Human Motor Learning Through Iterative Model Reference Adaptive Control

Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces

“…where we have used Ẇ = Ẇ . It can be verified that (û, e x , W ) = (0, 0, 0) is an isolated equilibrium of system (12). Moreover, the functions f 1 , f 2 , g are locally Lipschitz and their partial derivatives up to the second-order are bounded in their respective domains containing the origin.…”

“…Proof: Specializing [22,Theorem 11.3] to the autonomous system (12), and using Lemmas 1 and 2, we can show that the origin of ( 11) is an asymptotically stable equilibrium for a sufficiently small ε * .…”

“…For example, Shadmehr et al [11] proposed the idea of dynamic internal model in human motor learning that adapts to the changed limb dynamics because of a force field imposed on the hand. Similarly, Zhou et al [12] leveraged iterative model reference adaptive control to model human learning through repetitive tasks. However, most of these works only deal with motor learning in relatively low-dimensional spaces.…”

Optimal iterative learning control design for generator voltage regulation system

“…Iterative learning control (ILC) is a relatively new control method that was mainly introduced to improve the performance of processes that are repeated periodically over and over. It is used when a precise trajectory tracking is needed, for instance in robotics [1][2][3][4][5][6], glycemic control in diabetes mellitus [7], hard disk position control [8], iterative system identification [9], human learning behavior [10][11][12], industrial processes [13][14][15][16], electropneumatic servo systems [17], injection molding processes, food production facilities, robotic assembly lines, chemical batch reactors [18], and even for density control of freeway traffic flow [19]. The idea of the ILC algorithm was first introduced by Arimoto et al in 1984 [20].…”