Networked sensing and control has attracted significant interest in recent years due to its wide applications. For example, sensor networks, especially wireless sensor networks, have found important applications in en...Networked sensing and control has attracted significant interest in recent years due to its wide applications. For example, sensor networks, especially wireless sensor networks, have found important applications in environmental monitoring, agriculture, building and industrial automation, machine condition monitoring, intelligent transportation systems, health care, surveillance, and defense. On the other hand, due to the flexibility and significant COSt-saving,展开更多
In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on ...In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi- UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.展开更多
This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot...This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.展开更多
Cooperative control of multi-agent systems linked by communication networks is a well-developed and still growing field. The interplay of the individual agent dynamics and the communication graph topology results in i...Cooperative control of multi-agent systems linked by communication networks is a well-developed and still growing field. The interplay of the individual agent dynamics and the communication graph topology results in intriguing and often surprising behaviors that are not manifested in the study of control systems for single-agent dynamics. This field brings systems theory, feedback control, graph theory, communication systems, complex systems theory to provide rigorous analysis and design for multiple dynamical systems interconnected by a graph information flow structure. Applications have been made to vehicle formation control, coordinated multi-satellite control, electric power system control, robotics, autonomous airborne systems, manufacturing production lines, and the synchronization of dynamical processes in chemistry, physics, biology, and chaotic systems.展开更多
Cooperative control of multi-agent systems linked by communication networks is a well-developed and still growing field. The interplay of the individual agent dynamics and the communication graph topology results in i...Cooperative control of multi-agent systems linked by communication networks is a well-developed and still growing field. The interplay of the individual agent dynamics and the communication graph topology results in intriguing and often surprising behaviors that are not manifested in the study of control systems for single-agent dynamics. This field brings systems theory, feedback control, graph theory, communication systems, complex systems theory to provide rigorous analysis and design for multiple dynamical systems interconnected by a graph information flow structure. Applications have been made to vehicle formation control, coordinated multi-satellite control, electric power system control, robotics, autonomous airborne systems, manufacturing production lines, and the synchronization of dynamical processes in chemistry, physics, biology, and chaotic systems.展开更多
文摘Networked sensing and control has attracted significant interest in recent years due to its wide applications. For example, sensor networks, especially wireless sensor networks, have found important applications in environmental monitoring, agriculture, building and industrial automation, machine condition monitoring, intelligent transportation systems, health care, surveillance, and defense. On the other hand, due to the flexibility and significant COSt-saving,
基金This work was supported in part by the National Natural Science Foundation of China （No. 61633007, 61703112）, in part by the China Postdoctoral Science Foundation （No. 2016M600643） and the special fund （No. 2017T100618）, and in part by the Office of Naval Research （No. N00014-17-1-2239, NO0014-18-1-2221 ）.
文摘In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi- UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.
基金The work was supported by the National Science Foundation，the Office of Naval Research grant，the AFOSR (Air Force Office of Scientific Research) EOARD (European Office of Aerospace Research and Development) grant，the U.S. Army Research Office grant
文摘This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.
文摘Cooperative control of multi-agent systems linked by communication networks is a well-developed and still growing field. The interplay of the individual agent dynamics and the communication graph topology results in intriguing and often surprising behaviors that are not manifested in the study of control systems for single-agent dynamics. This field brings systems theory, feedback control, graph theory, communication systems, complex systems theory to provide rigorous analysis and design for multiple dynamical systems interconnected by a graph information flow structure. Applications have been made to vehicle formation control, coordinated multi-satellite control, electric power system control, robotics, autonomous airborne systems, manufacturing production lines, and the synchronization of dynamical processes in chemistry, physics, biology, and chaotic systems.
文摘Cooperative control of multi-agent systems linked by communication networks is a well-developed and still growing field. The interplay of the individual agent dynamics and the communication graph topology results in intriguing and often surprising behaviors that are not manifested in the study of control systems for single-agent dynamics. This field brings systems theory, feedback control, graph theory, communication systems, complex systems theory to provide rigorous analysis and design for multiple dynamical systems interconnected by a graph information flow structure. Applications have been made to vehicle formation control, coordinated multi-satellite control, electric power system control, robotics, autonomous airborne systems, manufacturing production lines, and the synchronization of dynamical processes in chemistry, physics, biology, and chaotic systems.
