Quantized Output Observer-based Data Driven Model-free Adaptive Control

This paper studies the quantized output data observer-based data-driven model-free adaptive control(qMFAC) for discrete-time nonlinear systems with unknown structures and network transmission constraints. First, an adaptive observer based on quantized output data is generated with the use of a logarithmic quantizer, and a pseudo-biased derivative(PPD) estimation scheme based on the output quantized data observer is proposed. By dynamic linearization(DL) techniques, a incomplete equivalent data model containing quantized output data are built. Then, the observer output is used to develop a data-driven model-free adaptive control strategy that only makes use of quantified output and input. With the Lyapunov function and sector boundary approaches, the bounded tracking performance of the proposed qMFAC is strictly theoretical analyzed, and the effectiveness of qMFAC is verified through numerical simulation and simulation experiments of the shell and tube heat exchanger control system.