Displaying similar documents to “A family of model predictive control algorithms with artificial neural networks”

Efficient nonlinear predictive control based on structured neural models

Maciej Ławryńczuk (2009)

International Journal of Applied Mathematics and Computer Science

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This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction...

Soft computing in modelbased predictive control footnotemark

Piotr Tatjewski, Maciej Ławrynczuk (2006)

International Journal of Applied Mathematics and Computer Science

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The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model...

Nonlinear predictive control based on neural multi-models

Maciej Ławryńczuk, Piotr Tatjewski (2010)

International Journal of Applied Mathematics and Computer Science

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This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated....

Input constraints handling in an MPC/feedback linearization scheme

Jiamei Deng, Victor M. Becerra, Richard Stobart (2009)

International Journal of Applied Mathematics and Computer Science

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The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear...

Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks

Zahir Ahmida, Abdelfettah Charef, Victor Becerra (2005)

International Journal of Applied Mathematics and Computer Science

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A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed on-line by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear...

Adaptive control scheme based on the least squares support vector machine network

Tarek A. Mahmoud (2011)

International Journal of Applied Mathematics and Computer Science

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Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support...

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...

Neural network based identification of hysteresis in human meridian systems

Yonghong Tan, Ruili Dong, Hui Chen, Hong He (2012)

International Journal of Applied Mathematics and Computer Science

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Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation...