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Tansel Yucelen - One of the best experts on this subject based on the ideXlab platform.

  • a set theoretic model reference adaptive Control Architecture with dead zone effect
    Control Engineering Practice, 2019
    Co-Authors: Ehsan Arabi, Tansel Yucelen
    Abstract:

    Abstract A new set-theoretic model reference adaptive Control Architecture with dead-zone effect is presented. The key feature of our approach utilizes a new generalized restricted potential function, where it not only provides a user-defined uncertain dynamical system performance but also has the capability to stop the adaptation when system errors are small (i.e., inside dead-zone) — a practice adopted in adaptive Control applications. The stability of the proposed technique is analyzed through showing the boundedness of an energy function in all possible variations and its experimental validation is also given through an aerospace testbed.

  • A Set-Theoretic Model Reference Adaptive Control Architecture with Dead-Zone Effect
    2019 American Control Conference (ACC), 2019
    Co-Authors: Ehsan Arabi, Tansel Yucelen
    Abstract:

    By introducing a system error dependent learning rate, the recently proposed set-theoretic model reference adaptive Control Architecture provides user-defined worst-case performance guarantees on the system error between an uncertain dynamical system of interest and a given reference model. In this Architecture, the adaptation process is always active. However, it is of practical interest to stop the adaptation process when it is not needed (i.e., in the presence of small system errors). Motivated from this standpoint, we present a new set-theoretic model reference adaptive Control Architecture with dead-zone effect. The key feature of our framework utilizes a modified and continuous generalized restricted potential function in the adaptation update law, where it not only stops the adaptation process inside the dead-zone (i.e., when the system error is small) but also allows the norm of the system error to be less than a-priori, user-defined worst-case performance bound. The efficacy of the proposed Architecture is also demonstrated through an illustrative numerical example.

  • An Adaptive Control Architecture for Mitigating Sensor and Actuator Attacks in Cyber-Physical Systems
    IEEE Transactions on Automatic Control, 2017
    Co-Authors: Xu Jin, Wassim M Haddad, Tansel Yucelen
    Abstract:

    Recent technological advances in communications and computation have spurred a broad interest in Control law Architectures involving the monitoring, coordination, integration, and operation of sensing, computing, and communication components that tightly interact with the physical processes that they Control. These systems are known as cyber-physical systems and due to their use of open computation and communication platform Architectures, Controlled cyber-physical systems are vulnerable to adversarial attacks. In this technical note, we propose a novel adaptive Control Architecture for addressing security and safety in cyber-physical systems. Specifically, we develop an adaptive Controller that guarantees uniform ultimate boundedness of the closed-loop dynamical system in the face of adversarial sensor and actuator attacks that are time-varying and partial asymptotic stability when the sensor and actuator attacks are time-invariant. Finally, we provide a numerical example to illustrate the efficacy of the proposed adaptive Control Architecture.

Wassim M Haddad - One of the best experts on this subject based on the ideXlab platform.

  • an adaptive Control Architecture for cyber physical system security in the face of sensor and actuator attacks and exogenous stochastic disturbances
    Cyber-Physical Systems, 2018
    Co-Authors: Xu Jin, Wassim M Haddad, Tomohisa Hayakawa
    Abstract:

    In this paper, we propose a novel adaptive Control Architecture for addressing security and safety in cyber-physical systems subject to exogenous disturbances. Specifically, we develop an a...

  • an adaptive Control Architecture for cyber physical system security in the face of sensor and actuator attacks and exogenous stochastic disturbances
    Conference on Decision and Control, 2017
    Co-Authors: Xu Jin, Wassim M Haddad, Tomohisa Hayakawa
    Abstract:

    In this paper, we propose a novel adaptive Control Architecture for addressing security and safety in cyber-physical systems subject to exogenous disturbances. Specifically, we develop an adaptive Controller for time-invariant, state-dependent adversarial sensor and actuator attacks in the face of stochastic exogenous disturbances. We show that the proposed Controller guarantees uniform ultimate boundedness of the closed-loop dynamical system in a mean-square sense. We further discuss the practicality of the proposed approach and provide a numerical example involving the lateral directional dynamics of an aircraft to illustrate the efficacy of the proposed adaptive Control Architecture.

  • An Adaptive Control Architecture for Mitigating Sensor and Actuator Attacks in Cyber-Physical Systems
    IEEE Transactions on Automatic Control, 2017
    Co-Authors: Xu Jin, Wassim M Haddad, Tansel Yucelen
    Abstract:

    Recent technological advances in communications and computation have spurred a broad interest in Control law Architectures involving the monitoring, coordination, integration, and operation of sensing, computing, and communication components that tightly interact with the physical processes that they Control. These systems are known as cyber-physical systems and due to their use of open computation and communication platform Architectures, Controlled cyber-physical systems are vulnerable to adversarial attacks. In this technical note, we propose a novel adaptive Control Architecture for addressing security and safety in cyber-physical systems. Specifically, we develop an adaptive Controller that guarantees uniform ultimate boundedness of the closed-loop dynamical system in the face of adversarial sensor and actuator attacks that are time-varying and partial asymptotic stability when the sensor and actuator attacks are time-invariant. Finally, we provide a numerical example to illustrate the efficacy of the proposed adaptive Control Architecture.

