One-Step Predictive H2 FIR Tracking under Persistent Disturbances and Data Errors

O. Ibarra-Manzano, J. Andrade-Lucio, Y. Shmaliy, Yuan Xu
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Abstract

Information loss often occurs in industrial processes under unspecified impacts and data errors. Therefore robust predictors are required to assure the performance. We design a one-step H2 optimal finite impulse response (H2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors by minimizing the squared weighted Frobenius norms for each error. The H2-OFIR predictive tracker is tested by simulations assuming Gauss-Markov disturbances and data errors. It is shown that the H2-OFIR predictor has a better robustness than the Kalman and unbiased FIR predictor. An experimental verification is provided based on the moving robot tracking problem
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持续干扰和数据误差下的一步预测H2 FIR跟踪
在工业生产过程中,由于不明确的影响和数据错误,经常发生信息丢失。因此,需要稳健的预测器来保证性能。我们通过最小化每个误差的加权Frobenius规范的平方,设计了一个在持续干扰、测量误差和初始误差下的一步H2最优有限脉冲响应(H2- ofir)预测器。在假设高斯-马尔可夫干扰和数据误差的情况下,对H2-OFIR预测跟踪器进行了仿真测试。结果表明,H2-OFIR预测器比Kalman和无偏FIR预测器具有更好的鲁棒性。针对运动机器人的跟踪问题,给出了实验验证
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