Title :
An identification based indirect iterative learning control via data-driven approach
Author :
Ronghu Chi ; Tao Su ; Shangtai Jin
Author_Institution :
Sch. of Autom. & Electr. Eng., Qingdao Univ. of Sci. & Technol., Qingdao, China
Abstract :
In this paper, an iterative learning control approach is developed for a class of uncertainty nonlinear discrete-time systems based on the identification of the controlled system. At first, the linearized model of the nonlinear system is proposed. And then using the identification method, we present an indirect iterative learning control scheme for the controlled system. Analysis shows that the scheme can guarantee the system convergence under some conditions.
Keywords :
adaptive control; convergence of numerical methods; discrete time systems; identification; iterative methods; learning systems; linear systems; linearisation techniques; nonlinear control systems; uncertain systems; controlled system identification; convergence; data-driven approach; identification-based indirect iterative learning control; nonlinear system linearized model; uncertainty nonlinear discrete-time systems; Adaptation models; Control systems; Convergence; Educational institutions; Gold; Nonlinear systems; Robots; convergence; iterative identification; iterative learning control; linearized model; nonlinear discrete system;
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4673-1871-6
Electronic_ISBN :
978-1-4673-1870-9
DOI :
10.1109/ICARCV.2012.6485418
