Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface

Abstract

In this work, an enhanced data-driven optimal iterative learning control (eDDOILC) is proposed for nonlinear nonaffine systems where a new iterative sliding mode surface (ISMS) is designed to replace the traditional tracking error in the controller design and analysis. It is the first time to extend the sliding mode surface to the iteration domain for systems operate repetitively over a finite time interval. By virtual of the new designed ISMS, the control design becomes more flexible where both the time and the iteration dynamics can be taken into account. Before proceeding to the controller design, an iterative dynamic linear data model is built between two consecutive iterations to formulate the linear input-output data relationship of the repetitive nonlinear nonaffine discrete-time system. The linear data model is virtual and does not have any physical meanings, which is very different to the traditional mechanism mathematical model. In the sequel, the eDDOILC is proposed by designing an objective function with respect to the proposed two-dimensional ISMS. Rigorous proof is provided to show the convergence of the proposed eDDOILC method. Furthermore, the results have been extended to a multiple-input multiple-output (MIMO) nonaffine nonlinear discrete-time repetitive system. In general, the proposed eDDOILC is data-driven where no explicit model information is included. It is illustrated that the presented eDDOILC is effective when applied to the nonlinear nonaffine uncertain systems.

Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots

“…A constrained fast-terminal sliding surface–based MFAC has also been developed in Esmaeili et al (2020). From the iteration point of view, in Chi et al (2019), an optimization-based model-free adaptive iterative learning sliding mode control has been designed for SISO plants, and its extension to MIMO systems has been proposed in Wang et al (2020a). The first and the only DSMC-based MFAILC for trajectory tracking performance of exoskeleton robots has been reported in Qiu et al (2020).…”

Multi-lagged-input information enhancing quantized iterative learning control

“…However, in real applications, the mathematical models of the systems are difficult to acquire because of the great complexity of the practical processes. Therefore, data-driven control methods (Rădac et al, 2014; Rădac and Precup, 2015; Liang et al, 2019a; Lin et al, 2019; Wang et al, 2020; Zhang et al, 2020), only using input/output data for controller design, become more attractive in the field of control theory. However, only a few works have been found to devote to investigating quantized data-driven iterative learning control (DDILC) method.…”