  • a new neuroadaptive Control Architecture for nonlinear uncertain dynamical systems beyond sigma and e modifications
    IEEE Transactions on Neural Networks, 2009
    Co-Authors: K Y Volyanskyy, Wassim M Haddad, Anthony J Calise
    Abstract:

    This paper develops a new neuroadaptive Control Architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel Controller Architecture involving additional terms in the update laws that are constructed using a moving time window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress and cancel system uncertainty without the need for persistency of excitation. A nonlinear parametrization of the system uncertainty is considered and state and output feedback neuroadaptive Controllers are developed. To illustrate the efficacy of the proposed approach we apply our results to a spacecraft model with unknown moment of inertia and compare our results with standard neuroadaptive Control methods.

Xu Jin - One of the best experts on this subject based on the ideXlab platform.

Anne Geraci - One of the best experts on this subject based on the ideXlab platform.

  • An innovative digital Control Architecture for low-voltage, high-current DC-DC converters with tight voltage regulation
    IEEE Transactions on Power Electronics, 2004
    Co-Authors: Stefano Saggini, Massimo Ghioni, Anne Geraci
    Abstract:

    This paper describes an innovative digital Control Architecture for low-voltage, high-current dc-dc converters, based on a combination of current-programmed Control and variable frequency operation. The key feature of the proposed Architecture is the low complexity: only two digital-to-analog converters (DACs) with low resolution (7-b) are used for Control. An original Control algorithm is used to reduce quantization effects to negligible levels, in spite of the low resolution of the DACs. Thanks to this algorithm, both static and dynamic output voltage regulation are improved with respect to traditional digital solutions. Adaptive voltage positioning and active current sharing are inherently provided by the new Architecture. A detailed description of the Control strategy is given with reference to a single-phase buck converter. Extension to multiphase converters is straightforward. The digital Control Architecture is experimentally verified on a FPGA-based four-phase prototype buck converter operating at 350 kHz/phase. Output voltage tolerance within ±0.5% is experimentally demonstrated, along with negligible quantization effects and fast transient response. The features and the performance of the proposed Architecture make it a valuable candidate for the Control of next generation voltage regulator modules.

Naira Hovakimyan - One of the best experts on this subject based on the ideXlab platform.

  • stability margins of cal l _ 1 adaptive Control Architecture
    IEEE Transactions on Automatic Control, 2010
    Co-Authors: Chengyu Cao, Naira Hovakimyan
    Abstract:

    This technical note presents the L 1 adaptive Control Architecture for systems in the presence of unknown high-frequency gain with known sign, time-varying unknown parameters and disturbances. The L 1 adaptive Controller leads to uniform performance bounds for the system's input and output signals, which can be systematically improved by increasing the adaptation rate. For constant unknown parameters, this result leads to analytically computable time-delay margin of a semiglobal nature.

  • design and analysis of a novel cal l _1 adaptive Control Architecture with guaranteed transient performance
    IEEE Transactions on Automatic Control, 2008
    Co-Authors: Chengyu Cao, Naira Hovakimyan
    Abstract:

    This paper presents a novel adaptive Control Architecture that adapts fast and ensures uniformly bounded transient response for system's both signals, input and output, simultaneously. This new Architecture has a low-pass filter in the feedback loop and relies on the small-gain theorem for the proof of asymptotic stability. The tools from this paper can be used to develop a theoretically justified verification and validation framework for adaptive systems. Simulations illustrate the theoretical findings.

  • novel cal l _ 1 neural network adaptive Control Architecture with guaranteed transient performance
    IEEE Transactions on Neural Networks, 2007
    Co-Authors: Chengyu Cao, Naira Hovakimyan
    Abstract:

    In this paper, we present a novel neural network (NN) adaptive Control Architecture with guaranteed transient performance. With this new Architecture, both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. This new Architecture uses a low-pass filter in the feedback loop, which consequently enables to enforce the desired transient performance by increasing the adaptation gain. For the guaranteed transient performance of both input and output signals of the uncertain nonlinear system, the L1 gain of a cascaded system, comprised of the low-pass filter and the closed-loop desired reference model, is required to be less than the inverse of the Lipschitz constant of the unknown nonlinearities in the system. The tools from this paper can be used to develop a theoretically justified verification and validation framework for NN adaptive Controllers. Simulation results illustrate the theoretical findings.