Finite-level Quantized Iterative Learning Control by Encoding-Decoding Mechanisms

Chao Zhang, D. Shen
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Abstract

This paper studies the zero-error tacking problem of finite-level quantized iterative learning control using an encoding-decoding method, where both measurement and actuator side quantization and transmission are considered. In particular, the system output is encoded, quantized, transmitted and decoded in sequence for input updating of the next iteration. Then the generated input is transmitted through networks following the same procedure as the output transmission for plant input updating. The zero-error convergence of the proposed scheme is strictly proved and a numerical simulation is provided to demonstrate the effectiveness of the proposed scheme.
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基于编解码机制的有限级量化迭代学习控制
在考虑测量端和执行端量化和传输的情况下,采用编解码方法研究了有限级量化迭代学习控制的零误差跟踪问题。特别是,系统输出按顺序进行编码、量化、传输和解码,以便下一次迭代的输入更新。然后,按照与工厂输入更新的输出传输相同的过程,通过网络传输生成的输入。严格证明了该方案的零误差收敛性,并通过数值仿真验证了该方案的有效性。
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