Fully probabilistic control for stochastic nonlinear control systems with input dependent noise
DOI10.1016/j.neunet.2014.12.004zbMath1323.93075OpenAlexW1969183112WikidataQ41619871 ScholiaQ41619871MaRDI QIDQ889398
Publication date: 6 November 2015
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://publications.aston.ac.uk/id/eprint/24935/1/NeuralNetwork15.pdf
nonlinear stochastic systemsfully probabilistic designmixture of Gaussiansadaptive criticdual heuristic programmingfunctional uncertainty
Sensitivity (robustness) (93B35) Approximation methods and heuristics in mathematical programming (90C59) Estimation and detection in stochastic control theory (93E10) Optimal stochastic control (93E20)
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Cites Work
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- Towards fully probabilistic control design
- Bayesian system identification
- Adaptive critic methods for stochastic systems with input-dependent noise
- Robust estimation of autoregressive processes using a mixture-based filter-bank
- Fully probabilistic control design
- Introduction to stochastic control theory
- Stochastic adaptive control using multiple models for improved performance in the presence of random disturbances
- Stochastic adaptive control and Martingale limit theory
- Embedding adaptive JLQG into LQ martingale control with a completely observable stochastic control matrix
- Robust $H_{\infty}$ Control for Nonlinear Stochastic Systems: A Sliding-Mode Approach
- ILC-Based Fixed-Structure Controller Design for Output PDF Shaping in Stochastic Systems Using LMI Techniques
- Minimum entropy control of closed-loop tracking errors for dynamic stochastic systems
- Functional adaptive control. An intelligent systems approach
- Polyhedral mixture of linear experts for many-to-one mapping inversion and multiple controllers
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