Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes. (August 2018)

• Record Type: Journal Article
• Authors: Liu, Dong; Yang, Guang-Hong
• Is Part Of: Journal of process control. Volume 68(2018)
• Page Start: 186
• Page End: 194
• Publication Date: 2018-08
• Subjects: Data-driven control -- Model-free adaptive control -- Sliding mode control -- Prescribed performance control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.06.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The constrained tracking error is discussed in the design scheme, the presented strategy can guarantee that the convergence rate is not smaller than a pre-assigned constant and maximum steady-state error is less than an arbitrarily small prespecified value without requiring any system model information. By using the revised transformed error, the consuming time is reduced since the considered framework does not exploit the logarithm function. Abstract: In this paper, a new data-driven model-free adaptive sliding mode control is discussed for discrete-time nonlinear processes with tracking error constraint. Furthermore, a novel transformed error method together with a new sliding mode control framework is studied to ensure the tracking error converges to a predefined region all the time. Meanwhile, the proposed controller can guarantee that the convergence rate is not smaller than a pre-assigned constant and maximum steady-state error is less than an arbitrarily small prespecified value all the time, which is more effective in the complex industrial processes. The effectiveness of the considered strategy is validated by the simulation examples.