21 research outputs found
Multi-Parametric Extremum Seeking-based Auto-Tuning for Robust Input-Output Linearization Control
We study in this paper the problem of iterative feedback gains tuning for a
class of nonlinear systems. We consider Input-Output linearizable nonlinear
systems with additive uncertainties. We first design a nominal Input-Output
linearization-based controller that ensures global uniform boundedness of the
output tracking error dynamics. Then, we complement the robust controller with
a model-free multi-parametric extremum seeking (MES) control to iteratively
auto-tune the feedback gains. We analyze the stability of the whole controller,
i.e. robust nonlinear controller plus model-free learning algorithm. We use
numerical tests to demonstrate the performance of this method on a mechatronics
example.Comment: To appear at the IEEE CDC 201
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
Many real-world domains require safe decision making in uncertain
environments. In this work, we introduce a deep reinforcement learning
framework for approaching this important problem. We consider a distribution
over transition models, and apply a risk-averse perspective towards model
uncertainty through the use of coherent distortion risk measures. We provide
robustness guarantees for this framework by showing it is equivalent to a
specific class of distributionally robust safe reinforcement learning problems.
Unlike existing approaches to robustness in deep reinforcement learning,
however, our formulation does not involve minimax optimization. This leads to
an efficient, model-free implementation of our approach that only requires
standard data collection from a single training environment. In experiments on
continuous control tasks with safety constraints, we demonstrate that our
framework produces robust performance and safety at deployment time across a
range of perturbed test environments.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS
2023
Fixed-Time Stable Proximal Dynamical System for Solving MVIPs
In this paper, a novel modified proximal dynamical system is proposed to
compute the solution of a mixed variational inequality problem (MVIP) within a
fixed time, where the time of convergence is finite, and is uniformly bounded
for all initial conditions. Under the assumptions of strong monotonicity and
Lipschitz continuity, it is shown that a solution of the modified proximal
dynamical system exists, is uniquely determined and converges to the unique
solution of the associated MVIP within a fixed time. As a special case for
solving variational inequality problems, the modified proximal dynamical system
reduces to a fixed-time stable projected dynamical system. Furthermore, the
fixed-time stability of the modified projected dynamical system continues to
hold, even if the assumption of strong monotonicity is relaxed to that of
strong pseudomonotonicity. Connections to convex optimization problems are
discussed, and commonly studied dynamical systems in the continuous-time
optimization literature follow as special limiting cases of the modified
proximal dynamical system proposed in this paper. Finally, it is shown that the
solution obtained using the forward-Euler discretization of the proposed
modified proximal dynamical system converges to an arbitrarily small
neighborhood of the solution of the associated MVIP within a fixed number of
time steps, independent of the initial conditions. Two numerical examples are
presented to substantiate the theoretical convergence guarantees.Comment: 12 pages, 5 figure