Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Habbi, Hacene

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    Experimental investigation of evolving cloud-based fuzzy control of a pilot thermal exchanger under a decentralized framework
    (Elsevier, 2023) Lamraoui, Oualid; Habbi, Hacene
    Relying on the Robust Evolving Cloud-based Control (RECCo) protocol, a decentralized evolving fuzzy control scheme is presented in this paper for a strongly interacting thermal exchanger process and for the first time experimentally investigated on the real pilot plant. Loop pairing information is first derived based on experimental analysis of the process dynamics. Accordingly, independent RECCo-controlled loops of system temperature are arranged in functional blocks without any prior decoupling or process modelling. The evolvable fuzzy controller structure is learned concurrently relying on data streams only. Simulation and experiments are carried out to support the design procedure and verify the control performance. The results show reasonable tracking performance under different scenarios.
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    Late-lumping fuzzy boundary geometric control of nonlinear partial differential systems
    (Wiley, 2020) Raab, Sadia; Habbi, Hacene; Maidi, Ahmed
    In this article, a fuzzy boundary geometric controller that stabilizes a class of nonlinear distributed parameter systems (DPSs) is proposed. The design procedure relies on the use of Takagi‐Sugeno (T‐S) type fuzzy partial differential equation (PDE) model, which approximates the dynamical behavior of the nonlinear DPS. The T‐S fuzzy PDE model is constructed through “fuzzy blending” of local linear PDE models of infinite characteristic indexes. This is a challenging task in the design procedure of fuzzy PDE model‐based boundary controller in the framework of the well‐established geometric control theory. To overcome this constraint, it is proposed in this article to resort to the concept of extended operator in order to transform the T‐S fuzzy PDE model with boundary control to an equivalent fuzzy PDE model with punctual control and finite characteristic index. Based on the developed fuzzy model, a fuzzy boundary geometric controller is derived and sufficient conditions of exponential stability of the resulting closed‐loop system are established by employing the Lyapunov direct method. The stabilizing performance of the proposed fuzzy PDE model‐based boundary geometric controller is evaluated on benchmark control problems and compared with other existing control methods via numerical simulations
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    Swarm bee colony optimization for heat exchanger distributed dynamics approximation with application to leak detection
    (IGI Global, 2018) Boudouaoui, Yassine; Habbi, Hacene; Harfouchi, Fatima
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    A cooperative learning strategy with multiple search mechanisms for improved artificial bee colony optimization
    (IEEE, 2015) Harfouchi, Fatima; Habbi, Hacene
    Artificial bee colony (ABC) optimization is a swarm based stochastic search strategy inspired by the foraging behavior of honeybees. Due to its simplicity and promising optimization capability, the ABC concept has devoted special interest with an increasing number of applications to scientific and engineering optimization problems. As an open research field, many researchers attempted to improve the performance of ABC algorithm through new algorithmic frameworks or by introducing modifications on the basic model. This paper presents an improved version of ABC algorithm based on a cooperative learning strategy with modified search mechanisms incorporated at both employed and onlooker levels. The proposed approach referred to as CLABC (Cooperative learning ABC) is tested on benchmark functions for numerical optimization. The results demonstrate the good performance and convergence of the proposed algorithm over other existing ABC variants
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    A cooperative learning artificial bee colony algorithm with multiple search mechanisms
    (IOS Press, 2016) Harfouchi, Fatima; Habbi, Hacene
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    Self-generated fuzzy systems design using artificial bee colony optimization
    (Elsevier, 2015) Habbi, Hacene; Boudouaoui, Yassine; Karaboga, Dervis; Ozturk, Celal
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    H-infinity fuzzy emulator design for multivariable control of drum boiler-turbine unit
    (Institute of Electrical and Electronics Engineers, 2015) Lamraoui, Oualid; Habbi, Hacene
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    Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger
    (Taylor & Francis, 2011) Zelmat, Mimoun; Kinnaert, Michel; Habbi, Hacene
    This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults
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    Data-driven fuzzy models for nonlinear identification of a complex heat exchanger
    (Elsevier, 2011) Habbi, Hacene; Kidouche, Madjid; Zelmat, Mimoun
    This paper presents and discusses experimental results on nonlinear model identification method applied to a real pilot thermal plant. The aim of this work is to develop a moderately complex model with interpretable structure for a complex parallel flow heat exchanger which is the main component of the thermal plant using a fuzzy clustering technique. The proposed Takagi–Sugeno-type (TS) fuzzy rule-based model is derived through an iterative fuzzy clustering algorithm using a set of input–output measurements. It is shown that the identified multivariable fuzzy rule-based model captures well the key dynamical properties of the physical plant over a wide operating range and under varying operating conditions. For validation, the model is run in parallel and series-parallel configurations to the real process. The experimental results show clearly the high performance of the proposed fuzzy model in achieving good prediction of the main process variables