e-journal
Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes
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 an 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 onemain 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—Adaptive observer, Data-driven control, Lyapunov-based stability analysis, nonlinear discrete-time systems, pseudo-partial derivative.
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