Data-driven iterative learning control using a uniform quantizer with an encoding-decoding mechanism

Key Words:MODEL PREDICTIVE CONTROL; DISCRETE-TIME-SYSTEMS; NETWORKED CONTROL; DIGITAL NETWORKS; CONSENSUS

Abstract:This work explores the problem of uniform quantization of iterative learning control (ILC) for nonlinear nonaffine systems under a data-driven design and analysis framework. First, to deal with the strong nonlinearity and nonaffine structure of the systems, an iterative linear data model (iLDM) utilizing more additional parameter information is developed consequently bypassing modeling process. The iLDM only serves for the controller design and analysis without any mechanistic interpretation. Then, an encoding-decoding mechanism (E-DM) is employed to deal with the bounded tracking performance caused by the uniform quantizer. Using the iLDM, an E-DM based quantized data-driven ILC (E-D QDDILC) method is developed with a quantized learning control law and a quantized parameter estimation law, both of which only utilize the quantized output estimations obtained from the E-DM. The quantized parameter estimation law enhances the robustness of the proposed E-D QDDILC as an adaptive mechanism to tune the learning gain in real-time. A mathematical induction approach and the contraction mapping principle are introduced for the convergence analysis as the basic tools. When the scaling function is bounded, one shows the tracking error is bounded convergent. When the scaling function approaches zero iteratively, a zero convergence can be guaranteed in the iteration domain. The main results are verified through simulation examples.

Volume:32

Issue:7

Translation or Not:no