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Found 26 papers, 2 papers with code
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty.
Learning Soft Constrained MPC Value Functions: Efficient MPC Design and Implementation providing Stability and Safety Guarantees
This combination enables us to obtain a corresponding optimal control law, which can be implemented efficiently on embedded platforms.
The Role of Identification in Data-driven Policy Iteration: A System Theoretic Study
By casting the concurrent model identification and control design as a feedback interconnection between two algorithmic systems, we provide a closed-loop analysis that shows convergence and robustness properties for arbitrary levels of excitation in the data.
Closed-Loop Finite-Time Analysis of Suboptimal Online Control
Finite-time guarantees allow the control design to distribute a limited computational budget over a time horizon and estimate the on-the-go loss in performance due to sub-optimality.
Stochastic Data-Driven Predictive Control: Regularization, Estimation, and Constraint Tightening
An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction.
A harmonic framework for the identification of linear time-periodic systems
This method is based on harmonic modeling and consists in converting any LTP system into an equivalent LTI system with infinite dimension.
An Online Learning Analysis of Minimax Adaptive Control
We present an online learning analysis of minimax adaptive control for the case where the uncertainty includes a finite set of linear dynamical systems.
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions.
Dual adaptive MPC using an exact set-membership reformulation
Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work.
Implications of Regret on Stability of Linear Dynamical Systems
The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for online learning.
Stochastic MPC with robustness to bounded parametric uncertainty
assumption on the noise distribution, we also provide an average asymptotic performance bound for the l2-norm of the closed-loop state.
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the goal of steering the system toward a desired reference trajectory.
Scalable tube model predictive control of uncertain linear systems using ellipsoidal sets
This contrasts with most existing tube MPC strategies using polytopic sets in the state tube, which are difficult to design and whose complexity grows combinatorially with the system order.
Computationally efficient robust MPC using optimized constraint tightening
A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances.
Kernel-Based Identification of Local Limit Cycle Dynamics with Linear Periodically Parameter-Varying Models
Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories.
Infinite-Dimensional Sparse Learning in Linear System Identification
Atomic norm regularization decomposes the transfer function into first-order atomic models and solves a group lasso problem that selects a sparse set of poles and identifies the corresponding coefficients.
Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates
In this paper, confidence regions are provided for these stochastic predictors based on the prediction error distribution.
A distributed framework for linear adaptive MPC
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation.
Design of input for data-driven simulation with Hankel and Page matrices
The paper deals with the problem of designing informative input trajectories for data-driven simulation.
The Balanced Mode Decomposition Algorithm for Data-Driven LPV Low-Order Models of Aeroservoelastic Systems
The algorithm formulation hinges on the idea of replacing the orthogonal projection onto the Proper Orthogonal Decomposition modes, used in Dynamic Mode Decomposition-based approaches, with a balancing oblique projection constructed entirely from data.
Experiment design for impulse response identification with signal matrix models
This paper formulates an input design approach for truncated infinite impulse response identification in the context of implicit model representations recently used as basis for data-driven simulation and control approaches.
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data.
Maximum Likelihood Estimation in Data-Driven Modeling and Control
The second one applies the signal matrix model as the predictor in predictive control.
Learning Dynamical Systems using Local Stability Priors
A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed.
Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs
The response is estimated with an ensemble of input-output data with periodic inputs.
Structured exploration in the finite horizon linear quadratic dual control problem
This paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty.
