An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control |
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Authors: | Shu-Ting Sun Ren-Xin Zhong |
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Affiliation: | 1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China;2. Key Lab of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China;3. Key Lab of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China;4. School of Engineering, Sun Yat-sen University, Guangzhou, China |
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Abstract: | For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law. |
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Keywords: | Freeway traffic iterative learning control (ILC) input constraints nonlinear switched discrete-time systems |
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