A Novel Model-Free Adaptive Control Design for Multivariable Industrial Processes

作者：Xu Dezhi^*; Jiang Bin; Shi Peng

来源：IEEE Transactions on Industrial Electronics, 2014, 61(11): 6391-6398.

DOI：10.1109/TIE.2014.2308161

• Data-driven control
• model-free adaptive control (MFAC)
• multiple adaptive observer
• multivariable nonlinear systems
• pseudopartial derivative (PPD)

In this paper, a multiple adaptive observer-based strategy is proposed for the control of multi-input multi-output nonlinear processes using input/output (I/O) data. In the strategy, the pseudopartial-derivative parameter matrix of compact form dynamic linearization is estimated by a multiple adaptive observer, which is used to dynamically linearize a nonlinear system. Then, the proposed data-driven model-free-adaptive-control algorithm is only based on the online identified multiobserver models derived from the I/O data of the controlled plants, and Lyapunov-based stability analysis is used to ensure that all signals of the close-loop control system are bounded. A numerical example and a Wood/Berry distillation column example are provided to show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.

• 出版日期2014-11
• 单位北京航空航天大学