The Experts below are selected from a list of 541977 Experts worldwide ranked by ideXlab platform

Desong Bian - One of the best experts on this subject based on the ideXlab platform.

  • a distributed cooperative Control Framework for synchronized reconnection of a multi bus microgrid
    IEEE Transactions on Smart Grid, 2018
    Co-Authors: Di Shi, Xi Chen, Zhiwei Wang, Xiaohu Zhang, Xinan Wang, Desong Bian
    Abstract:

    One critical value microgrids brings to power systems is resilience, the capability of being able to island from the main grid under certain conditions and connect back when necessary. Once islanded, a microgrid must be synchronized to the main grid before reconnection to prevent severe consequences. In general, synchronization of a single machine with the grid can be easily achieved using a synchronizer. The problem becomes more challenging when it comes to a multi-bus microgrid with multiple distributed generators (DGs) and dispersed loads. All DGs need to be properly Controlled in a coordinated way to achieve synchronization. This paper presents a novel bi-level distributed cooperative Control Framework for a multi-bus microgrid. In this Framework, DGs work collaboratively in a distributed manner using minimum and sparse communication. The topology of the communication network can be flexible which supports the plug-and-play feature of microgrids. Fast and deterministic synchronization can be achieved with tolerance to communication latency. Experimental results obtained from hardware-in-the-loop simulation demonstrate the effectiveness of the proposed approach.

  • a distributed cooperative Control Framework for synchronized reconnection of a multi bus microgrid
    arXiv: Systems and Control, 2017
    Co-Authors: Di Shi, Xi Chen, Zhiwei Wang, Xiaohu Zhang, Xinan Wang, Desong Bian
    Abstract:

    One critical value microgrids bring to power systems is resilience, the capability of being able to island from the main grid under certain conditions and connect back when necessary. Once islanded, a microgrid must be synchronized to the main grid before reconnection to prevent severe consequences. In general, synchronization of a single machine with the grid can be easily achieved using a synchronizer. The problem becomes more challenging when it comes to a multi-bus microgrid with multiple distributed generators (DGs) and dispersed loads. All distributed generators need to be properly Controlled in a coordinated way to achieve synchronization. This paper presents a novel bi-level distributed cooperative Control Framework for a multi-bus microgrid. In this Framework, DGs work collaboratively in a distributed manner using the minimum and sparse communication. The topology of the communication network can be flexible which supports the plug-and-play feature of microgrids. Fast and deterministic synchronization can be achieved with tolerance to communication latency. Experimental results obtained from Hardware-in-the-Loop (HIL) simulation demonstrate the effectiveness of the proposed approach.

Bart Van Arem - One of the best experts on this subject based on the ideXlab platform.

  • rolling horizon Control Framework for driver assistance systems part i mathematical formulation and non cooperative systems
    Transportation Research Part C-emerging Technologies, 2014
    Co-Authors: Meng Wang, Winnie Daamen, S P Hoogendoorn, Bart Van Arem
    Abstract:

    In this contribution, we put forward a novel rolling horizon Control Framework for driver assistance systems. Under this Framework, accelerations of equipped vehicles are Controlled to optimise a cost function reflecting different Control objectives, taking into account the predicted behaviour of other vehicles. A new numerical solution based on Pontryagin's Principle is proposed to solve the optimal Control problem. The Control Framework is generic such that a large variety of objective functions can be optimised and it allows us to Control not only one vehicle but a platoon of heterogeneous vehicles as well. The linear constant time gap algorithm widely used for Adaptive Cruise Control (ACC) systems can be derived under the Framework. The Framework is applied to derive algorithm for a non-linear model predictive ACC Controller. Simulation results for several representative scenarios demonstrate the desired performance of the proposed ACC Controller and the sensitivity of Control parameters on Controller characteristics. The resultant flow capacity is largely determined by the desired time gap setting. To show the flexibility of the Framework, it is also applied to Controller design for Ecological ACC (EcoACC) systems, where the Controlled vehicle minimises fuel consumption in addition to the objectives of the ACC systems. Compared to ACC systems, EcoACC systems lead to smoother following behaviour and a 18 % reduction of consumed fuels in the accelerating phase of the simulation. Part II of this research is devoted to Controller design of cooperative driving systems, where Controlled vehicles communicate and collaborate with each other.

