Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Mobile Robotics: A Practical Introduction: History, Design, Analysis and Examples
Mobile Robotics: A Practical Introduction: History, Design, Analysis and Examples
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
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Advanced robotic systems require an end effector capable of achieving considerable gripping dexterity in unstructured environments. A dexterous end effector has to be able of dynamic adaptation to novel and unforeseen situation. Thus, it is vital that gripper controller is able to learn from its perception and experience of the environment. An attractive approach to solve this problem is intelligent control, which is a collection of complementary 'soft computing' techniques within a framework of machine learning. Several attempts have been made to combine methodologies to provide a better framework for intelligent control, of which the most successful has probably been that of neurofuzzy modelling. Here, a neurofuzzy controller is trained using the actor-critic method. Further, an expert system is attached to the neurofuzzy system in order to provide the reward signal and failure signal. Results show that the proposed framework permits a transparent-robust control of a robotic end effector.