The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform

Mojtaba Alizadeh - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive PID controller design for wing rock suppression using self-recurrent wavelet neural Network Identifier
    Evolving Systems, 2016
    Co-Authors: Milad Malekzadeh, Jalil Sadati, Mojtaba Alizadeh
    Abstract:

    This paper presents a novel control scheme based on auto-tuning PID controller to suppress wing rock phenomena. Due to having a complex dynamic, wing rock motion identification is not a simple task, and this complexity can adversely affect the performance of PID controller. Employing a wavelet neural Network based Identifier, this paper develops an auto tuning adaptive PID controller to tackle the problem. Since having an acceptable control performance inevitably involves having a meticulously trained Identifier, the training performance is of utmost importance. Aiming at boosting the training efficacy, a two-phase algorithm encompassing Bees algorithm and Back-Propagation (BP) is proposed by this paper to train the proposed Identifier respectively in off-line and on-line modes. Due to its inherent capability in sifting the global minima, Bees algorithm is employed to find initial values of weights around which it is then possible to conduct a local search by means of BP based online training. Therefore, the Identifier can precisely furnish the proposed PID controller with the system sensitivity in on-line mode. The adaption of PID controller can thus be performed in each time step. The performance of this method has been presented in simulation results and the comparison section confirms the effectiveness of proposed scheme.

  • On‐line self‐learning PID based PSS using self‐recurrent wavelet neural Network Identifier and chaotic optimization
    COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 2012
    Co-Authors: Soheil Ganjefar, Mojtaba Alizadeh
    Abstract:

    Purpose – The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie lines. These oscillations may sustain and grow to cause system separation if no adequate damping is available. The present paper aims to propose an on‐line self‐learning PID (OLSL‐PID) controller in order to damp the low frequency power system oscillations in a single‐machine system.Design/methodology/approach – The proposed OLSL‐PID is used as a controller in order to damp the low frequency power system oscillations. It has a local nature because of its powerful adaption process based on back‐propagation (BP) algorithm that is implemented through an adaptive self‐recurrent wavelet neural Network Identifier (ASRWNNI). In fact PID controller parameters are updated in on‐line mode, using BP algorithm based on the information provided b...

Sanjeev Agarwal - One of the best experts on this subject based on the ideXlab platform.

  • active vibration control of smart composite plates using self adaptive neurocontroller
    Smart Structures and Materials 2000: Mathematics and Control in Smart Structures, 2000
    Co-Authors: Manish Thankappan Valoor, K Chandrashekhara, Sanjeev Agarwal
    Abstract:

    A neural Network-based control system is developed for self- adapting vibration control of laminated plates with piezoelectric sensors and actuators. The conventional vibration control approaches are limited by the requirement of an explicit and often accurate identification of the system dynamics and subsequent 'offline' design of an optimal controller. The present study utilizes the powerful learning capabilities of neural Networks to capture the structural dynamics and to evolve optimal control dynamics. A hybrid control system developed in this paper is comprised of a feed- forward neural Network Identifier and a dynamic diagonal recurrent neural Network (DRNN) controller. Sensing and actuation are achieved using piezoelectric sensors and actuators. The performance of hybrid control system is tested by numerical simulation of composite plate with embedded piezoelectric actuators and sensors. Finite element equations of motion are developed based on shear deformation theory and implemented for a plate element. The dynamic effects of the mass and stiffness of the piezoelectric patches are considered in the model. Numerical results are presented for a flat plate. A robustness study including the effects of structural parameter variation and partial loss of sensor and actuator is performed. The hybrid control system is shown to perform effectively in all these cases.

  • active vibration control of smart composite plates using self adaptive neuro controller
    Smart Materials and Structures, 2000
    Co-Authors: Manish Thankappan Valoor, K Chandrashekhara, Sanjeev Agarwal
    Abstract:

    A neural Network-based control system is developed for self-adapting vibration control of laminated plates with piezoelectric sensors and actuators. The conventional vibration control approaches are limited by the requirement of an explicit and often accurate identification of the system dynamics and subsequent `offline' design of an optimal controller. The present study utilizes the powerful learning capabilities of neural Networks to capture the structural dynamics and to evolve optimal control dynamics. A hybrid control system developed in this paper is comprised of a feed-forward neural Network Identifier and a dynamic diagonal recurrent neural Network controller. Sensing and actuation are achieved using piezoelectric sensors and actuators. The performance of the hybrid control system is tested by numerical simulation of a composite plate with embedded piezoelectric actuators and sensors. Finite-element equations of motion are developed based on shear deformation theory and implemented for the plate element. The dynamic effects of the mass and stiffness of the piezoelectric patches are considered in the model. Numerical results are presented for a flat plate. A robustness study including the effects of structural parameter variation and partial loss of the sensor and actuator is performed. The hybrid control system is shown to perform effectively in all of these cases.

Francoise Lamnabhilagarrigue - One of the best experts on this subject based on the ideXlab platform.

  • a novel online training neural Network based algorithm for wind speed estimation and adaptive control of pmsg wind turbine system for maximum power extraction
    Renewable Energy, 2016
    Co-Authors: Fernando Jaramillolopez, Godpromesse Kenne, Francoise Lamnabhilagarrigue
    Abstract:

    In this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural Network Identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme.

Françoise Lamnabhi-lagarrigue - One of the best experts on this subject based on the ideXlab platform.

  • A novel neural Network-based algorithm for wind speed estimation and block-backstepping control of PMSG wind turbine systems for maximum power extraction
    Renewable Energy, 2016
    Co-Authors: Fernando Jaramillo-lopez, Godpromesse Kenne, Françoise Lamnabhi-lagarrigue
    Abstract:

    In this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural Network Identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme.

Godpromesse Kenne - One of the best experts on this subject based on the ideXlab platform.

  • a novel online training neural Network based algorithm for wind speed estimation and adaptive control of pmsg wind turbine system for maximum power extraction
    Renewable Energy, 2016
    Co-Authors: Fernando Jaramillolopez, Godpromesse Kenne, Francoise Lamnabhilagarrigue
    Abstract:

    In this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural Network Identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme.

  • A novel neural Network-based algorithm for wind speed estimation and block-backstepping control of PMSG wind turbine systems for maximum power extraction
    Renewable Energy, 2016
    Co-Authors: Fernando Jaramillo-lopez, Godpromesse Kenne, Françoise Lamnabhi-lagarrigue
    Abstract:

    In this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural Network Identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme.