Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers

Kayacan, Erkan; Fossen, Thor I.

doi:10.1002/asjc.1802

Cited by 33 publications

(42 citation statements)

References 50 publications

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“…Additionally, FLC with an integral action (FLC-I) has been proposed in the literature to ensure robust control performance. While it can only handle time-invariant uncertainties, it deteriorates nominal control performance in their absence [29]. Unlike FLC and FLC-I methods, the proposed SL strategy does not deteriorate nominal control performance in the absence of disturbances and uncertainties.…”

Section: A Related Workmentioning

confidence: 98%

“…To address the stability proof issue, an artifice is to incorporate the feedback linearization technique that transforms the nonlinear system into its linear equivalent; wherein the advanced linear control approaches can be freely utilized. However, the performance of the traditional FLC method is sensitive to uncertainties and disturbances in the system dynamics such that the closed-loop error dynamics of the system cannot converge to zero [29], [30]. For instance, within the trajectory tracking application demonstrated in this study, the induced disturbances vary over time.…”

Section: A Related Workmentioning

confidence: 99%

“…Remark 3: Equation (9) shows that if the modeling uncertainties and external disturbances have a steady-state value, i.e, they are time-invariant, then the FLC-I method is robust against them and the steady-state error in the system is eliminated. However, it is to be noted that adding an integral action may result in a degraded performance in comparison to the nominal performance of the traditional FLC method in the absence of uncertainties [29].…”

Section: B Flc With Integral Actionmentioning

confidence: 99%

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Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization

Mehndiratta¹,

Kayacan²,

Reyhanoglu³

et al. 2020

IEEE Access

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To facilitate accurate tracking in unknown/uncertain environments, this paper proposes a simple learning (SL) strategy for feedback linearization control (FLC) of aerial robots subject to uncertainties. The SL strategy minimizes a cost function defined based on the closed-loop error dynamics of the nominal system via the gradient descent technique to find the adaptation rules for feedback controller gains and disturbance estimate in the feedback control law. In addition to the derivation of the SL adaptation rules, the closedloop stability for a second-order uncertain nonlinear system is proven in this paper. Moreover, it is shown that the SL strategy can find the global optimum point, while the controller gains and disturbance estimate converge to a finite value which implies a bounded control action in the steady-state. Furthermore, utilizing a simulation study, it is shown that the simple learning-based FLC (SL-FLC) framework can ensure desired closed-loop error dynamics in the presence of disturbances and modeling uncertainties. Finally, to validate the SL-FLC framework in real-time, the trajectory tracking problem of a tilt-rotor tricopter unmanned aerial vehicle under uncertain conditions is studied via three case scenarios, wherein the disturbances in the form of mass variation, ground effect, and wind gust, are induced. The real-time results illustrate that the SL-FLC framework facilitates a better tracking performance than that of the traditional FLC method while maintaining the nominal control performance in the absence of modeling uncertainties and external disturbances, and exhibiting robust control performance in the presence of modeling uncertainties and external disturbances. INDEX TERMS Feedback linearization control, nonlinear system, uncertain systems, learning control, unmanned aerial vehicle.

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Section: A Related Workmentioning

confidence: 98%

Section: A Related Workmentioning

confidence: 99%

Section: B Flc With Integral Actionmentioning

confidence: 99%

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Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization

Mehndiratta¹,

Kayacan²,

Reyhanoglu³

et al. 2020

IEEE Access

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“…Lemma 4. (Chen 2003; Kayacan and Fossen 2019; Kayacan et al 2017): If lz is positive (i.e. lz > 0 ), the disturbance error dynamics in equation (20) approach to zero exponentially.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Lemma 5. (Kayacan and Fossen 2019; Kayacan et al 2017; Khalil and Grizzle 1996): If a nonlinear system x · = F ( x , u ) is input-state stable and lim t → ∞ u ( t ) = 0 , then the state lim t → ∞ x ( t ) = 0 .…”

Section: Problem Formulationmentioning

confidence: 99%

Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

Kayacan

2018

Transactions of the Institute of Measurement and Control

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This paper presents a novel sliding mode control (SMC) algorithm to handle mismatched uncertainties in systems via a novel self-learning disturbance observer (SLDO). A computationally efficient SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a neuro-fuzzy structure (NFS) work in parallel. In this framework, the NFS estimates the mismatched disturbances and becomes the leading disturbance estimator while the former feeds the learning error to the NFS to learn system behaviour. The simulation results demonstrate that the proposed SMC based on SLDO (SMC-SLDO) ensures robust control performance in the presence of mismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC) and SMC based on a basic nonlinear disturbance observer (SMC-BNDO), and also remains the nominal control performance in the absence of mismatched uncertainties. Additionally, the SMC-SLDO not only counteracts mismatched time-varying uncertainties, but also improve the transient response performance in the presence of mismatched time-invariant uncertainties. Moreover, the controller gain of the SMC-SLDO is required to be selected larger than the upper bound of the disturbance estimation error rather than the upper bound of the actual disturbance to guarantee system stability, which results in eliminating the chattering effects on the control signal.

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Guaranteeing performance for uncertain nonlinear systems with bounded state constraint and mismatching condition

Fang

Chen

et al. 2019

Asian Journal of Control

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The control design problem for the uncertain nonlinear system with bounded state constraint and mismatching condition is considered in this paper. The uncertainty in the system, which may be due to unknown system parameters and external disturbance, is nonlinear and time‐varying. The state of the system is constrained to be bounded. The system does not satisfy the (global) matching condition. A creative one‐to‐one state transformation is proposed by converting the bounded states into the unbounded ones. A step‐by‐step state transformation is proposed to convert the mismatched system into a matched system. The robust control is then proposed based on the transformed system. The control is demonstrated to be able to guarantee the uniform boundedness and uniform ultimate boundedness of the system in the presence of uncertainty, while the state constraint can be always guaranteed.

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Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers

Cited by 33 publications

References 50 publications

Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization

Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization

Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

Guaranteeing performance for uncertain nonlinear systems with bounded state constraint and mismatching condition

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