Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes

Abstract

Abstract-In this paper, two adaptive observer-based strategies are proposed for control of nonlinear processes using input/output (I/O) data. In the two strategies, pseudo-partial derivative (PPD) parameter of compact form dynamic linearization and PPD vector of partial form dynamic linearization are all estimated by the adaptive observer, which are used to dynamically linearize a nonlinear system. The two proposed control algorithms are only based on the PPD parameter estimation derived online from the I/O data of the controlled system, and Lyapunov-based stability analysis is used to prove all signals of close-loop control system are bounded. A numerical example, a steam-water heat exchanger example and an experimental test show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.Note to Practitioners-In actual industrial process, the dynamic behaviors is complex and nonlinear, and their mathematical models are often difficult to obtain. How to design the controller for unknown nonlinear systems using input/output (I/O) data has become one main focus of control researches. Therefore, in this paper, two adaptive observer-based data-driven control algorithms are proposed for a class of unknown nonlinear systems. Finally, the effectiveness of two control strategies are illustrated via simulation study and experimental test.Index Terms-Data-driven control, adaptive observer, pseudopartial derivative, Lyapunov-based stability analysis, nonlinear discrete-time systems.

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