In this paper, an approach to resolve the kinematic redundancy and to control the motion/force of redundant manipulators is presented using the concept of a quasi-coordinate. Using this factorization, we can transform the joint space dynamics into a new decoupled form with dimensional consistency. ...
There have been many attempts to use neural networks in control systems. Based on special characteristics, they provide an approach with significant potential toward intelligent control. This paper reviews the emerging field of neural networks for control systems. The methods used in this field can ...
literature review, it can be found that these neural networks used in the control systems belong to the feedforward neural networks, which can improve the control performance of the magnetic levitation system by approximating a nonlinear function without prior knowledge of the closed-loop control ...
Neural networks for control systems A description is given of 11 papers from the April 1990 special issue on neural networks in control systems of IEEE Control Systems Magazine. The emphasis ... Antsaklis, P.J - 《IEEE Trans Neural Netw》 被引量: 909发表: 1990年 Neural Networks for Control...
In this article, we are concerned with neural-nets which can learn to control systems in accordance with a guiding intent, and can also learn how to formulate that control strategy or intent. The overall task of systems control is viewed as being carried out by four components, these being ...
on simulated systems, it suggests that advances in modern reinforcement learning may enable the solution of fundamental problems in neural control and movement towards more complex objectives in real systems. Introduction With the advent of deep learning and deep neural networks, reinforcement learning ...
Neural networks are used in a wide variety of applications in pattern classification, language processing, complex systems modeling, control, optimization, and prediction.92 Neural networks have also been actively used in many bioinformatics applications such as DNA sequence prediction, protein secondary ...
The structure of the neural network plant model is given in the following figure. This network can be trained offline in batch mode, using data collected from the operation of the plant. You can use any of the training algorithms discussed in Multilayer Shallow Neural Networks and Back...
The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and...
Controlling complex networks with complex nodes Article 24 March 2023 Introduction The problem of how to control complex systems has its roots in dynamical systems and optimization theory1,2,3,4. Mathematically, a dynamical system is said to be “controllable” if it can be steered from any init...