Effect analysis of data dropout on iterative learning control

Abstract

Iterative learning control (ILC) is an attractive technique to deal with systems that execute the same task repeatedly over a finite time-interval. The key feature of this technique is to use information from the previous (and/or current) operation (or iteration) in order to enable the controlled system to perform better progressively from operation to operation [1][2][3][4][5][6] . On the other hand, networked control systems (NCSs) are also the focus of many research studies over the last few decades [7][8][9][10] . Compared to the conventional point-to-point system connection, NCSs have the advantages of easy installation and reduced setup, wiring, and maintenance costs. In NCSs, data can travel through the communication channels from the sensors to the controller and from the controller to the actuators. Data packet dropout (a kind of uncertainty) is a common problem in networked control systems and could happen due to node failures or network congestion. Because of random dropout, conventional methods for estimation and control cannot be used directly.The data dropout problem in the context of ILC has been studied in [11][12][13][14][15][16], and such an ILC system is call intermittent ILC system. In [11] and [12], an optimal learning gain matrix is given for intermittent ILC systems. They considered the problem where each component in the multivariable output vector of the plant is subject to a dependent or independent dropout respectively. In [13], an averaging ILC algorithm is proposed to overcome the random data dropout, and it is shown that such an ILC algorithm can perform well and achieve asymptotic convergence in ensemble average along the iteration axis. In [14], the convergence of first-order and high-order ILC for linear intermittent ILC systems is considered. Using the super-vector technique, such an ILC system can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain, and then the convergence condition can be provided by solve a binary linear matrix inequality. It is worth noting that the binary linear matrix inequality is difficult to be solved, especially for the high order ILC scheme. To avoid the problem, another convergence condition in the expectation sense is given in [15]. In [16], the issue of intermittent ILC is considered for a class of nonlinear systems. Key conclusions of these works are that the ILC systems can still guarantee convergence in the face of data dropout as long as there is not 100% dropout. However, the effect of data dropout on ILC has not been studied. Intuitively, data dropout implies less information can be used, and it should be affect some performances of the system. This observation motivates the present study.In this paper, we analyze the effect of data dropout for intermittent ILC with asymptotic stability and monotonic convergence. Using the super-vector formulation, the relationship between convergence speed and data dropout rate can be presented. The remainder of this paper is organized as follows: In section...

Data-Driven Stochastic Optimal Iterative Learning Control for Nonlinear Non-Affine Systems With Measurement Data Loss

“…Ref. [14] shows that the rate of data loss has no effect on the convergence of the system. For random data loss, a switching ILC approach has been proposed in [15].…”