  • rolling horizon Control Framework for driver assistance systems part ii cooperative sensing and cooperative Control
    Transportation Research Part C-emerging Technologies, 2014
    Co-Authors: Meng Wang, Winnie Daamen, S P Hoogendoorn, Bart Van Arem
    Abstract:

    This contribution furthers the Control Framework for driver assistance systems in Part I to cooperative systems, where equipped vehicles can exchange relevant information via vehicle-to-vehicle communication to improve the awareness of the ambient situation (cooperative sensing) and to manoeuvre together under a common goal (cooperative Control). To operationalize the cooperative sensing strategy, the Framework is applied to the development of a multi-anticipative Controller, where an equipped vehicle uses information from its direct predecessor to predict the behaviour of its pre-predecessor. To operationalize the cooperative Control strategy, we design cooperative Controllers for sequential equipped vehicles in a platoon, where they collaborate to optimise a joint objective. The cooperative Control strategy is not restricted to cooperation between equipped vehicles. When followed by a human-driven vehicle, equipped vehicles can still exhibit cooperative behaviour by predicting the behaviour of the human-driven follower, even if the prediction is not perfect. The performance of the proposed Controllers are assessed by simulating a platoon of 11 vehicles with reference to the non-cooperative Controller proposed in Part I. Evaluations show that the multi-anticipative Controller generates smoother behaviour in accelerating phase. By a careful choice of the running cost specification, cooperative Controllers lead to smoother decelerating behaviour and more responsive and agile accelerating behaviour compared to the non-cooperative Controller. The dynamic characteristics of the proposed Controllers provide new insights into the potential impact of cooperative systems on traffic flow operations, particularly at the congestion head and tail.

Di Shi - One of the best experts on this subject based on the ideXlab platform.

  • a distributed cooperative Control Framework for synchronized reconnection of a multi bus microgrid
    IEEE Transactions on Smart Grid, 2018
    Co-Authors: Di Shi, Xi Chen, Zhiwei Wang, Xiaohu Zhang, Xinan Wang, Desong Bian
    Abstract:

    One critical value microgrids brings to power systems is resilience, the capability of being able to island from the main grid under certain conditions and connect back when necessary. Once islanded, a microgrid must be synchronized to the main grid before reconnection to prevent severe consequences. In general, synchronization of a single machine with the grid can be easily achieved using a synchronizer. The problem becomes more challenging when it comes to a multi-bus microgrid with multiple distributed generators (DGs) and dispersed loads. All DGs need to be properly Controlled in a coordinated way to achieve synchronization. This paper presents a novel bi-level distributed cooperative Control Framework for a multi-bus microgrid. In this Framework, DGs work collaboratively in a distributed manner using minimum and sparse communication. The topology of the communication network can be flexible which supports the plug-and-play feature of microgrids. Fast and deterministic synchronization can be achieved with tolerance to communication latency. Experimental results obtained from hardware-in-the-loop simulation demonstrate the effectiveness of the proposed approach.

  • a distributed cooperative Control Framework for synchronized reconnection of a multi bus microgrid
    arXiv: Systems and Control, 2017
    Co-Authors: Di Shi, Xi Chen, Zhiwei Wang, Xiaohu Zhang, Xinan Wang, Desong Bian
    Abstract:

    One critical value microgrids bring to power systems is resilience, the capability of being able to island from the main grid under certain conditions and connect back when necessary. Once islanded, a microgrid must be synchronized to the main grid before reconnection to prevent severe consequences. In general, synchronization of a single machine with the grid can be easily achieved using a synchronizer. The problem becomes more challenging when it comes to a multi-bus microgrid with multiple distributed generators (DGs) and dispersed loads. All distributed generators need to be properly Controlled in a coordinated way to achieve synchronization. This paper presents a novel bi-level distributed cooperative Control Framework for a multi-bus microgrid. In this Framework, DGs work collaboratively in a distributed manner using the minimum and sparse communication. The topology of the communication network can be flexible which supports the plug-and-play feature of microgrids. Fast and deterministic synchronization can be achieved with tolerance to communication latency. Experimental results obtained from Hardware-in-the-Loop (HIL) simulation demonstrate the effectiveness of the proposed approach.

Meng Wang - One of the best experts on this subject based on the ideXlab platform.

