Abstract
As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.
Highlights
In recent years, the research of neural network (NN) has attracted great attention
The concept of artificial NNs was initially investigated by McCulloch and Pitts in the 1940s [3], where the network is established with a parallel structure
Despite the impossibility in identifying or listing all the related contributions in this short review, efforts have been made to summarize the recent progress in the area of NN control and its particular applications in the robot learning control, the robot interaction control, and the robot recognition control
Summary
The research of neural network (NN) has attracted great attention. It is well known that, mammals’ brain, which consists of billions of interconnected neurons, has the ability to deal with complex and computationally demanding tasks, such as face recognition, body motion planning, and muscles activities control. The approximation errors could be made arbitrarily small by choosing sufficient neurons This enables us to deal with control problems for complex nonlinear systems [8,9,10,11,12,13]. NN has been extensively used for functions approximation, such as to compensate for the effect of unknown dynamics in nonlinear systems [20,21,22,23,24,25,26,27,28,29,30,31]. Thanks to the universal approximation and learning ability, the NN has been widely applied in robot control with various applications. We present a brief review of robot control by means of neural network.
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