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Found 11 papers, 1 papers with code
Uncertainty-Aware Guidance for Target Tracking subject to Intermittent Measurements using Motion Model Learning
This neural network (NN)-based motion model serves as the prediction step in a particle filter for target state estimation and uncertainty quantification.
An adaptive optimal control approach to monocular depth observability maximization
This paper presents an integral concurrent learning (ICL)-based observer for a monocular camera to accurately estimate the Euclidean distance to features on a stationary object, under the restriction that state information is unavailable.
Defending a Static Target Point with a Slow Defender
This paper studies a target-defense game played between a slow defender and a fast attacker.
Deep Nonlinear Adaptive Control for Unmanned Aerial Systems Operating under Dynamic Uncertainties
Recent literature in the field of machine learning (ML) control has shown promising theoretical results for a Deep Neural Network (DNN) based Nonlinear Adaptive Controller (DNAC) capable of achieving trajectory tracking for nonlinear systems.
State and Parameter Estimation for Affine Nonlinear Systems
Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances.
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model
Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances.
Model-based Reinforcement Learning
reinforcement-learning
+1
Guarding a Non-Maneuverable Translating Line with an Attached Defender
In this paper we consider a target-guarding differential game where the defender must protect a linearly translating line-segment by intercepting an attacker who tries to reach it.
A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games
Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions.
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning
Safe model-based reinforcement learning techniques based on a barrier transformation have recently been developed to address this problem.
Model-based Reinforcement Learning
reinforcement-learning
+1
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems
The ability to learn and execute optimal control policies safely is critical to realization of complex autonomy, especially where task restarts are not available and/or the systems are safety-critical.
Model-based Reinforcement Learning
reinforcement-learning
+1
Safe Model-Based Reinforcement Learning for Systems with Parametric Uncertainties
In recent years, reinforcement learning approaches that rely on persistent excitation have been combined with a barrier transformation to learn the optimal control policies under state constraints.
Model-based Reinforcement Learning
reinforcement-learning
+2
