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Research Papers

Iterative Learning Control With Switching Gain Feedback for Nonlinear Systems

[+] Author and Article Information
P. R. Ouyang

Department of Aerospace Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canadapouyang@ryerson.ca

B. A. Petz

Department of Aerospace Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canadabpetz@ryerson.ca

F. F. Xi

Department of Aerospace Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canadafengxi@ryerson.ca

J. Comput. Nonlinear Dynam 6(1), 011020 (Oct 13, 2010) (7 pages) doi:10.1115/1.4002384 History: Received September 12, 2009; Revised July 01, 2010; Published October 13, 2010; Online October 13, 2010

Iterative learning control (ILC) is a simple and effective technique of tracking control aiming at improving system tracking performance from trial to trial in a repetitive mode. In this paper, we propose a new ILC called switching gain PD-PD (SPD-PD)-type ILC for trajectory tracking control of time-varying nonlinear systems with uncertainty and disturbance. In the developed control scheme, a PD feedback control with switching gains in the iteration domain and a PD-type ILC based on the previous iteration combine together into one updating law. The proposed SPD-PD ILC takes the advantages of feedback control and classical ILC and can also be viewed as online-offline ILC. It is theoretically proven that the boundednesses of the state error and the final tracking error are guaranteed in the presence of uncertainty, disturbance, and initialization error of the nonlinear systems. The convergence rate is adjustable by the adoption of the switching gains in the iteration domain. Simulation experiments are conducted for trajectory tracking control of a nonlinear system and a robotic system. The results show that fast convergence and small tracking error bounds can be observed by using the SPD-PD-type ILC.

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Figures

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Figure 2

Maximum error bounds for example 2

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Figure 3

Comparison of SPD-PD and PD-PD learning control

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Figure 4

Effect of proportional control gains to convergence rate

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Figure 5

Tracking performance and controlled torque for joint 1 (the first, third, and fifth iterations)

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Figure 6

Tracking performance and controlled torque for joint 2 (the first, third, and fifth iterations)

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Figure 7

Tracking error bounds from iteration to iteration

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Figure 1

Maximum error bounds for example 1

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