Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education

Beijing, China

Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education

Beijing, China
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Li P.,Beijing Institute of Technology | Li P.,Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education | Chen J.,Beijing Institute of Technology | Chen J.,Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education | And 4 more authors.
International Journal of Innovative Computing, Information and Control | Year: 2013

This paper proposes an adaptive robust dynamic surface control (ARDSC) method integrated a novel self-constructing neural network (SCNN) for a class of complete non-affine pure-feedback systems with disturbances. By employing the mean-value theorem and implicit function theorem, the adaptive robust control (ARC) method is extended to pure-feedback systems, and improves the robustness and transient performance of the closed-loop system. The "explosion of complexity" in backstepping scheme is avoided via dynamic surface control (DSC) technique. Moreover, the controller complexity is further reduced by introducing an SCNN based on a novel pruning strategy and a width adjustment strategy. Input-to-state stability and small-gain theorem are utilized to analyze the stability of the closed-loop system. At the end, simulation results demonstrate effectiveness and advantages of the proposed control method. © 2013 ICIC International.


Deng F.,Beijing Institute of Technology | Deng F.,Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education | Chen J.,Beijing Institute of Technology | Chen J.,Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education | And 2 more authors.
International Journal of Innovative Computing, Information and Control | Year: 2012

This paper focuses on fault diagnosis for a class of digital sensors. The first derivative and second derivative of these sensors' output signal under normal conditions will not involve a great jump due to physical limitations. It is similar to maneuvering targets which do not exhibit particularly jump in velocity and acceleration. So, a real-time random sensor fault diagnosis is transformed into a maneuvering target tracking problem. And a fault diagnosis method independent on system models is proposed. An improved unscented Kalman filter (UKF) is employed to track the output and estimate the value of various states. A mean-adaptive acceleration (MAA) model is established to find the faults of digital sensors online. According to the analysis of the failure characteristics in different sampling conditions, a method is proposed to isolate the faults. Theoretical analysis and experimental results show that the method can diagnose and isolate digital sensor fault accurately in real applications. © 2012.

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