A Hybrid Filtered Basis Functions Approach for Tracking Control of Linear Systems with Unmodeled Nonlinear Dynamics

Chou, Cheng-Hao; Duan, Molong; Okwudire, Chinedum E.

doi:10.1109/case49439.2021.9551476

Cited by 2 publications

(10 citation statements)

References 18 publications

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“…where the L (j) dd matrix is constructed based on ŵ(j) [27] and Eq. ( 7) can be rewritten as follows…”

Section: B Derivation Of Hybrid Fbf Controllermentioning

confidence: 99%

“…It was shown through simulations and experiments that, thanks to the loose structure of its data-driven component, the hybrid model was able to learn unknown nonlinear dynamics and improve overall model prediction compared to its purely physics-based counterpart. A preliminary version [27] of the present paper has incorporated the linear hybrid model into the FBF approach, where it was shown in simulations that the resulting hybrid FBF controller could significantly improve the tracking performance of a system with unmodeled nonlinear dynamics. However, the preliminary investigation failed to address two issues that are key to the practicality of the hybrid FBF controller, namely:…”

mentioning

confidence: 99%

“…Moreover, the preliminary work did not validate the effectiveness of the hybrid FBF controller in experiments. To address these shortcomings, together with its preliminary version [27], the original contributions of this paper are:…”

mentioning

confidence: 99%

See 2 more Smart Citations

A physics-guided data-driven feedforward tracking controller for systems with unmodeled dynamics -- applied to 3D printing

Chou¹,

Duan²,

Okwudire³

2022

Preprint

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A hybrid (i.e., physics-guided data-driven) feedforward tracking controller is proposed for systems with unmodeled linear or nonlinear dynamics. The controller is based on the filtered basis function (FBF) approach, hence it is called a hybrid FBF controller. It formulates the feedforward control input to a system as a linear combination of a set of basis functions whose coefficients are selected to minimize tracking errors. The basis functions are filtered using a combination of two linear models to predict and minimize the tracking errors. The first model is physics-based and remains unaltered during the execution of the controller, while the second is data-driven and is continuously updated during the execution of the controller. To ensure its practicality and safe learning, the proposed hybrid FBF controller is equipped with the ability to handle delays in data acquisition and to detect impending instability due to its inherent data-driven feedback loop. Its effectiveness is demonstrated via application to vibration compensation of a 3D printer with unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF controller, the tracking accuracy of the 3D printer is significantly improved in experiments involving high-speed printing, compared to a standard FBF controller that does not incorporate a data-driven model. Furthermore, the ability of the hybrid FBF controller to detect and, hence, potentially avoid impending instability is demonstrated offline using data collected online from experiments.

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“…where the L (j) dd matrix is constructed based on ŵ(j) [27] and Eq. ( 7) can be rewritten as follows…”

Section: B Derivation Of Hybrid Fbf Controllermentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A physics-guided data-driven feedforward tracking controller for systems with unmodeled dynamics -- applied to 3D printing

Chou¹,

Duan²,

Okwudire³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It was shown through simulations and experiments that, thanks to the loose-structured data-driven component, the hybrid model was able to learn unknown nonlinear dynamics and improve overall model prediction compared to its purely physicsbased counterpart. A preliminary version [32] of the present paper has incorporated the linear hybrid model into the FBF approach, where it was shown in simulations that the resulting hybrid FBF controller could significantly improve the tracking performance of a system with unmodeled nonlinear dynamics. However, the preliminary investigation in [32] failed to address two issues that are key to the practicality of the hybrid FBF controller, namely: (1) how to handle the delays in the data acquisition for the inputs to the data-driven model, and (2) how to assess the stability of the hybrid FBF controller due to the inherent feedback loop introduced by its data-driven component.…”

Section: Introductionmentioning

confidence: 99%

“…A preliminary version [32] of the present paper has incorporated the linear hybrid model into the FBF approach, where it was shown in simulations that the resulting hybrid FBF controller could significantly improve the tracking performance of a system with unmodeled nonlinear dynamics. However, the preliminary investigation in [32] failed to address two issues that are key to the practicality of the hybrid FBF controller, namely: (1) how to handle the delays in the data acquisition for the inputs to the data-driven model, and (2) how to assess the stability of the hybrid FBF controller due to the inherent feedback loop introduced by its data-driven component. Moreover, the preliminary work did not validate the effectiveness of the hybrid FBF controller in experiments.…”

Section: Introductionmentioning

confidence: 99%

A Physics-Guided Data-Driven Feedforward Tracking Controller for Systems With Unmodeled Dynamics—Applied to 3D Printing

2023

Self Cite

View full text Add to dashboard Cite

A hybrid (i.e., physics-guided data-driven) feedforward tracking controller is proposed for systems with unmodeled linear or nonlinear dynamics. The proposed controller is based on the filtered basis functions (FBF) approach, and hence called a hybrid FBF controller. It formulates the feedforward control input to a system as a linear combination of a set of basis functions whose coefficients are selected to minimize tracking errors. To predict the system response and thereby the tracking errors, the basis functions are filtered using a combination of two linear models. The first model is physics-based and remains unaltered during the execution of the controller, while the second is data-driven and is continuously updated during the execution of the controller. To ensure its practicality and safe learning, the proposed hybrid FBF controller is equipped with the abilities to handle delays in data acquisition and to detect impending instability due to its inherent data-driven feedback loop. The effectiveness of the hybrid FBF controller is demonstrated via application to vibration compensation of a 3D printer with unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF controller, the tracking accuracy of the 3D printer and the print quality are both significantly improved in experiments involving high-speed printing, compared to standard FBF controller that does not incorporate a data-driven model. Furthermore, the ability of the hybrid FBF controller to detect, and hence to potentially avoid, impending instability is demonstrated offline using data collected online from experiments.

show abstract

A Hybrid Filtered Basis Functions Approach for Tracking Control of Linear Systems with Unmodeled Nonlinear Dynamics

Cited by 2 publications

References 18 publications

A physics-guided data-driven feedforward tracking controller for systems with unmodeled dynamics -- applied to 3D printing

A physics-guided data-driven feedforward tracking controller for systems with unmodeled dynamics -- applied to 3D printing

A Physics-Guided Data-Driven Feedforward Tracking Controller for Systems With Unmodeled Dynamics—Applied to 3D Printing

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