Quantized Iterative Learning Control Based On Encoding And Decoding Mechanism

As one of the effective methods to reduce the transmission burden and improve the operation efficiency of control systems,quantization is fairly suitable for control systems under network circumstances.However,as the side effect,data accuracy is therefore reduced.The encoding and decoding mechanism can just solve this problem effectively.For iterative learning control(ILC),it is of great theory and application significance to consider how to combine the quantization and encoding-decoding mechanisms to realize high-precision tracking performance.This thesis studies the quantized ILC problem based on an encoding-decoding mechanism,which is the process of transforming(encoding)and restoring(decoding)signals.This thesis introduces the preliminary knowledge firstly,including quantization and data dropouts as well as the research progress of quantized ILC in Chapter 1.Chapter 2 to 4 are the specific issues and results:1.Chapter 2 studies quantized ILC problem based on encoding-decoding mechanism using infinite and finite uniform quantizer respectively,where only the output quantization case is considered.For infinite quantizer case,the zero-error convergence is realized by the organic combination of infinite uniform quantizer and encoding-decoding mechanism.Both linear systems and affine nonlinear systems are considered.The simulation verifies the theoretical results.Then quantized ILC problem based on encoding-decoding mechanism using finite uniform quantizer is considered.Compared with infinite quantizer case,the difficulty lies in how to choose an appropriate quantizer saturation upper bound to realize the zero-error tracking performance using a finite number of quantization levels.3.Chapter 3 studies the application of encoding-decoding based ILC with finite uniform quantizer at both measurement and actuator sides.Compared with the previous chapters,the difficulty are two folds.The first difficult is how to select the appropriate quantizer saturation upper bound at each side and the second lies in the coupling between the measurement and actuator sides.Through strict proofs,the zero-error convergence for the general framework is obtained.4.Chapter 4 studies the quantized ILC problem with data dropouts.The critical issue of applying the encoding-decoding method is to insure the synchronous internal state update between the encoder and decoder.While random data dropouts arises,we need to design the update laws of encoder,decoder and controller to deal with data dropouts.It is shown that the proposed framework can still achieves zero-error tracking performance.