Survey of industrial optimized adaptive control

Martín‐Sánchez, Juan M.; Lemos, João M.; Rodellar, José

doi:10.1002/acs.2313

Cited by 30 publications

(15 citation statements)

References 131 publications

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“…with z := [e T ,W T ] T ∈ R n+N for the closed-loop dynamics composed of (6) and (8). It follows from the classical MRAC result of [17] that ifŴ (0) ∈ Ω cw and Φ(x) is of PE, then the closed-loop system (6) with (8) achieves global exponential stability in the sense that both the tracking error e(t) and the estimation errorW (t) converge to 0.…”

Section: A Review Of Previous Resultsmentioning

confidence: 99%

Composite learning control with application to inverted pendulums

Pan¹,

Yu²

2015

2015 Chinese Automation Congress (CAC)

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Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC has been verified by the application to an inverted pendulum control problem.

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Section: A Review Of Previous Resultsmentioning

confidence: 99%

Composite learning control with application to inverted pendulums

Pan¹,

Yu²

2015

2015 Chinese Automation Congress (CAC)

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“…As a major control technique of handling parametric uncertainties, adaptive control still remains among the most active research fields in the control community. [1][2][3][4][5][6][7] Usually, adaptive control only achieves asymptotic convergence of tracking errors and does not guarantee convergence of parameter estimation errors without a condition termed persistent excitation (PE). 8 Parameter convergence in adaptive control is desirable as it enhances the overall stability and robustness properties of the closed-loop system, 9 where the robustness properties result from closed-loop exponential stability, and detailed analysis can be referred to the work of Sastry and Bodson.…”

Section: Introductionmentioning

confidence: 99%

Efficient learning from adaptive control under sufficient excitation

Pan

Aranovskiy

Bobtsov

et al. 2019

Intl J Robust & Nonlinear

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Summary Parameter convergence is desirable in adaptive control as it enhances the overall stability and robustness properties of the closed‐loop system. In existing online historical data (OHD)–driven parameter learning schemes, all OHD are exploited to update parameter estimates such that parameter convergence is guaranteed under a sufficient excitation (SE) condition which is strictly weaker than the classical persistent excitation condition. Nevertheless, the exploitation of all OHD not only results in possible unbounded adaptation but also loses the flexibility of handling slowly time‐varying uncertainties. This paper presents an efficient OHD‐driven parameter learning scheme for adaptive control, where a variable forgetting factor is specifically designed and is equipped with an estimation error feedback such that exponential parameter convergence is achieved under the SE condition without the aforesaid drawbacks. The proposed parameter learning scheme is incorporated with direct adaptive control to construct an OHD‐based composite learning control strategy. Numerical results have verified the effectiveness of the proposed approach.

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“…Alternatively, a data-based control law design may be suitable for gust load alleviation. The data-based autoregressive model is a good tool to construct a discrete model without the theoretical continuous mathematical model [9]. It is found to be effective in aeroelastic modeling for online flutter prediction [10].…”

Section: Introductionmentioning

confidence: 99%

GPC-Based Gust Response Alleviation for Aircraft Model Adapting to Various Flow Velocities in the Wind Tunnel

Dai

Yang

2015

Shock and Vibration

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A unified autoregressive (AR) model is identified, based on the wind tunnel test data of open-loop gust response for an aircraft model. The identified AR model can be adapted to various flow velocities in the wind tunnel test. Due to the lack of discrete gust input measurement, a second-order polynomial function is used to approximate the gust input amplitude by flow velocity. Afterwards, with the identified online aeroelastic model, the modified generalized predictive control (GPC) theory is applied to alleviate wing tip acceleration induced by sinusoidal gust. Finally, the alleviation effects of gust response at different flow velocities are estimated based on the comparison of simulated closed-loop acceleration with experimental open-loop one. The comparison indicates that, after gust response alleviation, the wing tip acceleration can be reduced up to 20% at the tested velocities ranging from 12 m/s to 24 m/s. Demonstratively, the unified control law can be adapted to varying wind tunnel velocities and gust frequencies. It does not need to be altered at different test conditions, which will save the idle time.

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Survey of industrial optimized adaptive control

Cited by 30 publications

References 131 publications

Composite learning control with application to inverted pendulums

Composite learning control with application to inverted pendulums

Efficient learning from adaptive control under sufficient excitation

GPC-Based Gust Response Alleviation for Aircraft Model Adapting to Various Flow Velocities in the Wind Tunnel

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