Adaptive control using neural networks and approximate models

Narendra, Kumpati S.; Mukhopadhyay, Snehasis

doi:10.1109/acc.1995.529269

Cited by 35 publications

(50 citation statements)

References 11 publications

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“…Multilayer neural networks have been applied successfully in the identification and control of dynamic systems [16,17]. Rather than attempt to survey the many ways in which multilayer networks have been used in control systems, we will concentrate on three typical neural network controllers: model predictive control [18], NARMA-L2 control [19], and model reference control [20]. These controllers are representative of the variety of common ways in which multilayer networks are used in control systems.…”

Section: Control System Applicationsmentioning

confidence: 99%

“…There are a number of variations of the neural network predictive controller that are based on linear model predictive controllers [21]. The neural network predictive controller that is discussed in this paper (based in part on Reference [18]) uses a neural network model of a nonlinear plant to predict future plant performance. The controller then calculates the control input that will optimize plant performance over a specified future time horizon.…”

Section: Nn Predictive Controlmentioning

confidence: 99%

“…The model predictive control method is based on the receding horizon technique [18]. The neural network model predicts the plant response over a specified time horizon.…”

Section: Predictive Controlmentioning

confidence: 99%

“…As with model predictive control, the first step in using NARMA-L2 control is to identify the system to be controlled. The NARMA-L2 model [18] is an approximation of the NARMA model of Equation (34). The NARMA-L2 approximate model is given by # y yðk þ dÞ ¼ f ½yðkÞ; yðk À 1Þ; .…”

Section: Identification Of the Narma-l2 Modelmentioning

confidence: 99%

See 3 more Smart Citations

An introduction to the use of neural networks in control systems

Hagan

Demuth

Jesús

2002

Intl J Robust & Nonlinear

333

191

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SUMMARYThe purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are discussed. The paper also presents three control architectures: model reference adaptive control, model predictive control, and feedback linearization control. These controllers demonstrate the variety of ways in which multilayer perceptron neural networks can be used as basic building blocks. We demonstrate the practical implementation of these controllers on three applications: a continuous stirred tank reactor, a robot arm, and a magnetic levitation system.

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Section: Control System Applicationsmentioning

confidence: 99%

Section: Nn Predictive Controlmentioning

confidence: 99%

“…The model predictive control method is based on the receding horizon technique [18]. The neural network model predicts the plant response over a specified time horizon.…”

Section: Predictive Controlmentioning

confidence: 99%

Section: Identification Of the Narma-l2 Modelmentioning

confidence: 99%

See 2 more Smart Citations

An introduction to the use of neural networks in control systems

Hagan

Demuth

Jesús

2002

Intl J Robust & Nonlinear

333

191

View full text Add to dashboard Cite

show abstract

“…In order to solve the aforementioned controller design problem, an approximation of the NARMA model using Taylor series expansion, called the NARMA-L2 model, was proposed in [43]. To derive this NARMA-L2 model, we first separate the current input u.k/ from the regression vector .k/, and the original NARMA model form in (5) can be further expressed as…”

Section: Approximation Of Narma Model: Narma-l2 Modelmentioning

confidence: 99%

Data‐driven identification and control of nonlinear systems using multiple NARMA‐L2 models

Yue

Chen

Gao

et al. 2017

Intl J Robust & Nonlinear

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Summary The multiple model approach provides a powerful tool for identification and control of nonlinear systems. Among different multiple model structures, the piecewise affine (PWA) models have drawn most of the attention in the past two decades. However, there are two major issues for the PWA model‐based identification and control: the curse of dimensionality and the computational complexity. To resolve these two issues, we propose a novel multiple model approach in this paper. Different from PWA models in which all dimensions of the regressor space are engaged in the partitioning, the key idea of the proposed multiple model architecture is to partition only the range of the control input u(k) at time k (the instant of interest in the control problem) into several intervals and identify a local model that is linear in u(k) within each interval. On the basis of Taylor's theorem, a theoretical upper bound for the approximation error of the model structure can also be obtained. With the proposed multiple model architecture, a switching control algorithm is derived to control nonlinear systems on the basis of the weighted one‐step‐ahead predictive control method and constrained optimization techniques. In addition, the upper bound for the tracking error using this switching control strategy is also analyzed rigorously under certain assumptions. Finally, both simulation studies and experimental results demonstrate the effectiveness of the proposed multiple model architecture and switching control algorithm. Copyright © 2017 John Wiley & Sons, Ltd.

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Intelligent controller based energy management for stand‐alone power system using artificial neural network

Boujoudar

Azeroual

Moussaoui

et al. 2020

Int Trans Electr Energ Syst

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Summary Standalone Power Systems (SPS), formerly known as Micro Power Systems, is an off‐grid electricity system for locations that are not fitted with an electricity generation. Typical SPS include one or more renewable electricity sources, energy storage, and regulation. In this paper, supervision of hybrid Photovoltaic system and battery storage is presented. The power balance of the hybrid system is made on an intelligent supervisor based on Artificial Neural Network Controller (ANNC). The main purpose of this paper is the control of DC/DC Bidirectional Converter (DBC), which connect the Li‐ion battery with the DC_Bus of Stand‐alone Power Systems (SPS). Based on the use of artificial neural network controller, which are effective tools for highly nonlinear system. The proposed technique does not require a pre‐defined model of power DBC only depend on power and voltage system data. Simulation for stand‐alone systems performed at Matlab Simulink. The comparison with PID controller for the result obtained of DC_Bus voltage, and power demanded, demonstrate the robustness, and satisfactory performance of the proposed control strategy.

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Adaptive control using neural networks and approximate models

Cited by 35 publications

References 11 publications

An introduction to the use of neural networks in control systems

An introduction to the use of neural networks in control systems

Data‐driven identification and control of nonlinear systems using multiple NARMA‐L2 models

Intelligent controller based energy management for stand‐alone power system using artificial neural network

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