Multivariable predictive control of a pressurized tank using neural networks

Duarte‐Mermoud, Manuel A.; Suárez-García, María Jesús; Bassi, F.

doi:10.1007/s00521-005-0003-0

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…Recently, neural-network-based adaptive control technique has attracted increasing attentions, because it has provided an efficient and effective way in the control of complex nonlinear or ill-defined systems (Duarte-Mermoud et al, 2005;Hsu et al, 2006;Lin and Hsu, 2003;Lin et al, 1999;Peng et al 2004). The key elements of this success are the approximation capabilities of the neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control

Hsu¹,

Lin²

2008

Recurrent Neural Networks

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Section: Introductionmentioning

confidence: 99%

“…Some of these learning algorithms are based on the backpropagation algorithm. However, these approaches have difficulties to guarantee the stability and robustness of closed-loop system (Duarte-Mermoud et al, 2005;Lin et al, 1999). Another learning algorithms are based on the Lyapunov stability theorem.…”

Section: Introductionmentioning

confidence: 99%

Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control

Hsu¹,

Lin²

2008

Recurrent Neural Networks

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“…Recently, neural-network-based adaptive control technique has attracted increasing attentions, because it has provided an efficient and effective way in the control of complex nonlinear or ill-defined systems [1][2][3][4][5]. The key elements of this success are the approximation capabilities of the neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…Although the neural-network-based adaptive control performances are acceptable in [1][2][3][4][5][6][7][8], the learning algorithm only includes the parameter learning, and they have not considered the structure learning of the neural network. If the number of hidden neurons is chosen too large, the computation load is heavy so that they are not suitable for practical applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive recurrent neural network control using a structure adaptation algorithm

Hsu

2007

Neural Comput & Applic

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This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L 2 control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.

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“…The neural-network-based control technique has been represented an alternative design method for various control systems [10][11][12][13][14]. The successful key point is the approximation ability of neural network, where the neural network can approximate an unknown system dynamics or an ideal controller after learning.…”

Section: Introductionmentioning

confidence: 99%

Design of intelligent power controller for DC–DC converters using CMAC neural network

Hsu

2007

Neural Comput & Applic

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DC-DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC-DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.

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Multivariable predictive control of a pressurized tank using neural networks

Cited by 11 publications

References 29 publications

Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control

Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control

Adaptive recurrent neural network control using a structure adaptation algorithm

Design of intelligent power controller for DC–DC converters using CMAC neural network

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