Neural-network-based d-step-ahead predictors for nonlinear systems with time delay

Tan, Yonghong; Cauwenberghe, Achiel Van

doi:10.1016/s0952-1976(98)00043-8

Cited by 98 publications

(45 citation statements)

References 9 publications

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“…The time delay is a known function that depends of the water flow wf(k). The incorrect prediction of the time delay may lead to aggressive control if the time delay is under estimated or conservative control if the time delay is over estimated (Tan & Nazmul Karim, 2002), (Tan & Cauwenberghe, 1999).…”

Section: Frontiers Of Model Predictive Control 154mentioning

confidence: 99%

Adaptable PID Versus Smith Predictive Control Applied to an Electric Water Heater System

Vieira¹,

Mota²

2012

Frontiers of Model Predictive Control

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Section: Frontiers Of Model Predictive Control 154mentioning

confidence: 99%

Adaptable PID Versus Smith Predictive Control Applied to an Electric Water Heater System

Vieira¹,

Mota²

2012

Frontiers of Model Predictive Control

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“…A back-propagation method with the Levenberg-Marqudatt (LM) algorithm has been widely applied for training ANN. The LM algorithm is widely applied to many different domains and is faster and produces better results than other training methods (Hagan and Menhaj, 1994;Tan and van Cauwenberghe, 1999). To update weights and biases, the LM algorithm uses an approximation to the Hessian matrix.…”

Section: Artificial Neural Network: Annmentioning

confidence: 99%

Square Surface Aerator: Process Modeling and Parameter Optimization

Rao

Kumar²

2007

J ENV INFORM

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ABSTRACT. The performance of an aeration system generally depends upon the geometric and dynamic parameters. The main purpose of doing experiment in the area of surface aeration system is to interpret the laboratory results for the field application that is to scale-up the results. This requires a geometrical similarity conditions. Finding geometrical optimal conditions of a surface aeration system through experiments involves physical constraints and classically parameters can be optimized by varying one variable at a time and keeping others as constants. In the real experimental process, it is not possible to vary all others geometric parameters simultaneously. In such a case, the model of the system is built through computer simulation, assuming that the model will result in adequate determination of the optimum conditions for the real system. In this paper, two approaches have been used to model the phenomena: i) Multiple regression and ii) Neural network. It has been found that neural network approach is showing better predictability compared to the multiple regression approach. In process of optimization, the pertinent dynamic parameter is divided into a finite number of segments over the entire range of observations. For each segment of the dynamic parameter, the neural network model is optimized for the geometrical parameters spanning over the entire range of observations. Thus each segment of the dynamic parameter has its set of optimal geometrical conditions. Results obtained are having less variation among them and they are very nearer to the experimental optimal conditions. Input parameter significance test of neural network model reveals that, in general the blade width of rotor is a major geometric parameter to enhance the aeration process.

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“…The incorrect prediction of the time delay may lead to aggressive control if the time delay is under estimated or conservative control if the time delay is over estimated [13]- [14]. The physical inverse model is mathematically calculated based in the physical direct model presented in section II used with out time delay.…”

Section: Electric W Ater Heatermentioning

confidence: 99%

Passing from a Gas to an Electric Water Heater System: Adaptive PID Versus Smith Predictive Control

Vieira¹,

Mota

2007

Intelligent Engineering Systems, 2007 International Conference On

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Abstract-This paper presents the control results of an electric water heater system using two approaches: adaptable proportional integral derivative and Smith predictive control based in the physical internal model control structure. The electric water heater was modelled with two variable blocks connected in series: a first order system and a time delay. In fact, the gain, the time constant and the time delay of the system change linearly with the water that flows in the permutation chamber. The physical model of the electric water heater system was retched based in energy dynamic equations and validated with open loop data of the system in a similar way that was made in a previews study about modelling and controlling a gas water heater. The two different control algorithms explored are the adaptive proportional integral derivative (APID) and the Smith predictive control (SPC) based in the internal physical model control algorithm. The first approach has some problems dealing with the time constant and the time delay variations of the system. This solution can control the overshoot for all different water flows but the time constant of the close loop systems changes with the water flow. The APID does not deal well with water flow variations. The second approach is more adequate to control this kind of systems (first order system followed by a time delay that changes in time). The SPC loop is indicated for control time delay systems and with the à priori knowledge of the physical model we can achieve a very good control result. Finally, these two algorithms are applied in controlling the system and the results are compared using the mean square error criterion.Index terms-adaptive PID, electrical water heater, physical model identification, Smith predictive control and time delay system.

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Neural-network-based d-step-ahead predictors for nonlinear systems with time delay

Cited by 98 publications

References 9 publications

Adaptable PID Versus Smith Predictive Control Applied to an Electric Water Heater System

Adaptable PID Versus Smith Predictive Control Applied to an Electric Water Heater System

Square Surface Aerator: Process Modeling and Parameter Optimization

Passing from a Gas to an Electric Water Heater System: Adaptive PID Versus Smith Predictive Control

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