Xia Y.,Beijing Institute of Technology |
Xie W.,Key Laboratory of Intelligent Control |
Liu B.,Key Laboratory of Intelligent Control |
Wang X.,Key Laboratory of Intelligent Control
Information Sciences | Year: 2013
This paper is concerned with the problem of data-driven predictive control for networked control systems (NCSs), which is designed by applying the subspace matrices technique, obtained directly from the input/output data transferred from networks. The networked predictive control consists of the control prediction generator and network delay compensator. The control prediction generator provides a set of future control predictions to make the closed-loop system achieve the desired control performance and the network delay compensator eliminates the effects of the network transmission delay. The effectiveness and superiority of the proposed method is demonstrated in simulation as well as experiment study. © 2012 Elsevier Inc. All rights reserved.
Ma H.B.,Beijing Institute of Technology |
Ma H.B.,Key Laboratory of Intelligent Control |
Wang M.Z.,Beijing Institute of Technology |
Fu M.Y.,Beijing Institute of Technology |
And 2 more authors.
AIAA Guidance, Navigation, and Control Conference 2012 | Year: 2012
A new data-driven predictive discrete-time guidance law is presented for an interceptor pursuing a target which can perform arbitrary maneuver. The designed guidance law is driven by observed data of certain steps, which record previous positions of the target and make it feasible to estimate the behavior of the target and hence design the guidance command at each step by solving an time-dependent optimization problem, and this feature distinguishes the proposed guidance law from those traditional guidance laws which are usually described by an ordinary differential equations and use only the measurement at current time instant. To verify the performance of the new guidance law proposed, extensive simulations were carried out to compare it with some typical existing guidance laws like pursuit guidance (PG), beamer rider (BR) guidance, constant bearing (CB) guidance and proportional navigation (PN) law. The simulation studies show that the new predictive guidance law (abbreviated as LP) can provide comparative performance in all the cases studied, and it can even outperform other guidance laws when the target performs random maneuver, which show that the proposed guidance scheme exhibits certain robustness and adaptation. © 2012 by Hongbin Ma, on behalf of Beijing Institute of Technology.
Mao Y.,Beijing Institute of Technology |
Mao Y.,Key Laboratory of Intelligent Control |
Dou L.,Beijing Institute of Technology |
Dou L.,Key Laboratory of Intelligent Control |
And 4 more authors.
Journal of Systems Engineering and Electronics | Year: 2014
Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields (APF) which could guarantee the connectivity maintenance, collision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that all robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm. © 1990-2011 Beijing Institute of Aerospace Information.