Results 211 to 220 of about 158,186 (268)

Neural adaptive control of excavators

Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, 2002
An automatic control system for backhoe type excavators during free motion and digging operations is presented. Some of the uncertainties associated with the basically unstructured environment of soil digging tasks are dealt with by using an adaptive control system capable of on-line learning and control of the dynamic response over a wide range of ...
Bumjin Song, Antti J. Koivo
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NEURAL ADAPTIVE CONTROL OF A BIOREACTOR

IFAC Proceedings Volumes, 1994
Abstract This paper describes the application of two different adaptive control schemes based on neural networks to the control of a bioreactor. The results are a useful assessment of the adequacy of neural networks for identification and control of nonlinear systems.
R.E. Loke, G. Cembrano
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NEURAL NETWORK BASED ADAPTIVE CONTROL

Annual Review in Automatic Programming, 1994
This paper presents differents ways of using artificial neural networks in adaptive control. A classification of architectures for control using neural networks is presented, showing the existing paralelism with Adaptive Control techniques.
Eduardo F. Camacho, Manuel R. Arahal
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A neural adaptive controller

Chemical Engineering Science, 2000
In this paper, an adaptive neural network controller for the control of nonlinear dynamical systems is proposed. The new approach is adaptive in structure, and unlike standard adaptive controllers, uses no explicit model of the process in the design. Traditional neural networks are not practical in adaptive environments because of the large number of ...
Venugopal G Krishnapura, Arthur Jutan
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A neural adaptive controller

Proceedings of IEEE Systems Man and Cybernetics Conference - SMC, 2002
This paper deals with the application of neural techniques to the control of a nonlinear system. The process to be controlled is the opening of a turbofan exhaust nozzle. It exhibits strong nonlinearities and is difficult to control with classic methods. This time we have used an approach inspired by the concepts of indirect adaptive control.
Ph. Meyne, M. Houkari, C. Barret, J.M. Martinez, A. Garassino, P. Tormo   +5 more
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Combining Adaptive and Neural Control for Distillation Control

IFAC Proceedings Volumes, 1992
Abstract Although developments in the computer industry have moved towards highly integrated parallel processing, the control industry generally only makes use of the computer as a digital numerical manipulation tool for controlling the plant with a monitoring ability for failure detections.
M. Roele, K. Warwick
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Dynamic neural controller with somatic adaptation

IEEE International Conference on Neural Networks, 2002
A neural structure which is comprised of dynamic neural units with time-varying sigmoidal functions is proposed. The effect of sigmoidal gain on nonlinear dynamic systems is discussed. The learning and adaptive algorithm to determine the optimum sigmoidal gain, which results in selftuning of the neuron, is derived.
D. H. Rao, Madan M. Gupta
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Adaptive control with multiple neural networks

Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002
It is difficult to realize adaptive control for some complex nonlinear processes which are operated in different environments and the operation conditions are changed frequently. In this paper we propose an identifier-based adaptive control (or indirect adaptive control). The identifier uses two effective tools: multiple models and neural networks.
Wen Yu 0001, Xiaoou Li 0001
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Unsupervising adaption neural-network control

1990 IJCNN International Joint Conference on Neural Networks, 1990
Unsupervising learning control systems based on neural networks are discussed. The tasks are carried out by two neural networks which act as the plant identifier and system controller, respectively. A novel learning algorithm that can adapt the controller's control action by using information stores in the identifying network has been developed.
Gou-Jen Wang, Denny K. Miu
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Neural adaptive control of LoFLYTE(R)

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001
A major goal in flight control over the past decade has been the development of reconfigurable flight control systems which can adapt their gains in real-time to compensate for aircraft damage and in-flight system failures. The purpose of this paper is to describe the controller developed for the LoFLYTE(R) aircraft, which is a testbed for neural ...
Chadwick J. Cox, James C. Neidhoefer, Richard Saeks, George G. Lendaris   +3 more
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