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Found 6 papers, 0 papers with code
Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for unstructured uncertainties in nonlinear dynamical systems.
Composite Adaptive Lyapunov-Based Deep Neural Network (Lb-DNN) Controller
Recent advancements in adaptive control have equipped deep neural network (DNN)-based controllers with Lyapunov-based adaptation laws that work across a range of DNN architectures to uniquely enable online learning.
Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller
Simulation results of the developed dropout DNN-based adaptive controller indicate a 38. 32% improvement in the tracking error, a 53. 67% improvement in the function approximation error, and 50. 44% lower control effort when compared to a baseline adaptive DNN-based controller without dropout regularization.
Distributed State Estimation with Deep Neural Networks for Uncertain Nonlinear Systems under Event-Triggered Communication
Distributed state estimation is examined for a sensor network tasked with reconstructing a system's state through the use of a distributed and event-triggered observer.
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking
In this paper, we develop safe reinforcement-learning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specifications, similar to those required by search-and-rescue missions.
Efficient model-based reinforcement learning for approximate online optimal
In this paper the infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using the state following (StaF) kernel method to approximate the value function.
Model-based Reinforcement Learning
reinforcement-learning
+1
