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Found 8 papers, 0 papers with code
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches
AC-RL controllers are proposed for both classes of systems and shown to lead to online policies that guarantee stability using a high-order tuner and accommodate parametric uncertainties and magnitude limits on the input.
New Algorithms for Discrete-Time Parameter Estimation
We propose two algorithms for discrete-time parameter estimation, one for time-varying parameters under persistent excitation (PE) condition, another for constant parameters under no PE condition.
A High-order Tuner for Accelerated Learning and Control
Gradient-descent based iterative algorithms pervade a variety of problems in estimation, prediction, learning, control, and optimization.
Accurate Parameter Estimation for Risk-aware Autonomous Systems
Experiments are provided to support all theoretical derivations, which show that the spectral lines-based approach outperforms the Gaussian noise-based method when unmodeled dynamics are present, in terms of both parameter estimation error and Regret obtained using the parameter estimates with a Linear Quadratic Regulator in feedback.
Accelerated Learning with Robustness to Adversarial Regressors
Recently, connections with variational approaches have led to the derivation of new learning algorithms with accelerated learning guarantees.
Parameter Estimation in Adaptive Control of Time-Varying Systems Under a Range of Excitation Conditions
The main feature of this algorithm is a matrix of time-varying learning rates, which enables parameter estimation error trajectories to tend exponentially fast towards a compact set whenever excitation conditions are satisfied.
Connections Between Adaptive Control and Optimization in Machine Learning
This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning.
Provably Correct Learning Algorithms in the Presence of Time-Varying Features Using a Variational Perspective
This variational perspective includes higher order learning concepts and normalization, both of which stem from adaptive control, and allows stability to be established for dynamical machine learning problems where time-varying features are present.
