Input–output‐driven gain‐adaptive iterative learning control for linear discrete‐time‐invariant systems

Abstract

For a class of linear discrete-time-invariant systems repetitively operated in a finite time length, an iterative correction algorithm is exploited to identify the system Markov parameters with multi-operation inputs and outputs in obeying a criterion, of which the time order of the corrector is adaptive to the time order of the controller. Interactively, an adaptive iterative learning control is architected which composes the compensator with the approximated Markov parameters and the tracking error in sequentially minimizing a quadratic objective function of the tracking error and the compensation. Algebraic manipulation achieves that the Markov parameters correction error is monotonically declining without any step parameter selection. Further, a rigorous derivation clarifies that the time order of the learning compensator must agree to the relative degree of the system and the tracking error is linearly monotonously convergent if the Markov parameters correction error falls into an allowable range. Inspiringly, the methods and results release the confinement to both the correction and the learning ratios. In addition, a smaller learning ratio factor tolerates a larger Markov parameters correction error. Numerical experiments support the significance and the validity.

The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

“…In the case when the system Markov parameters are unavailable, iterative learning identification ( 16) is embedded into the norm-optimal ILC (27), which is formed in the DDOILC scheme. In (28), the control law is equipped with an inversion matrix, which is prominently different from the existing strategies [19,21,22]; for avoiding matrix inversion and guaranteeing the convergence of the tracking error, the gain matrix of the control law is transigent and replaces the…”

“…However, it cannot achieve optimal control in the practical sense. Recently, a newly input-output-driven gain-adaptive ILC has been proposed to abolish the harsh condition [22].…”

A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network

“…In English learning, students who are motivated are more likely to develop a strong passion for learning, set high goals and face difficulties to achieve them, and continuously improve their learning level. This literacy includes four key competencies as learning ability, which is a necessary competency for lifelong learning and continuous development of students, and it puts higher demands on the cultivation of students' learning motivation [ 2 ].…”

Distributed iterative learning temperature control for large‐scale buildings

“…Different from other control methods, ILC uses input and output information from previous iterations to generate input for the current iteration 16 . ILC needs little information on the system model, and this is a data‐driven approach 17 . It has attracted significant attention and been widely used in chemical industry, 18 industrial robot control, 19 and wafer scanner systems 20 …”

“…16 ILC needs little information on the system model, and this is a data-driven approach. 17 It has attracted significant attention and been widely used in chemical industry, 18 industrial robot control, 19 and wafer scanner systems. 20 There are few works considering ILC for HVAC systems.…”