Search Results for author:
Found 18 papers, 1 papers with code
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.
Pilot Performance modeling via observer-based inverse reinforcement learning
The observer is shown to converge to one of the equivalent solutions of the IRL problem.
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.
Fault Detection via Occupation Kernel Principal Component Analysis
The reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamical system.
Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning
A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions.
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
Carleman Lifting for Nonlinear System Identification with Guaranteed Error Bounds
This paper concerns identification of uncontrolled or closed loop nonlinear systems using a set of trajectories that are generated by the system in a domain of attraction.
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
Singular Dynamic Mode Decompositions
This manuscript is aimed at addressing several long standing limitations of dynamic mode decompositions in the application of Koopman analysis.
The kernel perspective on dynamic mode decomposition
This manuscript revisits theoretical assumptions concerning dynamic mode decomposition (DMD) of Koopman operators, including the existence of lattices of eigenfunctions, common eigenfunctions between Koopman operators, and boundedness and compactness of Koopman operators.
Control Occupation Kernel Regression for Nonlinear Control-Affine Systems
As the fundamental basis elements leveraged in approximation, higher order control occupation kernels represent iterated integration after multiplication by a given controller in a vector valued reproducing kernel Hilbert space.
Dynamic Mode Decomposition with Control Liouville Operators
A given feedback controller is represented through a multiplication operator and a composition of the control Liouville operator and the multiplication operator is used to express the nonlinear closed-loop system as a linear total derivative operator on RKHSs.
Optimization and Control Functional Analysis 37N35, 93B30
Motion Tomography via Occupation Kernels
The goal of motion tomography is to recover the description of a vector flow field using information about the trajectory of a sensing unit.
Optimization and Control Functional Analysis 93-08, 46E22
Theoretical Foundations for the Dynamic Mode Decomposition of High Order Dynamical Systems
Conventionally, data driven identification and control problems for higher order dynamical systems are solved by augmenting the system state by the derivatives of the output to formulate first order dynamical systems in higher dimensions.
Optimization and Control Functional Analysis 93-08, 46E22
Online Observer-Based Inverse Reinforcement Learning
In this paper, a novel approach to the output-feedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem.
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
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
