Adaptive Control with Recurrent High-order Neural Networks

Rovithakis, George A.; Christodoulou, Manolis A.

doi:10.1007/978-1-4471-0785-9

Cited by 275 publications

(210 citation statements)

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“…10,30 RHONNs are the result of including high-order interactions represented by triplets (y i y j y k ), quadruplets (y i y j y k y l ) and so on to the first-order Hopfield model. 10,30 The RHONN model used in this work is the seriesparallel model model, 10 which is defined aŝ…”

Section: Rhonnmentioning

confidence: 99%

Real-time neural identification and inverse optimal control for a tracked robot

Rios

Alanis

Lopez-Franco

et al. 2017

Advances in Mechanical Engineering

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This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter-based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB Ò , and also the experimental tests use a modified HD2Ò Treaded ATR Tank Robot Platform with wireless communication.

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Section: Rhonnmentioning

confidence: 99%

Real-time neural identification and inverse optimal control for a tracked robot

Rios

Alanis

Lopez-Franco

et al. 2017

Advances in Mechanical Engineering

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“…, p is a positive integer which denotes the number of external inputs, L denotes the neural network (NN) node number,  is an artificial quantity required only for analytical purposes [14,16]. In general, it is assumed that there exists an unknown but constant weight vector , w  whose estimate is L w   .…”

Section: Discrete-time Honnsmentioning

confidence: 99%

Decentralized Neural Backstepping Control Applied to a Robot Manipulator

Garcia-Hernandez

Ruz-Hernández

Lara

2013

International Journal of Advanced Robotic Systems

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This paper presents a discrete-time decentralized control scheme for trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The weights for each neural network are adapted online by an extended Kalman filter training algorithm. The motion for each joint is controlled independently using only local angular position and velocity measurements. The stability analysis for the closed-loop system via the Lyapunov approach is included. Finally, the real-time results show the feasibility of the proposed control scheme using a robot manipulator.

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“…For the case where the modelling error is not zero [Rovithakis, 2000], the solutions of differential equations (13) may become unbounded, even if the modelling error is bounded. Therefore, the learning law (13) has to be modified in order to avoid the parameter drift problem.…”

Section: Robust Updating Weight Lawmentioning

confidence: 99%

Decentralized Neuronal Control Structure

Benítez

Sánchez

Loukianov

2005

IFAC Proceedings Volumes

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A novel decentralized variable structure neural control approach for large-scale uncertain systems is developed using Recurrent High Order Neural Networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnections terms. The interconnection terms are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law, are propposed in order to approximate on-line the dynamical behaviour of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of proposed decentralized identification and control algorithm is illustrated via simulations as applied to stabilize an interconnected double inverted pendulum.

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Adaptive Control with Recurrent High-order Neural Networks

Cited by 275 publications

References 0 publications

Real-time neural identification and inverse optimal control for a tracked robot

Real-time neural identification and inverse optimal control for a tracked robot

Decentralized Neural Backstepping Control Applied to a Robot Manipulator

Decentralized Neuronal Control Structure

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