The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform

Anthony Tzes - One of the best experts on this subject based on the ideXlab platform.

  • H∞ Closed-Loop Control for Unstable Uncertain Discrete Input-Shaped Systems
    2009 American Control Conference, 2009
    Co-Authors: John Stergiopoulos, Anthony Tzes
    Abstract:

    This article focuses on the design of a discrete robust Hinfin controller for an input-shaped, underdamped, (stable or unstable) system. The system is noiseless, uncertain, time-invariant and its characteristic polynomial coefficients are allowed to vary within Predefined Interval-sets. A discrete input shaper is used for the nominal system generating a precompensated discrete finite impulse response shaped system. The shaped system's description is reformulated in order to be amenable to a robust controller synthesis. The designed Hinfin controller compensates for the uncertainties of the system's parameters while providing the control command for stabilizing the system.

  • ACC - H ∞ Closed-Loop Control for Unstable Uncertain Discrete Input-Shaped Systems
    2009 American Control Conference, 2009
    Co-Authors: John Stergiopoulos, Anthony Tzes
    Abstract:

    This article focuses on the design of a discrete robust H ∞ controller for an input-shaped, underdamped, (stable or unstable) system. The system is noiseless, uncertain, time-invariant and its characteristic polynomial coefficients are allowed to vary within Predefined Interval-sets. A discrete input shaper is used for the nominal system generating a precompensated discrete Finite Impulse Response shaped system. The shaped system's description is reformulated in order to be amenable to a robust controller synthesis. The designed H ∞ controller compensates for the uncertainties of the system's parameters while providing the control command for stabilizing the system.

Bernd Tibken - One of the best experts on this subject based on the ideXlab platform.

  • CDC - Designing polynomial state feedback controllers to enlarge the domain of attraction in non-polynomial systems using a multidimensional gridding approach
    2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
    Co-Authors: Ahmed Saleme, Bernd Tibken
    Abstract:

    This paper presents a technique of enlarging the domain of attraction (DOA) for non-polynomial systems using state feedback controllers. In order to deal with such a problem, we intend to extend our technique for the estimation of the DOA for non-polynomial systems presented in [1] to controller design, which maximizes the guaranteed DOA induced by quadratic Lyapunov functions (QLF). The state feedback controller design is formulated as a minimum-maximum optimization problem. A lower and upper bound of the approximation error of the non-polynomial terms on a Predefined Interval is determined. These bounds are used to adapt the theorem of Ehlich and Zeller [2] to non-polynomial systems. The aim of this contribution is to show that lower and upper bounds of the maximized DOA with a corresponding controller can be obtained. Moreover, two conditions for the tightness of the lower and upper bounds are established. The applicability of this method is demonstrated by an example with two different controllers.

  • Designing polynomial state feedback controllers to enlarge the domain of attraction in non-polynomial systems using a multidimensional gridding approach
    2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
    Co-Authors: Ahmed Saleme, Bernd Tibken
    Abstract:

    This paper presents a technique of enlarging the domain of attraction (DOA) for non-polynomial systems using state feedback controllers. In order to deal with such a problem, we intend to extend our technique for the estimation of the DOA for non-polynomial systems presented in [1] to controller design, which maximizes the guaranteed DOA induced by quadratic Lyapunov functions (QLF). The state feedback controller design is formulated as a minimum-maximum optimization problem. A lower and upper bound of the approximation error of the non-polynomial terms on a Predefined Interval is determined. These bounds are used to adapt the theorem of Ehlich and Zeller [2] to non-polynomial systems. The aim of this contribution is to show that lower and upper bounds of the maximized DOA with a corresponding controller can be obtained. Moreover, two conditions for the tightness of the lower and upper bounds are established. The applicability of this method is demonstrated by an example with two different controllers.

John Stergiopoulos - One of the best experts on this subject based on the ideXlab platform.

  • H∞ Closed-Loop Control for Unstable Uncertain Discrete Input-Shaped Systems
    2009 American Control Conference, 2009
    Co-Authors: John Stergiopoulos, Anthony Tzes
    Abstract:

    This article focuses on the design of a discrete robust Hinfin controller for an input-shaped, underdamped, (stable or unstable) system. The system is noiseless, uncertain, time-invariant and its characteristic polynomial coefficients are allowed to vary within Predefined Interval-sets. A discrete input shaper is used for the nominal system generating a precompensated discrete finite impulse response shaped system. The shaped system's description is reformulated in order to be amenable to a robust controller synthesis. The designed Hinfin controller compensates for the uncertainties of the system's parameters while providing the control command for stabilizing the system.

