Learning from adaptive control under relaxed excitation conditions

Bobtsov, Alexey A.; Darouach, Mohamed; Joo, Young Hoon

doi:10.1002/acs.3075

Cited by 17 publications

(6 citation statements)

References 25 publications

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“…Obviously, Assumption 1 is often used in parameter estimation algorithms 29 and adaptive control designs. 37 Assumption 3 is important to ensure the controllability of the nonlinear system (12).…”

Section: Problem Formulationmentioning

confidence: 99%

“…Even though the PE condition can be satisfied, the slow learning process will be acquired due to the parameter estimation speed seriously relying on the PE strength. 12 Therefore, some advanced learning based methods such as parameter estimation using dynamic regressor extension and mixing (DREM), 13 date memory-driven learning algorithms including composite learning [14][15][16][17][18][19][20][21][22] and concurrent learning, [23][24][25][26] are proposed to relax this stringent PE condition. However, most of those works are only able to ensure at most exponential or asymptotic convergence to the neighborhood of the estimated parameters, whose parameter estimation errors will approach to zero as time goes to infinite.…”

Section: Introductionmentioning

confidence: 99%

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Composite learning adaptive dynamic surface control for uncertain nonlinear strict‐feedback systems with fixed‐time parameter estimation under sufficient excitation

Guo

Liu

et al. 2021

Intl J Robust & Nonlinear

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This paper presents a novel practical fixed-time parameter identification algorithm and a composite learning based practical fixed-time adaptive dynamic surface control (DSC) scheme for nonlinear strict-feedback systems subject to linear-in-parameters uncertainties. The convergence of conventional parameter estimation algorithms often requires a restrictive prerequisite termed persistent excitation (PE) condition. By contrast, a new fixed-time parameter identification algorithm configured with two layer transformation technique is firstly proposed under relaxed sufficient excitation condition rather than strict PE condition. The key point of avoiding PE condition is by introducing a smooth switching function to adjust the forgetting factor in the filtered regressor dynamics. Instead of using the fractional power of the tracking errors to construct the control laws, a smooth hyperbolic tangent function based adaptive DSC scheme is designed such that the potential singular problem caused by time derivations of virtual control laws in back-stepping algorithm is avoided. Integrating the parameter identification algorithm into the adaptive DSC scheme, a composite learning based control is formed to guarantee the practical fixed-time convergence of parameter estimation errors and tracking errors. Comparative simulation results are given to illustrate the effectiveness of the proposed algorithm.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Composite learning adaptive dynamic surface control for uncertain nonlinear strict‐feedback systems with fixed‐time parameter estimation under sufficient excitation

Guo

Liu

et al. 2021

Intl J Robust & Nonlinear

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“…These techniques include adaptive control strategies (Aghababa, 2019;Tran and Kang, 2015), sliding mode control technique (Fang et al, 2019), state-space linearization method (Rana et al, 2019), sample-data control technique (Liu et al, 2018b), and linear parameter varying controller (Hsu and Bhattacharya, 2020), among others. The adaptive control strategy is an effective control technique that slowly controls time-varying dynamic systems (Pan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Robust attitude control of the three-dimensional unknown chaotic satellite system

Shafiq

Ahmad

Almatroud

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper proposes a novel continuous-time robust direct adaptive controller for the attitude control of the three-dimensional unknown chaotic spacecraft system. It considers that the plant’s nonlinear terms, exogenous disturbances, and model uncertainties are unknown and bounded; the controller design is independent of the system’s nonlinear terms. These controller attributes flourish the robust performance of the closed-loop and establish smooth state vector convergence to zero. The proposed controller consists of three parts: (1) a linear controller establishes the stability of the closed-loop at the origin, (2) a nonlinear controller component that autonomously adjusts the feedback gain, and (3) a nonlinear adaptive controller compensates for the model uncertainties and external disturbances using the online estimates of bounds and model uncertainties. The output of this part remains within a given upper and lower bound. The feedback controller gain is large when the state variables are away from the origin and become small in the origin’s vicinity. This feature is novel and contributes to the synthesis of smooth control effort that establishes robust fast and oscillation-free convergence of the state variables to zero. The Lyapunov direct stability analysis assures the global asymptotic robust stability of the closed-loop. Computer simulations and comparative analysis are included to verify the theoretical findings.

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“…Adaptive control had its peak in 1970–1990 when multiple classic results on closed‐loop control and parameter estimation were reported, such as References 1–3, and it is still of interest in our time. The current research directions of direct adaptive control address the problems of robustness and guaranteed performance, whereas one of the principal research axes of indirect adaptation and parameter estimation is the relaxation of excitation requirements 4 . These researches gave rise to various modern results of adaptive control, such as concurrent learning, 5 composite learning, 6 dynamic regressor extension and mixing, 7 as well as regulation methods, for example, those inspired by recent data‐driven learning advances 8 .…”

mentioning

confidence: 99%

“…The current research directions of direct adaptive control address the problems of robustness and guaranteed performance, whereas one of the principal research axes of indirect adaptation and parameter estimation is the relaxation of excitation requirements. 4 These researches gave rise to various modern results of adaptive control, such as concurrent learning, 5 composite learning, 6 dynamic regressor extension and mixing, 7 as well as regulation methods, for example, those inspired by recent data-driven learning advances. 8 This special issue aims to provide state-of-the-art developments of new approaches in adaptive control, both direct and indirect, covering various topics of adaptive systems and their applications.The issue includes seven papers that can be divided into three groups.…”

mentioning

confidence: 99%

New results on adaptive systems

Aranovskiy

Bobtsov

Pyrkin

2022

Adaptive Control & Signal

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Adaptive control had its peak in when multiple classic results on closed-loop control and parameter estimation were reported, such as References 1-3, and it is still of interest in our time. The current research directions of direct adaptive control address the problems of robustness and guaranteed performance, whereas one of the principal research axes of indirect adaptation and parameter estimation is the relaxation of excitation requirements. 4 These researches gave rise to various modern results of adaptive control, such as concurrent learning, 5 composite learning, 6 dynamic regressor extension and mixing, 7 as well as regulation methods, for example, those inspired by recent data-driven learning advances. 8 This special issue aims to provide state-of-the-art developments of new approaches in adaptive control, both direct and indirect, covering various topics of adaptive systems and their applications.The issue includes seven papers that can be divided into three groups. The first group focuses on direct adaptive control and its robustness to model uncertainties and disturbances. The second group studies adaptive parameter estimation under less restrictive excitation requirements than classic approaches. The third group unites these topics by addressing indirect adaptive solutions to control problems.The first group includes two papers, and both pay special attention to the robustness to parametric and signal uncertainties.• Rodrigues, Hsu, Oliveira, and Fridman, in their paper "Adaptive sliding mode control with guaranteed performance based on monitoring and barrier functions," propose a solution addressing both transient and tracking performance guarantees using a novel combination of two adaptation tools: monitoring and barrier function. The authors illustrate the benefits of the proposed method with an anti-lock braking system example.

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Learning from adaptive control under relaxed excitation conditions

Cited by 17 publications

References 25 publications

Composite learning adaptive dynamic surface control for uncertain nonlinear strict‐feedback systems with fixed‐time parameter estimation under sufficient excitation

Composite learning adaptive dynamic surface control for uncertain nonlinear strict‐feedback systems with fixed‐time parameter estimation under sufficient excitation

Robust attitude control of the three-dimensional unknown chaotic satellite system

New results on adaptive systems

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