Dec 29, 2023 · This letter develops a Lyapunov-based (Lb-) LSTM observer for state estimation in nonlinear systems. The Lb-LSTM weights adapt in real-time using Lyapunov- ...
Jan 29, 2024 · Abstract—Long short-term memory (LSTM) neural networks excel at capturing short- and long-term dependencies, making them powerful tools for ...
Aug 7, 2023 · Abstract—Recurrent neural networks (RNNs) are a dynamic mapping that can capture time-varying, accumu- lative effects in a sequence that ...
This paper develops a Lyapunov-based (Lb-) LSTM observer for state estimation in nonlinear systems. The Lb-LSTM weights adapt in real-time using Lyapunov-based ...
A Lyapunov-based stability analysis is performed to guarantee uniform ultimate boundedness (UUB) of the tracking errors and LSTM state and weight estimation ...
A novel on-line learning adaptive control scheme based on linear neuron is presented to facilitate controller design of unknown nonlinear dynamic system.
Lyapunov-Based Long Short-Term Memory (Lb-LSTM) Neural Network-Based Adaptive Observer. EJ Griffis, OS Patil, RG Hart, WE Dixon. IEEE Control Systems Letters ...
In this paper, an adaptive deep RNN observer is developed for identification of a class of nonlinear systems.
Lyapunov-Based Long Short-Term Memory (Lb-LSTM) Neural Network-Based Adaptive Observer. EJ Griffis, OS Patil, RG Hart, WE Dixon. IEEE Control Systems Letters ...
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Dec 31, 2023 · This letter introduces the first physics-informed LSTM (PI-LSTM) controller composed of DNNs and LSTMs, where the weight adaptation laws are designed from a ...
Missing: (Lb- Observer.