Neural network control for automatic braking control system

Ohno, Hiroshi; Suzuki, Toshihiko; Aoki, Keiko M.; Takahasi, Arata; Sugimoto, Gunji

doi:10.1016/0893-6080(94)90011-6

Cited by 20 publications

(5 citation statements)

References 14 publications

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“…The important point is that as long as disturbances are mostly reproducible and hardly stochastic, the compensator does not determine the performance. For mechatronic motion systems, this situation is often present, as can be concluded from the applications of the FEL controller that have been reported: − an automatic braking system for automobiles [Ohno et al, 1994]; − control of a camera system [Bruske et al, 1997]; − control of robot manipulators [Kim and Lee, 1996]; − welding [Tzafestas et al, 1997].…”

Section: Feedback Error Learningmentioning

confidence: 99%

Learning Feed-Forward Control: A Survey and Historical Note

Vries

Velthuis

Amerongen

2000

IFAC Proceedings Volumes

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From a mechatronic point of view, the performance of electromechanical motion systems can be improved by changing both the mechanical design and the controller. The design of a controller is generally based on a model of the plant. Thus, to improve the controller, a more accurate model of the plant is required. When the structure is not known or when many parameters cannot be determined, learning control may be considered. A simple yet powerful learning control scheme that is suitable for electro-mechanical motion systems is Learning Feed-Forward Control. In this paper an overview is given of applications that have been reported concerning this scheme. Also, relations are listed with alternative learning control schemes that are in some sense alike.

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Section: Feedback Error Learningmentioning

confidence: 99%

Learning Feed-Forward Control: A Survey and Historical Note

Vries

Velthuis

Amerongen

2000

IFAC Proceedings Volumes

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“…Firstly, the network control system is first introduced, and is modeled making its transformation into jumping discrete-time system, then the model of networked control system is studied for the stochastic stability. After this paper completes above study, this begins to the specific examples, to complete the identification for the method effectiveness in validation [1,2]. Finally, the stochastic stability of networked control systems has certain practical value on the application of network control system, and the use of modeling system related property problems have certain reference significance on the related problems of similar field [3].…”

Section: Introductionmentioning

confidence: 99%

Discussion and Research on Stochastic Stability Based on Network Control System

Liu¹

2013

AMM

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With the growing surge of network control system research results, the study of stability analysis aspect of network control system is diversified. This paper introduces the concept of packet loss and equivalent network-induced delay, the stochastic stability of network control model is studied, the use of network control system modeling is transformed into the concept of a discrete time based on jump linear system, and the stochastic stability some conditions of networked control systems are proposed. At the same time, the state feedback controller related to solve the problem will also translated into linear matrix inequality problems, which carries out the study of the stochastic stability of networked control systems. Finally, through the simulation examples, they are given to demonstrate the validity of the study method in application, which use the modeling method to study the problem, and also have certain significance in related fields in a certain extent.

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“…The main problem of this type of control is the computational cost that precludes realtime usage. For this reason, other authors, Zhu et al (2016) and Ohno et al (1994), proposed to use Neural Network, capable of reproducing the vehicle dynamics and acts in real time reducing computational cost. However, this type of control is based on an end-to-end approach, without any analytic correlation with the dynamics of the system.…”

Section: Introductionmentioning

confidence: 99%