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Zhang X.,Xian Jiaotong University | Chen X.,Xian Jiaotong University | You S.,System Engineering Research Institute | He Z.,Xian Jiaotong University
JVC/Journal of Vibration and Control | Year: 2015

The active control method is a popular way to control mechanical vibration. However, for some industrial needs and military requirements, it does not only need to control vibration, but also has to change the online constant working frequency responses, for example to improve the dynamic performance of mechanical devices and the stealth of military equipment. Because the present active control methods mainly focus on vibration control, the dynamic frequency responses active control method (DFRACM) is constructed to deal with this problem. The constructed DFRACM can not only accomplish vibration control but can also arbitrarily change the dynamic frequency responses of mechanical equipment. Besides, in order to satisfy engineering requirements, the multi-objective DFRACM, which can control the dynamic frequency responses of the controlled object to reach separate specific objectives at different places in one time, is also studied in this paper. The effectiveness of the constructed method is verified through experiments of the open cylindrical shell structure. © The Author(s) 2014.

Feng Y.,Tsinghua University | Fan W.,Tsinghua University | Qin Y.,System Engineering Research Institute
Communications in Computer and Information Science | Year: 2013

In this paper, we built simulation model of the production line from one car engine parts plant in Beijing, in order to find proper solutions to raise productivity. The method of Discrete Event Simulation was used to construct the simulation model on account of the fact that production line was a typical discrete event system. Besides, worker heterogeneity, stochastic environment and the effect of worker learning and forgetting were introduced into simulation model to make it closer to reality. We proposed different schedule policies to manage the running of production line with the verification from simulation experiments. Then, by taking advantage of the simulation results obtained previous, we built the optimization model by applying Markov Decision Process (MDP) to seek for the best policy promoting the productivity of production line. © Springer-Verlag Berlin Heidelberg 2013.

Zhang L.,California Institute of Technology | Zhang L.,Tsinghua University | Zhang L.,System Engineering Research Institute | Cui T.,California Institute of Technology | And 2 more authors.
Signal Processing | Year: 2010

This paper considers the optimal power scheduling for the distributed estimation of a source parameter using quantized samples of noisy sensor observations in a wireless sensor network (WSN). Repetition codes are used to transmit quantization bits of sensor observations to achieve unequal error protection, and a quasi-best linear unbiased estimate is constructed to estimate the source parameter at the fusion center (FC). Based on the adopted distributed estimation scheme (DES), we optimize the power scheduling among sensors to minimize the L1-norm of the power vector subject to the desired tolerance, which implies minimizing the total transmission power. Since the optimization problem is not convex, we propose a low-complexity alternative, which minimizes the L2-norm of the power vector while insuring the desired tolerance. We derive the closed form solution of the L2-norm power scheduling scheme. Simulation results show that the total power consumption of the L2-norm power scheduling scheme is close to that of the L1-norm power scheduling scheme, while complexity analysis demonstrates that the L2-norm power scheduling scheme has very low complexity. © 2009.

Zhang X.,Xian Jiaotong University | Chen X.,Xian Jiaotong University | You S.,System Engineering Research Institute | He Z.,Xian Jiaotong University | Li B.,Xian Jiaotong University
Sensors | Year: 2012

In general, mechanical equipment such as cars, airplanes, and machine tools all operate with constant frequency characteristics. These constant working characteristics should be controlled if the dynamic performance of the equipment demands improvement or the dynamic characteristics is intended to change with different working conditions. Active control is a stable and beneficial method for this, but current active control methods mainly focus on vibration control for reducing the vibration amplitudes in the time domain or frequency domain. In this paper, a new method of dynamic frequency characteristics active control (DFCAC) is presented for a flat plate, which can not only accomplish vibration control but also arbitrarily change the dynamic characteristics of the equipment. The proposed DFCAC algorithm is based on a neural network including two parts of the identification implement and the controller. The effectiveness of the DFCAC method is verified by several simulation and experiments, which provide desirable results. © 2012 by the authors; licensee MDPI, Basel, Switzerland.

Jia X.,Beihang University | Wu S.,Beihang University | Sun J.,System Engineering Research Institute
Chinese Control Conference, CCC | Year: 2014

Over the past few years, multi-agent coordination and control solutions have been applied to many challenging civilian and defense applications: surveillance, search and rescue, and fire monitoring. The existing multi-UAV dynamic task allocation method needs long computation time and does not apply to hard real-time and uncertain environment conditions. A relatively simple coordination mechanism was designed in this paper in an attempt to solve the problem that exists in the multi-agent systems method. Only with three times of information exchanges, the UAVs can enter their own flight modes, and this avoids the frequent communication problems between the UAVs. What's more, the UAV guidance law was designed, and the task allocation strategy and vehicle's coupling and temporal correlation are combined in this paper in order to verify the feasibility of the dynamic task allocation strategies in a dynamic process. Digital simulation was performed in two scales of UAV formation in order to compare the new task allocation method with the existing methods. © 2014 TCCT, CAA.

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