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Neural Luenberger state observer for nonautonomous nonlinear systems
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DOI：10.1016/j.jprocont.2026.103731.png)
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Abstract
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• Model-free observer synthesis for nonlinear systems with manipulated inputs. • Luenberger-like observer containing input-affine terms accounting for input effects. • Observer output mapping and input-affine terms learned as neural networks. • Numerical case studies on a bioreactor and a Williams-Otto reactor.
Keywords:
State observation
Nonlinear dynamics
Neural networks
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