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Universal Adaptive Control of Nonlinear Systems
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TLDR
In this paper, a universal adaptive control framework that extends the certainty equivalence principle to nonlinear systems with unmatched uncertainties is developed, which can handle model variations while achieving the desired level of performance.Abstract:
High-performance feedback control requires an accurate model of the underlying dynamical system which is often difficult, expensive, or time-consuming to obtain. Online model learning is an attractive approach that can handle model variations while achieving the desired level of performance. However, most model learning methods developed within adaptive nonlinear control are limited to certain types of uncertainties, called matched uncertainties, because the certainty equivalency principle can be employed in the design phase. This work develops a universal adaptive control framework that extends the certainty equivalence principle to nonlinear systems with unmatched uncertainties through two key innovations. The first is introducing parameter-dependent storage functions that guarantee closed-loop tracking of a desired trajectory generated by an adapting reference model. The second is modulating the learning rate so the closed-loop system remains stable during the learning transients. The analysis is first presented under the lens of contraction theory, and then expanded to general Lyapunov functions which can be synthesized via feedback linearization, backstepping, or optimization-based techniques. The proposed approach is more general than existing methods as the uncertainties can be unmatched and the system only needs to be stabilizable. The developed algorithm can be combined with learned feedback policies, facilitating transfer learning and bridging the sim-to-real gap. Simulation results showcase the methodread more
Citations
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Journal ArticleDOI
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
TL;DR: Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other as mentioned in this paper.
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Learning-based Adaptive Control via Contraction Theory.
TL;DR: In this article, a deep learning-based adaptive control framework for nonlinear systems with multiplicatively separable parametrization, called aNCM -for adaptive Neural Contraction Metric is presented.
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A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability.
TL;DR: The Neural Contraction Metric (NCM) as mentioned in this paper is a neural network model of an optimal contraction metric and corresponding differential Lyapunov function, which is a necessary and sufficient condition for incremental exponential stability of non-autonomous nonlinear system trajectories.
Journal ArticleDOI
Adaptive Actuator Failure Compensation on the Basis of Contraction Metrics
TL;DR: In this paper , an adaptive actuator failure compensation method for nonlinear systems with unmatched parametric uncertainty based on contraction metrics is proposed, which ensures the closed-loop stability and asymptotic tracking of the desired trajectory in the presence of actuator failures.
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Adaptive Variants of Optimal Feedback Policies.
TL;DR: In this paper, the authors combine adaptive control directly with optimal or near-optimal value functions to enhance stability and closed-loop performance in systems with parametric uncertainties, and prove asymptotic closed loop convergence of adaptive feedback controllers derived from optimization-based policies.
References
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Book
Applied Nonlinear Control
TL;DR: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
Proceedings ArticleDOI
YALMIP : a toolbox for modeling and optimization in MATLAB
TL;DR: Free MATLAB toolbox YALMIP is introduced, developed initially to model SDPs and solve these by interfacing eternal solvers by making development of optimization problems in general, and control oriented SDP problems in particular, extremely simple.
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Nonlinear and adaptive control design
TL;DR: In this paper, the focus is on adaptive nonlinear control results introduced with the new recursive design methodology -adaptive backstepping, and basic tools for nonadaptive BackStepping design with state and output feedbacks.
Proceedings ArticleDOI
Robust adaptive control
Petros Ioannou,Jing Sun +1 more
TL;DR: In this article, the authors present a model for dynamic control systems based on Adaptive Control System Design Steps (ACDS) with Adaptive Observers and Parameter Identifiers.
Journal ArticleDOI
On the adaptive control of robot manipulators
Jean-Jacques E. Slotine,W. Li +1 more
TL;DR: In this paper, an adaptive robot control algorithm is derived, which consists of a PD feedback part and a full dynamics feed for the compensation part, with the unknown manipulator and payload parameters being estimated online.