Improved D‐Type Anticipatory Iterative Learning Control for a Class of Inhomogeneous Heat Equations

Abstract

This paper aims at providing a practical iterative learning control (ILC) scheme for a wide class of heat transfer systems in the sense that it avoids high-gain learning of ILC, thus a potential non-monotonic convergence issue, and the risk of violating the hardware limitation of input profile in implementation. Meanwhile, the ILC scheme guarantees the identical initial condition of heat process. As a result, the output tracking precision may be improved while not reducing the anticipatory step size as in [1]. All the benefits of the proposed ILC scheme are achieved by applying a heuristic selection algorithm for the anticipatory step size and rectifying the output reference simultaneously.

Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints

“…After three decades of developments, ILC has shown its distinct advantages in handling with high-nonlinearity, complexity, and high-precision tracking problems. [24][25][26][27][28][29][30][31] Some pioneering works have been reported on ILC for MAS. The first result on this topic was given in the work of Ahn and Chen, 32 where the authors considered the formation control problem.…”

Fault diagnosis for semilinear distributed parameter systems with actuator/sensor based on iterative learning control

“…ILC was originally considered for robot applications by Arimoto et al [13] in 1984. Since then, ILC not only has been extensively investigated in practical applications [14][15][16] but also in the theoretical analysis of various systems [17][18][19][20]. Recently, ILC has achieved great development in fault diagnosis fields [21][22][23][24].…”