Iterative learning controller design for nonlinear generalized distributed parameter system with correction factor
Iterative learning control techniques are suitable for systems or devices that run over a limited time interval repeatedly. These include manipulators, disk drives, and inverter circuits. Under the premise that the initial error is zero, the tracking error can be zero everywhere. This applies to the whole operation interval after several iterations. In practical applications however, the initial error is zero, which is difficult to achieve. In order towiden the application scope of iterative learning control technology in practical industrial applications, it is necessary to study the suitable iterative learning control method under the condition of non-zero initial error. In this paper, an iterative learning control (ILC) method for initial correction of state-constrained reference signals for nonparametric a class of generalized distributed parameter systems is proposed. This method is suitable for the case of a non-zero initial error. By tracking the zero error of the system state to the corrected reference signal in the whole operation interval, the zero error tracking of the system state to the reference signal is obtained. According to the characteristics of generalized distributed parameter system, this paper uses the principle of singular value decomposition. First, the singular value decomposition of the generalized distributed parameter system is carried out. Secondly, an iterative learning PD-type learning law with correction factor is designed. Then its convergence is proved theoretically and strictly proved by Bell-Grown theory. The convergence condition is given. Finally, the algorithm is simulated by numerical simulation. The simulation results show that the algorithm is effective.
Keywords: Nonlinear generalized distributed parameter systems; iterative learning control; correction factor
Z. Lan, Y. Zhang, M. Chen, "Iterative learning controller design for nonlinear generalized distributed parameter
system with correction factor.", Engineering Intelligent Systems, vol. 26 no. 2-3, pp. 139-145, 2018.