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

Kayacan, Erkan

doi:10.1177/0142331218794266

Cited by 24 publications

(15 citation statements)

References 58 publications

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“…According to the SMC theory, selection of high controller gains will result in achieving robust performance against uncertainties. However, this high gain selection might cause undesirable effects, including the common chattering effect, on the system response [21]. The use of learning controllers such as learning-based nonlinear model predictive control, is shown to be yet another alternative approach to deal with uncertainties in the system.…”

Section: A Related Workmentioning

confidence: 99%

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: 99%

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

Mehndiratta¹,

Kayacan²,

Reyhanoglu³

et al. 2020

IEEE Access

Self Cite

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“…This approach has been widely used in practical control systems. The primary concept of the intelligent control method is to use the ability of learning from the input and output information integrated with the expert awareness in fuzzy logic to estimate effects of disturbances/uncertainties on the system [38,39]. The main drawback of this method is that the controllers require very complicated and intensive computations.…”

Section: Introductionmentioning

confidence: 99%

Perturbation Observer-Based Robust Control Using a Multiple Sliding Surfaces for Nonlinear Systems with Influences of Matched and Unmatched Uncertainties

Thanh¹,

Xuan-Mung²,

Nguyen³

et al. 2020

Mathematics

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This paper presents a lumped perturbation observer-based robust control method using an extended multiple sliding surface for a system with matched and unmatched uncertainties. The fundamental methodology is to apply the multiple surfaces to approximate the unknown lumped perturbations simultaneously influencing on a nonlinear single input–single output (SISO) system. Subsequently, a robust controller, based on the proposed multi-surface and the approximated values, is designed to highly improve the control performance of the system. A general stability of the lumped perturbation observer and closed-loop control system is obtained through the Lyapunov theory. Results of a numerical simulation of an illustrative example demonstrate the soundness of the proposed algorithm.

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“…However, it is not easy to obtain a partial derivative and prove the overall stability of a control system. Hence, this issue was solved by an adaptive SMC for fuzzy system in [34], [35], and by the integration of SMC and NN-fuzzy structure in [36].…”

Section: Introductionmentioning

confidence: 99%

“…andξ n are estimates of ξ n andξ n respectively; p n1 , p n2 are auxiliary states; l n1 , l n2 , δ n1 , δ n2 are positive constants;ξ n andξ n are the errors in the estimations of ξ n andξ n , and theirs values are computed from Eq (36). and Eq.…”

mentioning

confidence: 99%

An Extended Multi-Surface Sliding Control for Matched/Mismatched Uncertain Nonlinear Systems Through a Lumped Disturbance Estimator

Thanh

Hong

2020

IEEE Access

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In this paper, we introduce a novel extended sliding mode control algorithm for a class of uncertain nonlinear system through a lumped disturbance estimator. The fundamental concept of this approach is to use multiple proportional-integral sliding surfaces to estimate the lumped disturbances generated by arbitrary matched/mismatched uncertainties. Following, an effectively extended sliding mode controller integrated with the estimated values is designed through the proposed series of sliding-surfaces to deal with the control problem of an uncertain nonlinear engineering model. The stability of both closed-loop control system and lumped disturbance estimator is achieved by Lyapunov theorem. The effectiveness of the proposed approach is demonstrated through simulation results of an illustrative example.

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

Cited by 24 publications

References 58 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

Perturbation Observer-Based Robust Control Using a Multiple Sliding Surfaces for Nonlinear Systems with Influences of Matched and Unmatched Uncertainties

An Extended Multi-Surface Sliding Control for Matched/Mismatched Uncertain Nonlinear Systems Through a Lumped Disturbance Estimator

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