Delayed Output Feedback Control for Nonlinear Systems With Two-Layer Interval Fuzzy Observers

Yu, Wen-Shyong; Karkoub, Mansour; Wu, Tzu-Sung; Her, Ming-Guo

doi:10.1109/tfuzz.2013.2269693

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…SMC is a robust control technique capable of handling systems with multiple inputs and outputs (MIMO). However, there is always a need for more consistency between the actual model and the mathematical model of the plant when designing the controller [ 25 ]. Matched and unmatched uncertainties, external disturbances, and parametric uncertainties are the main inconsistencies between the actual and mathematical models of the plant.…”

Section: Control Techniquesmentioning

confidence: 99%

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Irfan,

Zhao,

Ullah

et al. 2024

PLoS ONE

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An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.

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Section: Control Techniquesmentioning

confidence: 99%

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Irfan,

Zhao,

Ullah

et al. 2024

PLoS ONE

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show abstract

“…Although time-delay systems are widely studied in the previous research papers, the control problem of systems in the presence of output delays is still open. 47–49 Moreover, the decentralized control problem of nonlinear LS systems with interconnection or input delays is mostly investigated 36–46 and only one research paper considers output delays in nonlinear interconnection systems. 49 A main difference of the structure involved in this article from other similar investigation about the control problem of interconnected systems subject to output delays 49 is that only the delayed output of each subsystem in equation (1) is available for measurement.…”

Section: Problem Statementmentioning

confidence: 99%

“…To the best of our knowledge, there exist a few studies which have been done to deal with the control problem of unknown nonlinear LS systems with output delays [47][48][49] and the decentralized tracking control of unknown nonlinear interconnected systems subject to output delays has not been studied in existing literature. By employing variable structure control strategies, the tracking control problems of nonlinear single-input single-output (SISO) systems in the presence of output delays are investigated.…”

Section: Introductionmentioning

confidence: 99%

“…By employing variable structure control strategies, the tracking control problems of nonlinear single-input single-output (SISO) systems in the presence of output delays are investigated. 47,48 Recently, Zhai et al 49 have proposed an adaptive fuzzy dynamic surface control technique for the decentralized regulation control problem of unknown nonlinear interconnected systems with sensor delays and packet dropouts. However, the tracking control problem which is more demanding and practical in comparison to the regulation control problem has been not yet studied for the unknown nonlinear interconnected systems subject to sensor delays.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Control of interconnected systems with sensor delay based on decentralized adaptive neural dynamic surface method

Beigzadehnoe

Rafiee

Khosravi

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, an adaptive neural network is proposed for the tracking control problem of unknown nonlinear interconnected systems with inaccessible states and sensor delays based on dynamic surface strategy. The system has unknown nonlinearities and immeasurable states. Thus, a neural network state observer based on delayed outputs of subsystems is applied. The main difficulty in obtaining local observers’ gains is that undelayed outputs are not available. As a result, by utilizing proper Lyapunov–Krasovskii functionals in dynamic surface design procedures, the gains of local observers are given in terms of linear matrix inequalities. Then, appropriate changes in coordinates are defined using delayed outputs, observed states, and filtered virtual controls for the purpose of designing dynamic surface controllers. Subsequently, proper Lyapunov–Krasovskii functionals are introduced to deal with sensor delays and obtain control laws and stability criteria. Furthermore, the proposed decentralized control scheme can suitably conquer the decentralized tracking problem of unknown large-scale systems with sensor delays and guarantee that all the signals in the closed-loop interconnected systems be uniformly ultimately bounded. Finally, to show the effectiveness and efficiency of the proposed approach, the theoretic achievements are employed to design a controller for a double-inverted pendulum system and a cascade chemical reactor system.

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Introduction

Maiti

Sharma

Sarkar

2022

Studies in Systems, Decision and Control

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Delayed Output Feedback Control for Nonlinear Systems With Two-Layer Interval Fuzzy Observers

Cited by 11 publications

References 46 publications

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Control of interconnected systems with sensor delay based on decentralized adaptive neural dynamic surface method

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