Adaptive control and identification using one neural network for a class of plants with uncertainties

Human-robotic systems that include interaction between human operators and robots should be designed with careful consideration for the dynamic property and control ability of a human operator. This paper performs manual tracking control tests on a human-robotic system using an impedance-controlled robot, and investigates control characteristics of a human operator according to the robot impedance properties. Experimental results demonstrate that humans try to maintain dynamic properties of an overall system as constant as possible by adjusting their own impedance properties. Then, a new training system using a neural network for operating a human-robotic system is constructed on the basis of the experimental findings in the human tracking control properties.

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“…including an unknown multiplicative modeling error [32], where is the reference model: the target training property of the overall system.…”

Section: A Formulationmentioning

confidence: 99%

Tracking Control Properties of Human–Robotic Systems Based on Impedance Control

Tsuji

Tanaka

2005

IEEE Trans. Syst., Man, Cybern. A

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“…The neural networks usage in this area is explained mainly by their following capability: flexible structure to model and learn nonlinear systems behavior (Cibenko, 1989). In general, two neural networks topologies are used: feedforward neural networks combined with tapped delays (Hemerly and Nascimento, 1999;Tsuji et al, 1998) and recurrent neural networks (Sivakumar et al, 1999;Ku and Lee, 1995). The neural network used here belongs to the former topology.…”

Section: Introductionmentioning

confidence: 99%

“…Although our approach is similar to that used by Tsuji et al (1998), it has three main advantages (Cajueiro, 2000): (a) while their method demands modification in the weight updating equations which is computationally more complicated, our training procedure can be performed by a conventional feedforward algorithm; (b) while their paper presents a local asymptotic stability analysis of the parametric error for the neural network trained by backpropagation algorithm where the nominal model must be SPR, that analysis can also be applied to our scheme without this condition; (c) their scheme can be only applied for plants with stable nominal model, which is not the case here. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Direct adaptive control using feedforward neural networks

Cajueiro

Hemerly

2003

Sba Controle & Automação

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This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.
Este artigo propõe uma nova estratégia de controle adaptativo direto em que uma única rede neural é usada para simultaneamente identificar e controlar uma planta. A motivação para essa estratégia de controle adaptativo é compensar a entrada de controle gerada por um controlador retroalimentado convencional. O processo de treinamento da rede neural é realizado através de duas técnicas: backpropagation e filtro de Kalman estendido. Adicionalmente, a convergência do erro de identificação é analisada através do segundo método de Lyapunov. O desempenho da estratégia proposta é avaliado através de simulações e uma aplicação em tempo real

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“…Pointwise linearization has been claimed by neural network-based control and/or adaptive control, which uses linear models to approximate predominant dynamics around an operating point or every input-output dynamic gain at each time instance and then employs a neural network to determine the error induced by the linearization [24,25].…”

Section: Model Identificationmentioning

confidence: 99%

Control of Complex Nonlinear Dynamic Rational Systems

et al. 2018

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Nonlinear rational systems/models, also known as total nonlinear dynamic systems/models, in an expression of a ratio of two polynomials, have roots in describing general engineering plants and chemical reaction processes. The major challenge issue in the control of such a system is the control input embedded in its denominator polynomials. With extensive searching, it could not find any systematic approach in designing this class of control systems directly from its model structure. This study expands the U-model-based approach to establish a platform for the first layer of feedback control and the second layer of adaptive control of the nonlinear rational systems, which, in principle, separates control system design (without involving a plant model) and controller output determination (with solving inversion of the plant U-model). This procedure makes it possible to achieve closed-loop control of nonlinear systems with linear performance (transient response and steady-state accuracy). For the conditions using the approach, this study presents the associated stability and convergence analyses. Simulation studies are performed to show off the characteristics of the developed procedure in numerical tests and to give the general guidelines for applications.

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Adaptive control and identification using one neural network for a class of plants with uncertainties

Cited by 11 publications

References 43 publications

Tracking Control Properties of Human–Robotic Systems Based on Impedance Control

Tracking Control Properties of Human–Robotic Systems Based on Impedance Control

Direct adaptive control using feedforward neural networks

Control of Complex Nonlinear Dynamic Rational Systems

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