Non-linear threshold algorithm based solution for the redundancy allocation problem considering multiple redundancy strategies

Nahas, Nabil; Darghouth, Mohamed Noomane; Kara, Abdul Qadar; Nourelfath, Mustapha

doi:10.1108/jqme-05-2018-0041

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…The results outperformed other analytical and heuristic solutions in terms of the closed-loop time response characteristics. In addition, the NLTA algorithm is applied to solve redundancy allocation problems by optimizing the reliability of the underlying systems in [32]. In [33], the algorithm is employed to solve a non-convex economic dispatch problem using various non-convex objective functions.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Optimized Tracking Control Heuristic

et al. 2020

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Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

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Section: Introductionmentioning

confidence: 99%

Model-Free Optimized Tracking Control Heuristic

et al. 2020

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“…It tackled energy distribution optimization problems like power system dispatch with prohibition zones and multiple fuel options in [11]. NLTA is employed to find solutions for the redundancy allocation where it is applied to solve the redundancy allocation problems and enhance the associated reliability in [12]. Further, it is employed to regulate the load frequency and automatic voltage disturbances for a network of power generation units in [13].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study

Wang

Abouheaf

Gueaieb

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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A data-driven computational heuristic is proposed to control MIMO systems without prior knowledge of their dynamics. The heuristic is illustrated on a two-input two-output balance system. It integrates a self-adjusting nonlinear threshold accepting heuristic with a neural network to compromise between the desired transient and steady state characteristics of the system while optimizing a dynamic cost function. The heuristic decides on the control gains of multiple interacting PID control loops. The neural network is trained upon optimizing a weightedderivative like objective cost function. The performance of the developed mechanism is compared with another controller that employs a combined PID-Riccati approach. One of the salient features of the proposed control schemes is that they do not require prior knowledge of the system dynamics. However, they depend on a known region of stability for the control gains to be used as a search space by the optimization algorithm. The control mechanism is validated using different optimization criteria which address different design requirements.

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“…Fazlollahtabar et al (2018) estimated the required number of robots consisting of some non-repairable components, by using a renewal model, to assure the availability of standby autonomous robots for reducing and preventing downtimes of advanced production systems. Nahas et al (2019) determined the optimal selection of elements, redundancy levels and redundancy strategy, which maximizes the system reliability under various system-level.…”

Section: Introductionmentioning

confidence: 99%

Fault-tolerant redundant repairable system with different failures and delays

Shekhar

Kumar

Varshney

et al. 2019

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Purpose The internet of things and just-in-time are the embryonic model of innovation for the state-of-the-art design of the service system. This paper aims to develop a fault-tolerant machining system with active and standby redundancy. The availability of the fault-tolerant redundant repairable system is a key concern in the successful deployment of the service system. Design/methodology/approach In this paper, the authors cogitate a fault-tolerant redundant repairable system of finite working units along with warm standby unit provisioning. Working unit and standby unit are susceptible to random failures, which interrupt the quality-of-service. The system is also prone to common cause failure, which tends its catastrophe. The instantaneous repair of failed unit guarantees the increase in the availability of the unit/system. The time-to-repair by the single service facility for the failed unit follows the arbitrary distribution. For increasing the practicability of the studied model, the authors have also incorporated real-time machining practices such as imperfect coverage of the failure of units, switching failure of standby unit, common cause failure, reboot delay, switch over delay, etc. Findings For deriving the explicit expression for steady-state probabilities of the system, the authors use a supplementary variable technique for which the only required input is the Laplace–Stieltjes transform (LST) of the repair time distribution. Research limitations/implications For complex and multi-parameters distribution of repair time, derivation of performance measures is not possible. The authors prefer numerical simulation because of its importance in the application for real-time uses. Practical implications The stepwise recursive procedure, illustrative examples, and numerical results have been presented for the diverse category of repair time distribution: exponential (M), n-stage Erlang (Ern), deterministic (D), uniform (U(a,b)), n-stage generalized Erlang (GE[n]) and hyperexponential (HE[n]). Social implications Concluding remarks and future scopes have also been included. The studied fault-tolerant redundant repairable system is suitable for reliability analysis of a computer system, communication system, manufacturing system, software reliability, service system, etc. Originality/value As per the survey in literature, no previous published paper is presented with so wide range of repair time distribution in the machine repair problem. This paper is valuable for system design for reliability analysis of the fault-tolerant redundant repairable.

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Non-linear threshold algorithm based solution for the redundancy allocation problem considering multiple redundancy strategies

Cited by 8 publications

References 39 publications

Model-Free Optimized Tracking Control Heuristic

Model-Free Optimized Tracking Control Heuristic

Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study

Fault-tolerant redundant repairable system with different failures and delays

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