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| Multimodel-based flight control system reconfiguration control in the presence of input constraints |
| YuyingGUO,BinJIANG,YufeiXU |
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| (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China; School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China) |
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| 摘要: |
| In this paper, an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints, which is a combination of a direct adaptive control algorithm with multiple model switching. The μ-modification is introduced in the model reference architecture to construct the adaptive controller. The proof of stability is based on the candidate Lyapunov function, while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals. Simulation results illustrate the efficiency of the proposed method. |
| 关键词: Actuator fault Adaptive control reconfiguration Multiple model Input constraint RBF neural network |
| DOI： |
| Received:March 18, 2009Revised:June 20, 2009 |
| 基金项目:This work was supported by the Aeronautics Science Foundation of China (No.2007ZC52039) and the National Natural Science Foundation of China(No.90816023). |
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| Multimodel-based flight control system reconfiguration control in the presence of input constraints |
| Yuying GUO,Bin JIANG,Yufei XU |
| (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China; School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China) |
| Abstract: |
| In this paper, an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints, which is a combination of a direct adaptive control algorithm with multiple model switching. The μ-modification is introduced in the model reference architecture to construct the adaptive controller. The proof of stability is based on the candidate Lyapunov function, while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals. Simulation results illustrate the efficiency of the proposed method. |
| Key words: Actuator fault Adaptive control reconfiguration Multiple model Input constraint RBF neural network |
