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Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes
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DOI：10.1109/TASE.2013.2284062.png)
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Abstract
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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.
Keywords:
Adaptive observer
Data-driven control
Lyapunov-based stability analysis
nonlinear discrete-time systems
pseudo-partial derivative
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