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Huang J.,Xian University of Technology | Qian F.,Xian University of Technology | Qian F.,Xian Technological University | Wang L.,Xian University of Technology | Wang L.,University of Telecommunications and Posts
Proceedings of the World Congress on Intelligent Control and Automation (WCICA) | Year: 2015

For the LOG problems with bounded uncertain parameter existing in the state equation, a adaptive dual control approach is proposed. In this paper, it is assumed that the unknown parameter belongs to a known bounded interval. Firstly, a subdivision for the continuous bounded interval is used. Secondly, based on the subdivision, the dual controller is developed. Furthermore, the controller not only achieves minimization of the performance index, but also ensures the learning feature. Namely, it can learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. Finally, the effectiveness of the algorithm is verified by example simulation. © 2014 IEEE. Source


Wang L.,Xian University of Technology | Wang L.,University of Telecommunications and Posts | Qian F.,Xian University of Technology | Qian F.,Xian Technological University | Huang J.,Xian University of Technology
Proceedings of the World Congress on Intelligent Control and Automation (WCICA) | Year: 2015

Aiming at a class of nonlinear stochastic systems with additive Gaussian White noise and the polynomial nonlinear function we propose a shape control technique for probability density function (PDF) of the state variable. Controlling the PDF shape requires to design a controller making the PDF shape as close as possible to the desired PDF, and this actually is to determine the parameters of the controller. Firstly, we design the control law of the controller to be polynomial form, which is substituted into the dynamical equation of the nonlinear systems, obtaining the corresponding FPK equation. After a mount of derivation, we have the solution of the FPK equation, and the solution includes the parameters of the controller. Adopting the linear least square method, we find the exact solution of the FPK equation, and then solve out the parameters of the controller. Lastly, the algorithm is verified to be effective through the simulation. © 2014 IEEE. Source

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