Neural Generalized Predictive Control for Industrial Processes

Chidrawar, Sadhana; Patre, B. M.; LaxmanWaghmare,

doi:10.5772/7905

Cited by 3 publications

(1 citation statement)

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“…Those neural network based adaptive control system is called neural network predictive control (NNPC). It is widely used as a controller for many nonlinear systems [10,11,12,13,14]. In this work, a neural network predictive controller is designed for controlling the liquid level of the coupled tank system.…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

Aldair¹

2014

AJEEE

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In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant to predict future plant performance. The simulation results are compared with PID control. The results show that the effectiveness of using the neural predictive controller for the coupled tank system. The Simulink Toolbox in MATLAB has been used to simulate the controlled system with the proposed controller. The VHDL has been used to describe the implementation of neural controller. Xilinx ISE Project Navigator Version 10.1 is used to obtain the compilation and timing test results as well as the synthesized design. The hardware implementation of the neural network predictive controller using FPGA board is proposed. To make sure that the FPGA board works like the simulated neural predictive controller, MATLAB programme is used to compare between the set of the data that are obtained from the ModelSim program and the set of the data that are obtained from the MATLAB Simulink model. Simulation results show that the FPGA board can be used as neural predictive controller for controlling the liquid level of the coupled tank system.

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Section: Introductionmentioning

confidence: 99%

Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

Aldair¹

2014

AJEEE

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Hybrid Neural Predictive-Fuzzy Controller for Motorized Robot Arm

Zada

Guirguis

Sead

2011

2011 International Symposium on Computer Science and Society

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Development and Validation of Control Systems with Variable Dead Time for Networked Control Systems

Ouadoudi,

Schwarz,

Börcsök

2024

Wseas Transactions on Systems and Control

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Dead times can manifest in various control systems, posing a challenge due to their limiting effect on the maximum allowable gain required for system stability. Consequently, researchers have developed various structure-based controllers to enhance control performance or mitigate the strong influences of the dead time component. Among these structures are the Smith Predictor, Generalized Predictive Controller (GPC), and fractional PID controllers. With the significant advancements in networking and communication technology, the application of networked control has gained importance. However, due to the diverse network characteristics, additional variable delays are challenging to avoid. Modern control engineering offers methods capable of significantly improving control performance, considering both theoretical and practical aspects.

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Neural Generalized Predictive Control for Industrial Processes

Cited by 3 publications

References 0 publications

Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

Hybrid Neural Predictive-Fuzzy Controller for Motorized Robot Arm

Development and Validation of Control Systems with Variable Dead Time for Networked Control Systems

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