Composite learning control with application to inverted pendulums

Pan, Lin; Yu, Haoyong

doi:10.1109/cac.2015.7382502

Cited by 18 publications

(13 citation statements)

References 34 publications

(31 reference statements)

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“…The first proposed relaxation method and those in other works [24][25][26][27][28][29] store information via integration. The second method uses delay, which is more close to concurrent learning [20][21][22][23] with direct using of the previous measurements in the estimator.…”

Section: Resultsmentioning

confidence: 99%

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Relaxation for online frequency estimator of bias‐affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing

Vedyakov

Vediakova

Bobtsov

et al. 2019

Adaptive Control & Signal

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This paper considers the problem of continuous-time online frequency estimation for a biased damped sinusoidal signal. The previous result for a sinusoidal signal with time-varying amplitude requires a persistency of excitation condition for regressor, which is not satisfied in the considered case. To relax this condition, we propose to use Dynamic Regressor Extension and Mixing method on the first step to replace nth-order regression with n first-order regression models. On the second step, two simple relaxation methods are proposed to establish necessary excitation for the first-order gradient-based estimator. The efficiency of the proposed approach is demonstrated through the set of numerical simulations for the exponentially damped sinusoidal signal.

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Section: Resultsmentioning

confidence: 99%

“…Another method is called composite learning, which is proposed in the work of Pan et al 24 and developed in other works of Pan et al 25,26 This method also utilizes both current and online recorded data. However, there are not the history stack and measurements selecting algorithm.…”

mentioning

confidence: 99%

Relaxation for online frequency estimator of bias‐affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing

Vedyakov

Vediakova

Bobtsov

et al. 2019

Adaptive Control & Signal

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“…Simulation results have shown that the proposed approach possesses superior control performances under both noise‐free and noisy‐measurement environments. Further work would focus on composite learning control using online recorded data .…”

Section: Discussionmentioning

confidence: 99%

Online data‐driven composite adaptive backstepping control with exact differentiators

Liu

2015

Adaptive Control & Signal

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This paper presents an online data-driven composite adaptive backstepping control for a class of parametric strict-feedback nonlinear systems with mismatched uncertainties, where both tracking errors and prediction errors are utilized to update parametric estimates. Hybrid exact differentiators are applied to obtain the derivatives of virtual control inputs such that the complexity problem of integrator backstepping can be avoided. Closed-loop tracking error equations are integrated in a moving-time window to generate prediction errors such that online recorded data can be utilized to improve parameter adaptation. Semiglobal asymptotic stability of the closed-loop system is rigorously established by the time-scales separation and Lyapunov synthesis. The proposed composite adaptation can not only avoid the application of identification models and linear filters resulting in a simpler control structure, but also suppress parametric uncertainties and external perturbations via the time-interval integral. Simulation results have demonstrated that the proposed approach possesses superior control performances under both noise-free and noisy-measurement environments.

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“…Define the vector Y e and the matrix M e as given in (7). Consider the estimator (11) with φ and Y i defined in (10) and 9, respectively. The implication (13) holds.…”

Section: B Proposed Dynamic Regressor Extension and Mixing Proceduresmentioning

confidence: 99%

Parameters estimation via dynamic regressor extension and mixing

Aranovskiy

Bobtsov

Ortega

et al. 2016

2016 American Control Conference (ACC)

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A new way to design parameter estimators with enhanced performance is proposed in the paper. The procedure consists of two stages, first, the generation of new regression forms via the application of a dynamic operator to the original regression. Second, a suitable mix of these new regressors to obtain the final desired regression form. For classical linear regression forms the procedure yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation. The technique is also applied to nonlinear regressions with "partially" monotonic parameter dependence-giving rise again to estimators with enhanced performance. Simulation results illustrate the advantages of the proposed procedure in both scenarios.

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Composite learning control with application to inverted pendulums

Cited by 18 publications

References 34 publications

Relaxation for online frequency estimator of bias‐affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing

Relaxation for online frequency estimator of bias‐affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing

Online data‐driven composite adaptive backstepping control with exact differentiators

Parameters estimation via dynamic regressor extension and mixing

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