This paper focusses on the application of intelligent control techniques (neural networks,
fuzzy logic and genetic algorithms) and their hybrid forms (neuro-fuzzy networks, neuro …

An adaptive cerebellar-model-articulation-controller (CMAC)-based supervisory control
system is developed for uncertain nonlinear systems. This adaptive CMAC-based …

Over the past decades, machine learning techniques, such as supervised learning,
reinforcement learning, and unsupervised learning, have been increasingly used in the …

This paper aims to propose a more efficient control algorithm for chaos time-series
prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller …

This paper presents a general fuzzy reinforcement learning (FRL) method for biped dynamic
balance control. Based on a neuro-fuzzy network architecture, different kinds of expert …

This paper proposes the implementation of fuzzy motion control based on reinforcement
learning (RL) and Lagrange polynomial interpolation (LPI) for gait synthesis of biped robots …

This paper presents new path planning algorithms for bipedal robots without a trunk. The
proposed methods can be regarded as an extension to the concept of the inverted …

This paper presents a general control architecture for bipedal walking which is based on a
divide-and-conquer approach. Based on the architecture, the sagittal-plane motion-control …

In this paper, an adaptive cerebellar-model articulation computer (CMAC) neural network
(NN) control system is developed for a linear piezoelectric ceramic motor (LPCM) that is …

How to learn from both expert knowledge and measurement-based information for a robot to
acquire perception and motor skills is a challenging research topic in the field of …