Reduced‐order Observer for State‐dependent Coefficient Factorized Nonlinear Systems

Ornelas‐Tellez, Fernando; Alanis, Alma Y.; Rios, Jorge D.; Graff, Mario

doi:10.1002/asjc.1808

Cited by 7 publications

(8 citation statements)

References 39 publications

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“…To compare the proposed controller against existing ones, Table 3, displays the mean value and standard deviation for speed tracking error, to a comparison between an optimal tracking control (OTC) [29], a conventional discrete-time sliding mode control (SMC) scheme [30], a NIOC with a neural observer (NIOCNO) [22] with respect to the proposed Quantized NIOC. It is shown that the proposed Quantized NIOC presents the best performance.…”

Section: Comparative Analysismentioning

confidence: 99%

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Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor

et al. 2020

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This work introduces a neural-feedback control scheme for discrete-time quantized nonlinear systems with time delay. Traditionally, a feedback controller is designed under ideal assumptions that are unrealistic for real-work problems. Among these assumptions, they consider a perfect communication channel for controller inputs and outputs; such a perfect channel does not consider delays, or noise introduced by the sensors and actuators even if such undesired phenomena are well-known sources of bad performance in the systems. Moreover, traditional controllers are also designed based on an ideal plant model without considering uncertainties, disturbances, sensors, actuators, and other unmodeled dynamics, which for real-life applications are effects that are constantly present and should be considered. Furthermore, control system design implemented with digital processors implies sampling and holding processes that can affect the performance; considering and compensating quantization effects of measured signals is a problem that has attracted the attention of control system researchers. In this paper, a neural controller is proposed to overcome the problems mentioned above. This controller is designed based on a neural model using an inverse optimal approach. The neural model is obtained from available measurements of the state variables and system outputs; therefore, uncertainties, disturbances, and unmodeled dynamics can be implicitly considered from the available measurements. This paper shows the performance and effectiveness of the proposed controller presenting real-time results obtained on a linear induction motor prototype. Also, this work includes stability proof for the whole scheme using the Lyapunov approach.

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Section: Comparative Analysismentioning

confidence: 99%

“…OTC [29] 0.027393 0.04821 SMC [30] 0.008358 0.09212 NIOCNO [22] 0.002468 0.04703 NIOC 0.001023 0.00763…”

Section: Controller Mean Value (M/s) Standard Deviation (M/s)mentioning

confidence: 99%

Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor

et al. 2020

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“…Theorem 3.1. Consider the conformable fractional-order system (2) in its healthy operating case (f = 0), under assumption 1 and the observer (5). If there exist matrices X and P = P T > 0 and positive scalars β and η such that the condition (7) is feasible, then the error origin e = 0 is globally fractional exponential stable.…”

Section: The Healthy Operating Casementioning

confidence: 99%

“…To summarize the previous discussion in this section, conditions that allow the system states in the healthy operating case, as well as in the faulty operating cases, to be accurately reconstructed have been validated for this numerical example. Now, one can exploit the observers (5) and (10) and present the simulation results. The initial conditions and the fractional derivative order are chosen as:…”

Section: Simulation Studymentioning

confidence: 99%

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State estimation for nonlinear conformable fractional‐order systems: A healthy operating case and a faulty operating case

Jmal¹,

Elloumi

Naifar³

et al. 2019

Asian Journal of Control

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The issue of estimating states for classical integer‐order nonlinear systems has been widely addressed in the literature. Yet, generalization of existing results to the fractional‐order framework represents a fertile area of research. Note that, recently, a new and advantageous type of fractional derivative, the conformable derivative, was defined. So far, the general query of designing observers for conformable fractional‐order systems has not been investigated. In addition, it has been proved in the literature that some important tools for stability analysis of fractional‐order systems are valid using the conformable derivative concept, but invalid using other fractional derivative concepts. Motivated by the cited facts, this paper presents a first‐state estimation scheme for fractional‐order systems under the conformable derivative concept. A healthy operating case and a faulty operating case are treated. In this paper, a version of Barbalat's lemma, which is invalid using the well‐known Caputo derivative, is exploited to prove the convergence of the estimation errors. In order to validate the theoretical results, a numerical example is studied in the simulation section.

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Tensor product transformation‐based modeling of an induction machine

Nemeth

Kuczmann

2020

Asian Journal of Control

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The paper demonstrates a tensor product (TP) model transformation‐based framework for an induction machine (IM). The state space model of an IM is highly nonlinear, thus the Takagi–Sugeno (TS) fuzzy model‐based quasi‐linear parameter‐varying (qLPV) representation can be a good alternative approach of machines modeling. The paper presents the basics of IM state space modeling, how the TP transformation can be applied in details. The control of IM is always a pivotal point; hence, options of feedback control are discussed. The main goal of this paper is to present the whole process of IM TP transformation‐based modeling including a control system.

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Reduced‐order Observer for State‐dependent Coefficient Factorized Nonlinear Systems

Cited by 7 publications

References 39 publications

Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor

Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor

State estimation for nonlinear conformable fractional‐order systems: A healthy operating case and a faulty operating case

Tensor product transformation‐based modeling of an induction machine

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