Composite Adaptive Attitude-Tracking Control With Parameter Convergence Under Finite Excitation

Dong, Hongyang; Hu, Qinglei; Akella, Maruthi R.; Yang, Haoyang

doi:10.1109/tcst.2019.2942802

Cited by 24 publications

(6 citation statements)

References 20 publications

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“…Definition 4 [41], Finite Excitation: A bounded signal gðÁÞ: R ! R nÂm is said to be finite exciting (FE) if there exist positive constants a and c such that in time interval ½t; t þ c, one has R tþc t gðsÞg T ðsÞds !…”

Section: Remarkmentioning

confidence: 99%

Learning-based nonlinear model predictive control with accurate uncertainty compensation

2021

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A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and input constraints. The proposed LBNMPC strategy decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system’s safety and stability, and the learned model aims to improve the tracking behaviors. As a core of the learned model construction, an online parameter estimator is designed to deal with system uncertainties. This estimation process effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods. By constructing an invariant terminal constraint set, we prove that the LBNMPC is recursively feasible and robustly asymptotically stable. Numerical verifications for a two-link manipulator are conducted to validate the effectiveness and robustness of the proposed control scheme.

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Section: Remarkmentioning

confidence: 99%

Learning-based nonlinear model predictive control with accurate uncertainty compensation

2021

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“…In the traditional CL framework [29,37,38], the state derivative information is assumed to be known or needs to be calculated by noncausal numerical smoothing techniques. These numerical processes are usually vulnerable to approximation errors, and also lay a big barrier for the rigorous theoretical analysis [39]. In this paper, benefited from the extended design, the state derivative information is estimated simultaneously with the state.…”

Section: A Observer Designmentioning

confidence: 99%

Adaptive Observation-Based Efficient Reinforcement Learning for Uncertain Systems

Ran

Xie

2020

Preprint

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This paper develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the system state and parameter. This observer has a two-timescale structure and doesn't require any additional numerical techniques to calculate the state derivative information. The idea of concurrent learning (CL) is leveraged to use the recorded data, which leads to a relaxed verifiable excitation condition for the convergence of parameter estimation. Based on the estimated state and parameter provided by the CL-AEO, a simulation of experience based RL scheme is developed to online approximate the optimal control policy. Rigorous theoretical analysis is given to show that the practical convergence of the system state to the origin and the developed policy to the ideal optimal policy can be achieved without the persistence of excitation (PE) condition. Finally, the effectiveness and superiority of the developed methodology are demonstrated via comparative simulations.

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“…Besides, due to the inherent limitations arising from the CE design structure, CE adaptive controllers may result in significant performance degradation [2], [3] when compared with the corresponding deterministic-case controllers. Some non-CE adaptive control schemes, such as the immersionand-invariance method [4], [5] and its extensions [6], [7], can partially address these limitations and achieve improved closed-loop performance. But it is still a challenging problem to quantitatively analyze or characterize the performance of these non-CE adaptive controllers.…”

Section: Introductionmentioning

confidence: 99%

Optimal Tracking Control for Uncertain Nonlinear Systems With Prescribed Performance via Critic-Only ADP

Dong

Zhao

Luo

2022

IEEE Trans. Syst. Man Cybern, Syst.

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Composite Adaptive Attitude-Tracking Control With Parameter Convergence Under Finite Excitation

Cited by 24 publications

References 20 publications

Learning-based nonlinear model predictive control with accurate uncertainty compensation

Learning-based nonlinear model predictive control with accurate uncertainty compensation

Adaptive Observation-Based Efficient Reinforcement Learning for Uncertain Systems

Optimal Tracking Control for Uncertain Nonlinear Systems With Prescribed Performance via Critic-Only ADP

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