Non-Fragile Fault-Tolerant Control Design for Fractional-Order Nonlinear Systems With Distributed Delays and Fractional Parametric Uncertainties

Sweetha, S.; Sakthivel, R.; Almakhles, Dhafer; Priyanka, S.

doi:10.1109/access.2022.3150477

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…What we are studying is how to make the optimal combination of these optimal solutions (i.e., maximize their own interests) most effectively distributed to various enterprises to achieve the goal of profit maximization. The first aspect is to generate the lowest or most favorable resource allocation for other participants through analyzing the interaction between various elements within the system, so as to make the whole supply chain obtain the best efficiency and the highest profit [11][12]. Figrue 2.…”

Section: Game Modelmentioning

confidence: 99%

Hybrid Task Scheduling Algorithm Considering Game Model in Distributed System

2022

DPS

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The distributed system has the characteristics of small scale, strong dynamics, and has an impact on both supply and demand. It plays a very important role in solving complex problems. In this paper, a mathematical model of hybrid task scheduling algorithm is established based on the consideration of profit maximization and uncertainty constraints in cooperative game among enterprises. Firstly, this paper studies the hybrid scheduling problem, then introduces the game model, and then studies the application of hybrid task scheduling algorithm. On this basis, the structure of distributed system is designed and the model is tested. Finally, the test results are shown. Under the optimization of hybrid task scheduling algorithm, the distributed system has improved the worst result and stability to a certain extent, and the improvement degree is great. The improvement effect of load balance degree is obvious and the stability is higher.

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Section: Game Modelmentioning

confidence: 99%

Hybrid Task Scheduling Algorithm Considering Game Model in Distributed System

2022

DPS

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“…An adaptive FTC has been established for fractional order fuzzy financial time-delay system in Sundarrajan and Palanisami (2021). The issue of non-fragile FTC for nonlinear fractional order systems with distributed delays has been studied in Sweetha et al (2022).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, time-delay takes place in the majority of practical systems and influences the performance of systems and may bring about instability (Mahmoudabadi and Tavakoli-Kakhki, 2022a; Phat et al, 2020). The problem of FTC for nonlinear fractional order systems in the presence of time-delay has been noticed in several research studies (Sundarrajan and Palanisami, 2021; Sweetha et al, 2022). An adaptive FTC has been established for fractional order fuzzy financial time-delay system in Sundarrajan and Palanisami (2021).…”

Section: Introductionmentioning

confidence: 99%

Stabilization of nonlinear fractional order faulty systems with time-varying delay

Mahmoudabadi

Tavakoli-Kakhki

2023

Journal of Vibration and Control

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This paper focuses on the issue of stabilization of nonlinear fractional order time-delay systems in the presence of fault. A general class of nonlinear fractional order systems is considered in which fault causes variations in the system dynamic and actuators. Besides, time-varying delay is noticed in the system equations that is of high importance in control of real-life systems. In order to facilitate the problem of controller design for such systems, a precise method for modeling complex nonlinear systems, namely, Takagi–Sugeno fuzzy model, is adopted. Regarding a Caputo derivative–based Lyapunov–Krasovskii functional, new delay-dependent stabilization conditions are established in the form of linear matrix inequalities. Eventually, two examples including a truck–trailer and Lorenz system are simulated in order to evaluate the results of this research work.

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“… Zhang et al (2022) addressed nonlinear systems with mismatched uncertainties under input/output quantization proposing adaptive output feedback control. Fractional parametric uncertainties and distributed delays in nonlinear systems together with time delay, parametric uncertainties and actuator faults were just addressed by Sweetha et al using a non-fragile fault-tolerant controller, which makes the system asymptotically stable with the specified mixed H∞ and passive performance index ( Sweetha et al, 2022 ). Wei et al (2022) sought to control uncertain nonlinear processes using neural networks incorporating into the control loop an adaptive neural network embedded contraction-based controller (to ensure convergence to time-varying references) and an online parameter identification module coupled with reference generation (to ensure modeled parameters converge those of the physical system).…”

Section: Introductionmentioning

confidence: 99%

Treatise on Analytic Nonlinear Optimal Guidance and Control Amplification of Strictly Analytic (Non-Numerical) Methods

Sands

2022

Front. Robot. AI

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Optimal control is seen by researchers from a different perspective than that from which the industry practitioners see it. Either type of user can easily become confounded when deciding which manner of optimal control should be used for guidance and control of mechanics. Such optimization methods are useful for autonomous navigation, guidance, and control, but their performance is hampered by noisy multi-sensor technologies and poorly modeled system equations, and real-time on-board utilization is generally computationally burdensome. Some methods proposed here use noisy sensor data to learn the optimal guidance and control solutions in real-time (online), where non-iterative instantiations are preferred to reduce computational burdens. This study aimed to highlight the efficacy and limitations of several common methods for optimizing guidance and control while proposing a few more, where all methods are applied to the full, nonlinear, coupled equations of motion including cross-products of motion from the transport theorem. While the reviewed literature introduces quantitative studies that include parametric uncertainty in nonlinear terms, this article proposes accommodating such uncertainty with time-varying solutions to Hamiltonian systems of equations solved in real-time. Five disparate types of optimum guidance and control algorithms are presented and compared to a classical benchmark. Comparative analysis is based on tracking errors (both states and rates), fuel usage, and computational burden. Real-time optimization with singular switching plus nonlinear transport theorem decoupling is newly introduced and proves superior by matching open-loop solutions to the constrained optimization problem (in terms of state and rate errors and fuel usage), while robustness is validated in the utilization of mixed, noisy state and rate sensors and uniformly varying mass and mass moments of inertia. Compared to benchmark, state-of-the-art methods state tracking errors are reduced one-hundred ten percent. Rate tracking errors are reduced one-hundred thirteen percent. Control utilization (fuel) is reduced eighty-four percent, while computational burden is reduced ten percent, simultaneously, where the proposed methods have no control gains and no linearization.

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Non-Fragile Fault-Tolerant Control Design for Fractional-Order Nonlinear Systems With Distributed Delays and Fractional Parametric Uncertainties

Cited by 12 publications

References 30 publications

Hybrid Task Scheduling Algorithm Considering Game Model in Distributed System

Hybrid Task Scheduling Algorithm Considering Game Model in Distributed System

Stabilization of nonlinear fractional order faulty systems with time-varying delay

Treatise on Analytic Nonlinear Optimal Guidance and Control Amplification of Strictly Analytic (Non-Numerical) Methods

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