  • rolling horizon Control Framework for driver assistance systems part i mathematical formulation and non cooperative systems
    Transportation Research Part C-emerging Technologies, 2014
    Co-Authors: Meng Wang, Winnie Daamen, S P Hoogendoorn, Bart Van Arem
    Abstract:

    In this contribution, we put forward a novel rolling horizon Control Framework for driver assistance systems. Under this Framework, accelerations of equipped vehicles are Controlled to optimise a cost function reflecting different Control objectives, taking into account the predicted behaviour of other vehicles. A new numerical solution based on Pontryagin's Principle is proposed to solve the optimal Control problem. The Control Framework is generic such that a large variety of objective functions can be optimised and it allows us to Control not only one vehicle but a platoon of heterogeneous vehicles as well. The linear constant time gap algorithm widely used for Adaptive Cruise Control (ACC) systems can be derived under the Framework. The Framework is applied to derive algorithm for a non-linear model predictive ACC Controller. Simulation results for several representative scenarios demonstrate the desired performance of the proposed ACC Controller and the sensitivity of Control parameters on Controller characteristics. The resultant flow capacity is largely determined by the desired time gap setting. To show the flexibility of the Framework, it is also applied to Controller design for Ecological ACC (EcoACC) systems, where the Controlled vehicle minimises fuel consumption in addition to the objectives of the ACC systems. Compared to ACC systems, EcoACC systems lead to smoother following behaviour and a 18 % reduction of consumed fuels in the accelerating phase of the simulation. Part II of this research is devoted to Controller design of cooperative driving systems, where Controlled vehicles communicate and collaborate with each other.

  • rolling horizon Control Framework for driver assistance systems part ii cooperative sensing and cooperative Control
    Transportation Research Part C-emerging Technologies, 2014
    Co-Authors: Meng Wang, Winnie Daamen, S P Hoogendoorn, Bart Van Arem
    Abstract:

    This contribution furthers the Control Framework for driver assistance systems in Part I to cooperative systems, where equipped vehicles can exchange relevant information via vehicle-to-vehicle communication to improve the awareness of the ambient situation (cooperative sensing) and to manoeuvre together under a common goal (cooperative Control). To operationalize the cooperative sensing strategy, the Framework is applied to the development of a multi-anticipative Controller, where an equipped vehicle uses information from its direct predecessor to predict the behaviour of its pre-predecessor. To operationalize the cooperative Control strategy, we design cooperative Controllers for sequential equipped vehicles in a platoon, where they collaborate to optimise a joint objective. The cooperative Control strategy is not restricted to cooperation between equipped vehicles. When followed by a human-driven vehicle, equipped vehicles can still exhibit cooperative behaviour by predicting the behaviour of the human-driven follower, even if the prediction is not perfect. The performance of the proposed Controllers are assessed by simulating a platoon of 11 vehicles with reference to the non-cooperative Controller proposed in Part I. Evaluations show that the multi-anticipative Controller generates smoother behaviour in accelerating phase. By a careful choice of the running cost specification, cooperative Controllers lead to smoother decelerating behaviour and more responsive and agile accelerating behaviour compared to the non-cooperative Controller. The dynamic characteristics of the proposed Controllers provide new insights into the potential impact of cooperative systems on traffic flow operations, particularly at the congestion head and tail.

Camillo J Taylor - One of the best experts on this subject based on the ideXlab platform.

  • a vision based formation Control Framework
    International Conference on Robotics and Automation, 2002
    Co-Authors: Rafael Fierro, Vijay Kumar, James Ostrowski, John R Spletzer, Camillo J Taylor
    Abstract:

    We describe a Framework for cooperative Control of a group of nonholonomic mobile robots that allows us to build complex systems from simple Controllers and estimators. The resultant modular approach is attractive because of the potential for reusability. Our approach to composition also guarantees stability and convergence in a wide range of tasks. There are two key features in our approach: 1) a paradigm for switching between simple decentralized Controllers that allows for changes in formation; 2) the use of information from a single type of sensor, an omnidirectional camera, for all our Controllers. We describe estimators that abstract the sensory information at different levels, enabling both decentralized and centralized cooperative Control. Our results include numerical simulations and experiments using a testbed consisting of three nonholonomic robots.