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Quantized Data Driven Iterative Learning Control for a Class of Nonlinear Systems With Sensor Saturation
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DOI：10.1109/TSMC.2018.2866909.png)
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Abstract
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This paper considers the problem of data driven iterative learning control (DDILC) for a class of nonaffine nonlinear systems subject to data quantization and sensor saturation. Two novel quantized DDILC (QDDILC) algorithms are proposed based on saturated and quantized information of system outputs. The convergence of the proposed QDDILC algorithms is strictly proved and the effects of output saturation and data quantification are also analyzed. It is shown that sensor saturation does not change the convergence property, thus it causes the convergence rate to slow down. For the QDDILC algorithm, data quantization will cause the tracking error to converge to a bound depending on the quantization level. However, the modified QDDILC algorithm, which using the different quantization scheme from QDDILC algorithm, can ensure that the tracking error converges to zero. Illustrative simulations are exploited to verify the theoretical results.
Keywords:
Convergence
Quantization (signal)
Control systems
Nonlinear systems
Linear systems
Task analysis
Iterative learning control
Data driven design
data quantization
iterative learning control (ILC)
sensor saturation
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