Displaying similar documents to “Adaptive teleoperation system with neural network-based multiple model control.”

Neural network-based MRAC control of dynamic nonlinear systems

Ghania Debbache, Abdelhak Bennia, Noureddine Golea (2006)

International Journal of Applied Mathematics and Computer Science

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This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also...

Statistical-learning control of multiple-delay systems with application to ATM networks

Chaouki T. Abdallah, Marco Ariola, Vladimir Koltchinskii (2001)

Kybernetika

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Congestion control in the ABR class of ATM network presents interesting challenges due to the presence of multiple uncertain delays. Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to challenging control problems. In this paper, using some recent results by the authors, an efficient statistical algorithm is used to design a robust, fixed-structure, controller for a high-speed communication network with multiple uncertain...

Design of a neuro-sliding mode controller for interconnected quadrotor UAVs carrying a suspended payload

Özhan Bingöl, Haci Mehmet Güzey (2023)

Kybernetika

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In this study, a generalized system model is derived for interconnected quadrotor UAVs carrying a suspended payload. Moreover, a novel neural network-based sliding mode controller (NSMC) for the system is suggested. While the proposed controller uses the advantages of the robust structure of sliding mode controller (SMC) for the nonlinear system, the neural network component eliminates the chattering effects in the control signals of the SMC and increases the efficiency of the SMC against...

Approach to the design of robust networked control systems

Michał Morawski, Antoni M. Zajączkowski (2010)

International Journal of Applied Mathematics and Computer Science

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The paper describes the application of the traffic engineering framework together with application layer procedures as mechanisms for the reduction of network latency lags. These mechanisms allow using standard and inexpensive hardware and software technologies typically applied for office networking as a means of realising networked control systems (NCSs) with high dynamic control plants, where a high dynamic control plant is the one that requires the sampling period several times shorter...

Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode

Yun Chen, Hua Chen (2023)

Kybernetika

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To tackle the underactuated surface vessel (USV) trajectory tracking challenge with input delays and composite disturbances, an integral time-delay sliding mode controller based on backstepping is discussed. First, the law of virtual velocity control is established by coordinate transformation and the position error is caused to converge utilizing the performance function. At the same time, based on the estimation of velocity vector by the high-gain observer (HGO), radial basis function...

New challenges in dynamical systems: The networked case

Peter H. Bauer (2008)

International Journal of Applied Mathematics and Computer Science

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This paper describes new technical challenges that arise from networking dynamical systems. In particular, the paper takes a look at the underlying phenomena and the resulting modeling problems that arise in such systems. Special emphasis is placed on the problem of synchronization, since this problem has not received as much attention in the literature as the phenomena of packet drop, delays, etc. The paper then discusses challenges arising in prominent areas such as congestion control,...