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Research on SINS Error Recursive Model of Ballistic Missile 被引量:2
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作者 鲜勇 李刚 《Defence Technology(防务技术)》 SCIE EI CAS 2010年第1期71-74,共4页
The strap-down inertial navigation system (SINS) error of ballistic missile is generated by the mutual influence of gyroscope and accelerometer, and the recursive model is completely different from that of gimbaled IN... The strap-down inertial navigation system (SINS) error of ballistic missile is generated by the mutual influence of gyroscope and accelerometer, and the recursive model is completely different from that of gimbaled INS. In the paper, a discrete error recursive model was obtained by studying the applied SINS error model of ballistic missile, and the discrete Kalman filtering simulation based on the model was carried out. The simulated results show that the model can depict the SINS error exactly and provide the advantages for research on integrated guidance and improved hit accuracy. 展开更多
关键词 control and navigation technology of aerocraft SINS error recursion discrete model
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Iterative identification of output error model for industrial processes with time delay subject to colored noise 被引量:1
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作者 董世健 刘涛 +1 位作者 李明忠 曹毅 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第12期2005-2012,共8页
To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to e... To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method. 展开更多
关键词 Time delay system Output error model Recursive least-squares Instrumental variable Variable forgetting factor
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NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS
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作者 Tian Sheping Ding Guoqing +1 位作者 Yan Detian Lin Liangming Department of Information Measurement and Instrumentation,Shanghai Jiaotong University,Shanghai 200030, China 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2004年第2期306-310,共5页
The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is... The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme. 展开更多
关键词 Artificial muscle Neural networks Recursive prediction error algorithm Nonlinear modeling and controlling
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Adaptive Hammerstein Predistorter Using the Recursive Prediction Error Method 被引量:2
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作者 李辉 王德生 陈兆武 《Tsinghua Science and Technology》 SCIE EI CAS 2008年第1期17-22,共6页
The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers (PAs) with memory effects. However, most available adaptive predistorters based on direct learning a... The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers (PAs) with memory effects. However, most available adaptive predistorters based on direct learning architectures suffer from slow convergence speeds. In this paper, the recursive prediction error method is used to construct an adaptive Hammerstein predistorter based on the direct learning architecture, which is used to linearize the Wiener PA model. The effectiveness of the scheme is demonstrated on a digital video broadcasting-terrestrial system. Simulation results show that the predistorter outperforms previous predistorters based on direct learning architectures in terms of convergence speed and linearization. A similar algorithm can be applied to estimate the Wiener PA model, which will achieve high model accuracy. 展开更多
关键词 power amplifier PREDISTORTER Wiener system Hammerstein system recursive prediction error method
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