Abstract
This paper develops a type of data-driven networked optimal iterative learning control strategy for a class of discrete linear time-varying systems with one-operation Bernoulli-type communication delays. In terms of the stochastic Bernoulli-type one-operation communication delayed inputs and outputs, the previous-iteration synchronous compensations are adopted. By means of deriving gradients of two types of objective functions that express the optimal approximation of the system matrix and the minimal tracking error, the strategy approximates the system matrix and upgrades the control inputs in an interact mode as the iteration evolves. By taking advantage of matrix theory and statistical technique, it is derived that the approximation discrepancy of the system matrix is bounded and the mathematical expectation of the tracking error vanishes as the iteration goes on. Numerical simulations manifest the validity and effectiveness.
Highlights
Iterative learning control (ILC) has been acknowledged as one of effectively intelligent strategies, which performs a high-precision trajectory tracking repetitively over a fixed time interval, as surveyed in [1,2,3]
In industry applications, model-based ILC can perform better than without any system information ILC, where at least an approximate model is needed. This implies that the ILC may be regarded as a data-driven scheme which utilizes the historical inputs, outputs, and model-approximation to formulate a sequence of updating control inputs
(DDNOILC) algorithm is developed for system (9) with oneoperation communication delays as follows: ũ1 = u1 and ỹ1 = y1: given test signals; H1: arbitrarily given and nonsingular; μ > 0 and ε > 0: set appropriately
Summary
Iterative learning control (ILC) has been acknowledged as one of effectively intelligent strategies, which performs a high-precision trajectory tracking repetitively over a fixed time interval, as surveyed in [1,2,3]. It is worth minding that the data-driven ILC utilizes the multi-iteration inputs and outputs to construct the updating law in a recursive mode This implies that the so-called open-loop ILC makes it possible to relax the communication delay within one-operation period. These motivate the paper to develop a data-driven optimal ILC scheme for a class of discrete linear time-varying systems with one-operation communication delays. Differing from the data-driven ILCs in [14,15,16], this paper compensates for the delayed Bernoulli-type inputs and outputs by its previousiteration synchronous data and analyzes the convergence of the approximation benefiting from matrix theory with no requirement of positively definite or diagonal dominance.
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