  • ACC - H ∞ Closed-Loop Control for Unstable Uncertain Discrete Input-Shaped Systems
    2009 American Control Conference, 2009
    Co-Authors: John Stergiopoulos, Anthony Tzes
    Abstract:

    This article focuses on the design of a discrete robust H ∞ controller for an input-shaped, underdamped, (stable or unstable) system. The system is noiseless, uncertain, time-invariant and its characteristic polynomial coefficients are allowed to vary within Predefined Interval-sets. A discrete input shaper is used for the nominal system generating a precompensated discrete Finite Impulse Response shaped system. The shaped system's description is reformulated in order to be amenable to a robust controller synthesis. The designed H ∞ controller compensates for the uncertainties of the system's parameters while providing the control command for stabilizing the system.

Hussein T Mouftah - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive systems for smart buildings utilizing wireless sensor networks and artificial intelligence
    2020
    Co-Authors: Hussein T Mouftah, Blerim Qela
    Abstract:

    In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – "transforming" them into smart adaptable devices, i.e., "Smart Thermostats". An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant's pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a Predefined Interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the "RL and Agent-based Search", versus the "RL only" technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a "House Simulator" is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the "House Simulator", the "Smart Thermostat", and a placeholder for the "Adaptive Learning Models". As a result, a novel adaptive learning algorithm, "Observe, Learn and Adapt" (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the "House Simulator". OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

  • synergy of the reinforcement learning and agent based technique for finding optimal solution in a Predefined Interval
    Summer Computer Simulation Conference, 2010
    Co-Authors: Blerim Qela, Hussein T Mouftah
    Abstract:

    In this paper, a new algorithm for finding the optimal solution, in particular, finding maximum of a function in a Predefined Interval efficiently, by integrating reinforcement and agent based technique is presented. "Reinforcement Learning and Agent-based Search" application was implemented in C# to observe the algorithm at work and demonstrate its main features. The simulation results for several different functions are presented in order to demonstrate the result of synergy among 'reinforcement learning' and 'agent based' technique. In addition, its usefulness, in embedded systems with limited memory and/or processing power, such as the wireless sensor and/or actuator nodes are discussed.

  • SummerSim - Synergy of the reinforcement learning and agent-based technique for finding optimal solution in a Predefined Interval
    2010
    Co-Authors: Blerim Qela, Hussein T Mouftah
    Abstract:

    In this paper, a new algorithm for finding the optimal solution, in particular, finding maximum of a function in a Predefined Interval efficiently, by integrating reinforcement and agent based technique is presented. "Reinforcement Learning and Agent-based Search" application was implemented in C# to observe the algorithm at work and demonstrate its main features. The simulation results for several different functions are presented in order to demonstrate the result of synergy among 'reinforcement learning' and 'agent based' technique. In addition, its usefulness, in embedded systems with limited memory and/or processing power, such as the wireless sensor and/or actuator nodes are discussed.

Ahmed Saleme - One of the best experts on this subject based on the ideXlab platform.

  • CDC - Designing polynomial state feedback controllers to enlarge the domain of attraction in non-polynomial systems using a multidimensional gridding approach
    2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
    Co-Authors: Ahmed Saleme, Bernd Tibken
    Abstract:

    This paper presents a technique of enlarging the domain of attraction (DOA) for non-polynomial systems using state feedback controllers. In order to deal with such a problem, we intend to extend our technique for the estimation of the DOA for non-polynomial systems presented in [1] to controller design, which maximizes the guaranteed DOA induced by quadratic Lyapunov functions (QLF). The state feedback controller design is formulated as a minimum-maximum optimization problem. A lower and upper bound of the approximation error of the non-polynomial terms on a Predefined Interval is determined. These bounds are used to adapt the theorem of Ehlich and Zeller [2] to non-polynomial systems. The aim of this contribution is to show that lower and upper bounds of the maximized DOA with a corresponding controller can be obtained. Moreover, two conditions for the tightness of the lower and upper bounds are established. The applicability of this method is demonstrated by an example with two different controllers.

  • Designing polynomial state feedback controllers to enlarge the domain of attraction in non-polynomial systems using a multidimensional gridding approach
    2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
    Co-Authors: Ahmed Saleme, Bernd Tibken
    Abstract:

    This paper presents a technique of enlarging the domain of attraction (DOA) for non-polynomial systems using state feedback controllers. In order to deal with such a problem, we intend to extend our technique for the estimation of the DOA for non-polynomial systems presented in [1] to controller design, which maximizes the guaranteed DOA induced by quadratic Lyapunov functions (QLF). The state feedback controller design is formulated as a minimum-maximum optimization problem. A lower and upper bound of the approximation error of the non-polynomial terms on a Predefined Interval is determined. These bounds are used to adapt the theorem of Ehlich and Zeller [2] to non-polynomial systems. The aim of this contribution is to show that lower and upper bounds of the maximized DOA with a corresponding controller can be obtained. Moreover, two conditions for the tightness of the lower and upper bounds are established. The applicability of this method is demonstrated by an example with two different controllers.