In this paper, we propose an actor-critic neuro-control for a class of continuous-time nonlinear systems under nonlinear abrupt faults, which is combined with an adaptive fault diagnosis observer (AFDO). Together with its estimation laws, an AFDO scheme, which estimates the faults in real time, is designed based on Lyapunov analysis. Then, based on the designed AFDO, a fault tolerant actor- critic control scheme is proposed where the critic neural network (NN) is used to approximate the value function and the actor NN updates the fault tolerant policy based on the approximated value function in the critic NN. The weight update laws for critic NN and actor NN are designed using the gradient descent method. By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Finally, we verify the effectiveness of the proposed method through numerical simulations. 相似文献
Visual tracking is one of the most important problems considered in computer vision. To improve the performance of the visual tracking, a part-based approach will be a good solution. In this paper, a novel method of visual tracking algorithm named part-based mean-shift (PBMS) algorithm is presented. In the proposed PBMS, unlike the standard mean-shift (MS), the target object is divided into multiple parts and the target is tracked by tracking each individual part and combining the results. For the part-based visual tracking, the objective function in the MS is modified such that the target object is represented as a combination of the parts and iterative optimization solution is presented. Further, the proposed PBMS provides a systematic and analytic way to determine the scale of the bounding box for the target from the perspective of the objective function optimization. Simulation is conducted with several benchmark problems and the result shows that the proposed PBMS outperforms the standard MS.
We introduce a new method of solving C1 Hermite interpolation problems, which makes it possible to use a wider range of PH curves with potentially better shapes. By characterizing PH curves by roots of their hodographs in the complex representation, we introduce PH curves of type K(t−c)2n+1+d. Next, we introduce a speed reparametrization. Finally, we show that, for C1 Hermite data, we can use PH curves of type K(t−c)2n+1+d or strongly regular PH quintics satisfying the G1 reduction of C1 data, and use these curves to solve the original C1 Hermite interpolation problem. 相似文献
Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic
control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance
measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated
performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance
measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary
control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation
reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain
with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed
two models.
Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University,
Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial
Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial
Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles
at international journals. He is currently working on applications of artificial intelligence and adaptive control theory
in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor
manufacturing.
Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign.
Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication
Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering
from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management,
inventory control, production planning and scheduling.
Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received
his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively.
From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk
Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis,
and ubiquitous logistics information systems.
An erratum to this article can be found at 相似文献
This paper studies the steady-state queue length process of the MAP/G/1 queue under the dyadic control of the D-policy and multiple server vacations. We derive the probability generating function of the queue length and the mean queue
length. We then present computational experiences and compare the MAP queue with the Poisson queue.