Neural Network Control of Robot Arms and Nonlinear Systems

Lewis, Frank L.; Jagannathan, S.; Yeşildirek, Aydın

doi:10.1016/b978-012526430-3/50008-8

Cited by 118 publications

(116 citation statements)

References 21 publications

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“…The details of the reduced dynamics can be found in the works [1,3]. Let us to define the following variables…”

Section: The Description Of the Manipulator's Mathematical Model And mentioning

confidence: 99%

“…The proportional control P is frequently used in the literature [1,3]. Its advantage is the simplicity but the main disadvantages are a large force error lack of taking into account the dynamics of the force error signal.…”

Section: Force Controlmentioning

confidence: 99%

“…Dynamical equations of motion of the analysed manipulator are in the following form [1,2] ( ) ( ) ( ) ( ) ( ) ( ) ( )…”

Section: The Description Of the Manipulator's Mathematical Model And mentioning

confidence: 99%

“…Therefore the hybrid position-force control should be applied. The problem of the manipulator hybrid position-force control [1,2,3,4,5,6] is complex, because the manipulator is a nonlinear object, whose parameters may be unknown, variable and the working conditions are changeable. The hybrid control consists of a position control, which realises movement in the so-called contact surface, and a force control, which realises an interaction force normal to the surface.…”

Section: Introductionmentioning

confidence: 99%

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Conventional and Fuzzy Force Control in Robotised Machining

Hendzel

Burghardt

Gierlak

et al. 2013

SSP

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Abstract. This article presents an application of the hybrid position-force control of the robotic manipulator with use of artificial neural networks and fuzzy logic systems in complex control system. The mathematical description of the manipulator and a closed-loop system are presented. In the position control were used the PD controller and artificial neural networks, which compensate nonlinearities of the manipulator. The paper presents mainly the application of various strategies of the force control. The force control strategies using conventional controllers P, PI, PD, PID and fuzzy controllers are presented and discussed. All of the control methods were verified on the real object in order to make a comparison of a control quality.

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“…The details of the reduced dynamics can be found in the works [1,3]. Let us to define the following variables…”

Section: The Description Of the Manipulator's Mathematical Model And mentioning

confidence: 99%

Section: Force Controlmentioning

confidence: 99%

“…Dynamical equations of motion of the analysed manipulator are in the following form [1,2] ( ) ( ) ( ) ( ) ( ) ( ) ( )…”

Section: The Description Of the Manipulator's Mathematical Model And mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Conventional and Fuzzy Force Control in Robotised Machining

Hendzel

Burghardt

Gierlak

et al. 2013

SSP

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show abstract

“…According to the theory of nonlinear systems control, the mathematical structure of the positionforce control law was assumed in the form proposed by [11] …”

Section: Description Of the Manipulator Motion And Neuro Fuzzy Nonlinmentioning

confidence: 99%

Verification hybrid control of a wheeled mobile robot and manipulator

et al. 2016

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Abstract:In this article, innovative approaches to realization of the wheeled mobile robots and manipulator tracking are presented. Conceptions include application of the neural-fuzzy systems to compensation of the controlled system's nonlinearities in the tracking control task. Proposed control algorithms work on-line, contain structure, that adapt to the changeable work conditions of the controlled systems, and do not require the preliminary learning. The algorithm was veri cation on the real object which was a Scorbot -ER 4pc robotic manipulator and a Pioneer -2DX mobile robot.

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Robust adaptive fuzzy control of nonaffine systems guaranteeing transient and steady state error bounds

Bechlioulis

Rovithakis

2012

Adaptive Control & Signal

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SUMMARY We consider the prescribed performance tracking problem of single‐input single‐output, unknown, nonaffine systems in the presence of exogenous disturbances. By prescribed performance, we mean that the tracking error should converge to a predefined arbitrarily small residual set, with convergence rate no less than a prespecified value, exhibiting maximum overshoot less than a sufficiently small preassigned constant. The aforementioned goal is obtained via an output error transformation, which incorporates the desired performance characteristics, and whose boundedness is proven a sufficient condition. Fuzzy systems are utilized to approximate the system uncertainties, and consequently, a smooth robust adaptive fuzzy controller is designed to achieve the target. Simulation studies clarify and verify the approach. Copyright © 2012 John Wiley & Sons, Ltd.

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Neural Network Control of Robot Arms and Nonlinear Systems

Cited by 118 publications

References 21 publications

Conventional and Fuzzy Force Control in Robotised Machining

Conventional and Fuzzy Force Control in Robotised Machining

Verification hybrid control of a wheeled mobile robot and manipulator

Robust adaptive fuzzy control of nonaffine systems guaranteeing transient and steady state error bounds

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