Modern power system is a typical multi-level complex giant system consisting of physical infrastructures, human operators, and social resources, etc. The conventional analytical methods and simulation systems can'...
详细信息
The urban traffic coordination controls (UTCCs) can make full use of the mutual advantages of intersections, which makes it can improve the traffic access capacity and decrease the possibility of traffic congestion in...
详细信息
Web services are becoming the most promising technology for cloud computing. When a single web service fails to satisfy service requestor's multiple function demands, web services need to be configured together to...
详细信息
Nowadays, most industrial robots have been designed to be mechanically stiff with rigid link. When a robot with heavy payload is running in fast motions, the residual vibrations of the end-effector are primary caused ...
详细信息
Nowadays, most industrial robots have been designed to be mechanically stiff with rigid link. When a robot with heavy payload is running in fast motions, the residual vibrations of the end-effector are primary caused by joint flexibility. Therefore, the flexibility of joint cannot be neglected. This paper presents a systematic approach to dynamic modeling and residual vibration mode analysis for an industrial robot with rigid links and flexible joints (RLFJ). First, the flexibility of the joint is modeled as a torsional spring and the dynamic equations for this robot are derived by using Lagrange’s method. Then, to examine residual vibration properties of the system, numerical simulation is carried out. The following important conclusions are drawn from simulation results: (1) the proposed dynamic model can represent joint flexibility of industrial robot and the joint flexibility causes the residual vibration of the end-effector;(2) for a given RLFJ model, the residual vibration is mainly affected by the payload mass, path of the task and joint stiffness.
Artificial Transportation systems (ATS) provide a comprehensive perspective to study actual transportation systems, which are a kind of open and complex giant system referring to diverse engineering and social discipl...
详细信息
Autonomous navigation plays important role in mobile robots. In this paper, a navigation controller based on spiking neural networks (SNNs) for mobile robots is presented. The proposed target-reaching navigation contr...
详细信息
In this paper, a new stable value iteration adaptive dynamic programming (ADP) algorithm, named "θ-ADP" algorithm, is proposed for solving the optimal control problems of infinite horizon discrete-time nonl...
ISBN:
(纸本)9781467314886
In this paper, a new stable value iteration adaptive dynamic programming (ADP) algorithm, named "θ-ADP" algorithm, is proposed for solving the optimal control problems of infinite horizon discrete-time nonlinear systems. By introducing a parameter θ in the iterative ADP algorithm, it is proved that any of iterative control obtained in the proposed algorithm can stabilize the nonlinear system which overcomes the disadvantage of traditional value iteration algorithms. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative θ-ADP algorithm. Finally, a simulation example is given to illustrate the performance of the proposed method.
In this paper, we solve the H_∞ robust optimal control problem for discrete-time nonlinear systems with control saturation constraints using the iterative adaptive dynamic programming algorithm. First, a heuristic dy...
详细信息
ISBN:
(纸本)9781467314886
In this paper, we solve the H_∞ robust optimal control problem for discrete-time nonlinear systems with control saturation constraints using the iterative adaptive dynamic programming algorithm. First, a heuristic dynamic programming algorithm is derived to solve the Hamilton-Jacobi-Isaacs equation associated with the H_∞ control problem, and a convergence analysis is provided. Then, a dual heuristic dynamic programming algorithm with nonquadratic performance functional is developed to overcome the control saturation constraints. Finally, to facilitate the implementation of the algorithm, four neural networks are used to approximate the unknown nonlinear system, the control policy, the disturbance policy, and the value function.
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a no...
详细信息
ISBN:
(纸本)9781467322164
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a novel human action recognition approach which learns weights for features is proposed, where each feature is assigned a weight for human action representation. These weights are learned jointly with discriminative model. There are two advantages of our model. First, small weights are assigned to noise, which can help to reduce the effect of noise on representation of human action. Second, discriminative features, which are critical for human action recognition, are assigned large weights. Experimental results demonstrate the advantages of the proposed method.
Movie recommendation is a very popular service in internet based movie related websites such as NetFlix, MovieLens. The performance of the recommendation plays the key role in the user experience. Existing works have ...
详细信息
Movie recommendation is a very popular service in internet based movie related websites such as NetFlix, MovieLens. The performance of the recommendation plays the key role in the user experience. Existing works have shown that combining content based and collaborative filtering based algorithms is the best way for movie recommendation. Nevertheless, the performance of this hybrid algorithm is strongly depended on the strategy how to combine the basic pure algorithms. Existing works usually use a static combination strategy which may generate even worse performance for some users. To solve this problems, in this paper we propose a new item based hybrid algorithm that uses a dynamic user adaptive combination strategy. Besides, we also exploit the external open resources IMDB as the movie content data. Experiments on real datasets show that the dynamic user adaptive combination strategy can significantly enhance the performance of the recommendation and the external open resource IMDB is a very good information resource for recommendation.
暂无评论