Application of chaos in a recurrent neural network to control in ill-posed problems: a novel autonomous robot arm

Abstract

Inspired by a viewpoint that complex/chaotic dynamics would play an important role in biological systems including the brain, chaotic dynamics introduced in a recurrent neural network was applied to robot control in ill-posed situations. By computer experiments we show that a model robot arm without an advanced visual processing function can catch a target object and bring it to a set position under ill-posed situations (e.g., in the presence of unknown obstacles). The key idea in these works is adaptive switching of a system parameter (connectivity) between a chaos regime and attractor regime in a neural network model, which generates, depending on environmental circumstances, either chaotic motions or definite motions corresponding to embedded attractors. The adaptive switching results in useful functional motions of the robot arm. These successful experiments indicate that chaotic dynamics is potentially useful for practical engineering control applications. In addition, this novel autonomous arm system is implemented in a hardware robot arm that can avoid obstacles and reach for a target in a situation where the robot can get only rough target information, including uncertainty, by means of a few sensors, as indicated in the appendix, A1 and A2.

Improved quasi‐uniform stability criterion of fractional‐order neural networks with discrete and distributed delays

“…In the past few years, neural networks [1][2][3][4][5][6] have been a active topic owing to their wide applications in robot control [7], traditional Chinese medicine [8], biomedical image segmentation [9], scene text recognition [10], bearing fault diagnosis [11], image super-resolution [12], multimodal emotion recognition [13], bridge monitoring [14], and so on. Fractional calculus [15][16][17][18] is an effective instrument to characterize the hereditary and memory properties of various dynamical processes.…”