil Precup, Raul-Cristian Roman, Emil M. Petriu, Iuliu Alexandru Zamfirache
Publication date: 1 February 2024
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2021.10.070
reinforcement learninggravitational search algorithmQ-learningservo systemsNN trainingoptimal reference tracking control
Artificial neural networks and deep learning (68T07) Approximation methods and heuristics in mathematical programming (90C59) Nonlinear systems in control theory (93C10)
Cites Work
- Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity
- BGSA: Binary gravitational search algorithm
- Control of blood glucose for type-1 diabetes by using reinforcement learning with feedforward algorithm
- Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems
- Membership-function-dependent stability analysis and local controller design for T-S fuzzy systems: a space-enveloping approach
- GSA: A gravitational search algorithm
- Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
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