This paper is concerned with the problem of stability of systems with time-varying delay in a given interval. A novel Lyapunov-Krasovskii functional is proposed to obtain new stability conditions. Some triple integral...
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This paper is concerned with the problem of stability of systems with time-varying delay in a given interval. A novel Lyapunov-Krasovskii functional is proposed to obtain new stability conditions. Some triple integral terms are introduced in the Lyapunov-Krasovskii functional and the information on the lower bound on the delay are sufficiently used. New delay-dependent stability criteria are derived using integral inequalities and formulated in terms of linear matrix inequality (LMI). Comparing numerical examples show that the proposed criteria yield a larger upper bound on the delay for a given lower bound on the delay than existing results.
作者：
Gong KunDeng FangMa TaoGong Kun is with School of Automation
Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Deng Fang is with School of Automation
Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Ma Tao is with School of Automation
Beijing Institute of Technology and Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China
In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optim...
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In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optimization (MPSO-FNN). This method makes use of Fourier Neural Network (FNN) to establish the error compensation model of electronic compass's azimuth, and introduces Modified Particle Swarm Optimization (MPSO) algorithm to optimize the weights of neural network. Thus the comparatively accurate error model of azimuth is obtained to compensate the output of electronic compass. This method not only has strong nonlinear approximation capability, but also overcomes the neural networks' shortcomings which are too slow convergence speed, oscillation, and easy to fall into local optimum and sensitive to the initial values. Experimental results demonstrate that after calibrated by this method, the range of azimuth error reduces to -0.35°~0.70° from -3.4°~25.2°, and the average value of absolute error is only 0.30°.
This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image...
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This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.
This paper is devoted to robust adaptive sliding mode control for a class of nonlinear systems in the Takagi-Sugeno forms with mismatched parametric uncertainties. Sufficient conditions for the existence of linear sli...
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A platform of the Internet-based teleoperation system with an omni-directional mobile robot which has a five DOFs robot arm is constructed. Remote control of the robot through the Internet is implemented. The system i...
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A platform of the Internet-based teleoperation system with an omni-directional mobile robot which has a five DOFs robot arm is constructed. Remote control of the robot through the Internet is implemented. The system is featured as low-cost and user interface friendly: remote users can control the robot through the Internet just by a client program in a general computer. The client computer can receive the live video and environment information measured by sensors. With the help of the remote video and the local simulation, users can easily communicate with the robot. Different modules are proposed and the implementation method of the system is presented. Related experiments are conducted to test the validity of the proposed system.
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