Stable adaptive control of multivariable servomechanisms, with application to a passive line-of-sight stabilization system

Lee, T.H.; Koh, E. K.; Loh, Matilda

doi:10.1109/41.481413

Cited by 48 publications

(10 citation statements)

References 4 publications

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“…Statistical values comparison is performed in the Chapter Proposed algorithms comparison. [4] Inverse model controller (IMC) This algorithm is based on the mathematical inverse function to the servomotor transfer characteristic. This algorithm is very simple to design, stable in each condition, reliable in service and has small computational demands.…”

Section: Introductionmentioning

confidence: 99%

Engineering approaches in control of complex electro-mechanical systems

Andoga

Draganová

Laššák

2015

2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI)

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The paper deals with approaches in controlling of a complex electro-mechanical system. The proposed system is servomotor as a part of camera gimbal intended for UAVs (Unamnned Aerial Vehicles). Camera gimbal is a non-linear system with hysteresis and transport delays. The paper deals with experimental data driven method of servomotor modeling and also with classical control algorithms method and control algorithms based on fuzzy logic. The presented methods, approaches and applications are directly usable for similar electro-mechanical systems. The presented solution is cost effective, precise and fast using advanced algorithms.

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Section: Introductionmentioning

confidence: 99%

Engineering approaches in control of complex electro-mechanical systems

Andoga

Draganová

Laššák

2015

2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI)

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“…In recent decades, adaptive control of complex nonlinear systems such as robot manipulators with full-state constraints and uncertainties has been developed to deal with theoretical challenges and practical needs (Cheng, Cheng, Yu, Deng, & Hou, 2016;Y. Huang, Na, Wu, Liu, & Guo, 2015; T. Lee, Koh, & Loh, 1996;G.-H. Yang & Ye, 2006). In the field of adaptive control, neural networks (NNs) are always considered as an efficient way to handle the uncertain or poorly known dynamics due to their universal approximation capabilities (Cheng, Liu, Hou, Yu, & Tan, 2015;Hou, 2001;Kennedy & Chua, 1988;Z.…”

Section: Introductionmentioning

confidence: 99%

An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions

Xia

Zhang

Yang

et al. 2019

International Journal of Systems Science

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Conventional Neural Network (NN) control for robots use radial basis function (RBF) and for n-link robot with online control, the number of nodes and weighting matrix increases exponentially, which requires a number of calculations to be performed within a very short duration of time. This consumes a large amount of computational memory and may subsequently result in system failure. To avoid this problem, this paper proposes an innovative NN robot control using a dimension compressed RBF (DCRBF) for a class of n-degree of freedom (DOF) robot with full-state constraints. The proposed DCRBF NN control scheme can compress the nodes and weighting matrix greatly and provide an output that meets the prescribed tracking performance. Additionally, adaption laws are designed to compensate for the internal and external uncertainties. Finally, effectiveness of the proposed method has been verified by simulations. The results indicate that the proposed method, integral Barrier Lyapunov Functions (iBLF) avoids the existing defects of Barrier Lyapunov Functions (BLF) and prevents the constraint violations.

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“…The development in platform stabilization technology is driven primarily by accelerating developments in sensor technologies such as those of inertial sensors using microelectromechanical systems (MEMS) [4], low cost precision machinery [5], digital video signal processing methods and algorithms [6] as well as power electronics and servo drive technologies [7]. For example, the small scale mechanism presented in the present work has been made possible by the availability of very small size brushless linear motors.…”

Section: Introductionmentioning

confidence: 99%

Lagrangian based mathematical modeling and experimental validation of a planar stabilized platform for mobile systems

Rohani

Yazıcıoğlu

Mutlu

et al. 2014

Journal of Computational and Applied Mathematics

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a b s t r a c tTypical operating conditions for mobile sensor systems, and in particular mobile robots, exhibit a wide range of mechanical disturbances due their ego-motion. Sensor systems mounted on these mobile platforms often suffer to varying degrees from these disturbances. The quality of acquired data is degraded as a result. For instance, the quality of captured video frames from an onboard camera greatly depends on the angular velocity of the body on which the camera is mounted. Motion blur degradation results if large angular motions are present. In order to compensate for such disturbances, stabilization platforms are used. A common approach is measuring body movements using inertial sensors and attempting their cancellation with actuators and control systems. Design of high performance control systems often requires analytical system models. In this article, a planar stabilization platform is considered, to develop and study its kinematic and simple-tocomplex dynamic model. The mathematical derivation of the model is presented with and without neglect of the actuator mass components as well as friction effects. This is followed by the comparative validation of these model alternatives against a realistic numerical model fitted to physical experimental data. The results demonstrate that the analytical model, in particular with the actuator mass and friction components included, provides a high degree of fit to the actual behavior.

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Stable adaptive control of multivariable servomechanisms, with application to a passive line-of-sight stabilization system

Cited by 48 publications

References 4 publications

Engineering approaches in control of complex electro-mechanical systems

Engineering approaches in control of complex electro-mechanical systems

An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions

Lagrangian based mathematical modeling and experimental validation of a planar stabilized platform for mobile systems

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