Multiple models and second level adaptation for a class of nonlinear systems with nonlinear parameterization

Pandey, Vinay Kumar; Kar, Indrani; Mahanta, Chitralekha

doi:10.1109/iciinfs.2014.7036598

Cited by 8 publications

(6 citation statements)

References 17 publications

(19 reference statements)

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“…Based on the chain of integrators (19), a model predictive controller is developed to get the desired trajectory tracking under the input-output constraints. By discretizing (20), the following discrete state-space equation is derived as…”

Section: Nonlinear Disturbance Observer-based Adaptive Feedback Linea...mentioning

confidence: 99%

Nonlinear disturbance observer‐based adaptive feedback linearized model predictive controller design for a class of nonlinear systems

Dutta

Das

2022

Asian Journal of Control

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Adaptive control with less prior information of a nonlinear system subjected to external disturbance and parametric uncertainties is a longstanding problem in the control community. In this paper, a nonlinear disturbance observer‐based adaptive feedback linearized model predictive control (NDO‐AFLMPC) technique is proposed for a class of nonlinear MIMO systems. In this approach, a nonlinear disturbance observer (NDO) is used to observe the external disturbance of the system. A suitable constraint mapping algorithm is developed to map the input–output constraints within the proposed control scheme. Here, a multiple estimation model and the concept of second‐level adaptation technique is used to handle the parametric uncertainty of the real‐time plant. The boundedness of the estimated parameter is solved by developing the projection‐based parameter adaptation laws. Using an aerodynamic laboratory set‐up, known as the twin‐rotor MIMO system (TRMS), the effectiveness of the proposed control algorithm is verified. The complete state information of the real‐time system is provided to the proposed adaptive controller from an extended Kalman filter (EKF)‐based state observer. The performance of the proposed control algorithm is compared with other existing nonlinear control algorithms in the presence of unknown external disturbance and parametric uncertainties.

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Section: Nonlinear Disturbance Observer-based Adaptive Feedback Linea...mentioning

confidence: 99%

Nonlinear disturbance observer‐based adaptive feedback linearized model predictive controller design for a class of nonlinear systems

Dutta

Das

2022

Asian Journal of Control

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“…Moreover, another technique was developed in Reference 37 to enhance the transient response performance of a system by developing its multiple identification models. In continuation to research on multiple models, Reference 24 introduced a new method termed as multiple models with second‐level adaptation (MMSLA) for a nonlinear MIMO system. The MMSLA is most favorable when a standard degree of transient performance and relatively fast convergence are prime requirements 24 .…”

Section: Proposed Control Schemementioning

confidence: 99%

“…In continuation to research on multiple models, Reference 24 introduced a new method termed as multiple models with second‐level adaptation (MMSLA) for a nonlinear MIMO system. The MMSLA is most favorable when a standard degree of transient performance and relatively fast convergence are prime requirements 24 . Motivated by this, in this work, the multiple identification models and the concept of second‐level adaptation technique 22,23 is used to estimate the unknown system parameters online.…”

Section: Proposed Control Schemementioning

confidence: 99%

“…In this section, the multiple estimation models and the concept of the second‐level adaptation technique introduced by References 22‐24 for a nonlinear MIMO feedback linearizable system is discussed. In multiple models, the same estimation structure as Equation () is used for all N estimation models.…”

Section: Proposed Control Schemementioning

confidence: 99%

“…This approach estimates system parameters on‐line and produces the control input satisfying input–output constraints for desired trajectory tracking. Here, an adaptive parameter estimation technique, that is, multiple model two‐level adaptation 22‐24 is used to estimate the system parameters online. One of the differences between the present research and Reference 25 is related to parameter estimation as well as constraints handling technique.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An adaptive feedback linearized model predictive controller design for a nonlinear multi‐input multi‐output system

DuttaLakshmi¹,

DasDushmanta²

2021

Adaptive Control & Signal

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SummaryIn this work, an adaptive feedback linearized model predictive control (AFLMPC) scheme is proposed to compensate system uncertainty for a class of nonlinear multi‐input multi‐output system. Initially, a feedback linearization technique is used to transform the nonlinear dynamics into an exact linear model, thereafter, a model predictive control scheme is designed to obtain the desired tracking performance. A suitable constraint mapping algorithm has been developed to map input constraints to the new virtual input of the proposed control scheme. The proposed control scheme utilizes multiple estimation model and the concept of second‐level adaptation technique Pandey et al. (2014) to handle the parametric uncertainty in real‐time. Hence, the adaptive term in the control scheme is used to counteract the effect of model uncertainties and parameter adaptation errors. The effectiveness of the proposed AFLMPC control algorithm has been verified successfully in simulation as well as the experimental setup of the TRMS model. The unavailable states of the nonlinear system have been estimated using an extended Kalman filter based state observer. The performance of the proposed control algorithm has been compared with other existing nonlinear control techniques in simulation and experimental validation.

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Adaptive model predictive control design using multiple model second level adaptation for parameter estimation of two‐degree freedom of helicopter model

Dutta

Das

2021

Intl J Robust & Nonlinear

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This paper proposed an adaptive nonlinear model predictive control approach for a 2‐DoF helicopter model with both parametric uncertainties and input–output constraints. In the proposed control technique, the nonlinear helicopter model is linearized along the prediction horizon using a state and parameter‐dependent state‐space model. Furthermore, a linear quadratic objective function with constraints is carried out using the developed linearized model. Here, the multiple estimation model and the concept of second‐level adaptation are used to handle the parametric uncertainty of the nonlinear system. To ensure the boundedness of the estimated parameter within a predefined compact region, a projection based adaptive law is used. The adaptive tuning laws for the unknown parameters are derived by Lyapunov stability analysis. An ensemble Kalman filter has been used to observe the unavailable states of the 2‐DoF helicopter model. The effectiveness of the proposed control algorithm has been verified successfully in simulation as well as real‐time experimental setup of 2‐DoF of helicopter model and results are documented in tabular form to show superiority with an existing approach.

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Multiple models and second level adaptation for a class of nonlinear systems with nonlinear parameterization

Cited by 8 publications

References 17 publications

Nonlinear disturbance observer‐based adaptive feedback linearized model predictive controller design for a class of nonlinear systems

Nonlinear disturbance observer‐based adaptive feedback linearized model predictive controller design for a class of nonlinear systems

An adaptive feedback linearized model predictive controller design for a nonlinear multi‐input multi‐output system

Adaptive model predictive control design using multiple model second level adaptation for parameter estimation of two‐degree freedom of helicopter model

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