Decoupling Coordinated Control System Based on PID Neural Network for Marine Steam Power Plant

Xia, Guoqing; Tang, Zhaodong; Wang, Yuanhui; Ren, Liyong

doi:10.1109/csie.2009.632

Cited by 3 publications

(4 citation statements)

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“…Future work should include increasing the complexity of the MSTRSCS mathematical model, making it closer to the real marine steam turbine; investigating the relevant parameters of the ADCMPC algorithm, such as the prediction horizon, the control horizon, output tracking error weighting and control input weighting, improving its optimization space. There is a coupling effect between the marine steam turbine rotational speed control loop and the marine boiler's main steam pressure control loop [1]. The main steam pressure of the marine boiler is time-varying.…”

Section: Discussionmentioning

confidence: 99%

“…The steam turbine is one of the most important power conversion devices in the marine power plant [1]. It is a rotary prime mover that converts the thermal energy of the steam into mechanical energy.…”

Section: Marine Steam Turbine Rotational Control Modelingmentioning

confidence: 99%

“…The pressurized marine steam power plant is the key to ensuring the completion of shipping and navigation tasks [1]. MSTRSCS is the main form of marine power equipment.…”

Section: Introductionmentioning

confidence: 99%

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A Novel MPC with Actuator Dynamic Compensation for the Marine Steam Turbine Rotational Control with a Novel Energy Dynamic Model

Liu¹,

Zhao²,

Wu³

2019

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The conventional modeling method of the marine steam turbine rotational speed control system (MSTRSCS) is based on Newton’s second law, constructing the mechanical equations between the rotational acceleration and the resultant torque. The disadvantages of this are nonlinearity, a complex structure and an infinite point of discontinuity in the rotational acceleration when the rotational speed is close to 0. Taking the kinetic energy of MSTRSCS as the output variable by using the kinetic energy theorem in this paper, we convert the complex nonlinear model of MSTRSCS into a linear one, since kinetic energy and rotational speed are homeomorphic. Model predictive control (MPC) adopts a discrete-time model, whereas the real system is time-continuous. Hence, poor performance is obtained in the real system when the time-discrete control law is applied to the MSTRSCS through the actuator. In case of high requirements for system accuracy and control performance, conventional MPC (CMPC) cannot meet the engineering requirements. In order to lessen the impact of this phenomenon, this paper proposes a novel MPC with actuator dynamic compensation (ADCMPC), in which the dynamics of the actuator are quantified and the system performance is improved. Compared with other control techniques such as CMPC, the performance of the ADCMPC strategy in MSTRSCS is successfully validated.

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Section: Discussionmentioning

confidence: 99%

Section: Marine Steam Turbine Rotational Control Modelingmentioning

confidence: 99%

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A Novel MPC with Actuator Dynamic Compensation for the Marine Steam Turbine Rotational Control with a Novel Energy Dynamic Model

Liu¹,

Zhao²,

Wu³

2019

Processes

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“…ANN has the virtue of stronger robustness, swift response, and minimal steady-stable error. The performance of aeroengine and marine diesel engine has been studied, and the result showed that control structure of NN owned a variety of virtue [2,3,4] ,in addition, the paper [5,6,7] has discussed respectively NN performance of the virtual instrument, hydro-generator system, and marine steam power plant. This paper adopts a dynamic recursive NN-Elman NN as identifier to identify the physical model of AFR in automotive engine, which is more fitted for nonlinear plant identification than common feedback NN with higher flexibility of feedback.…”

Section: Introductionmentioning

confidence: 99%

Research in Elman Neural Network for AFR Model of Automotive Engine

2011

AMR

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Air fuel ratio is a key index affecting power performance and fuel economy and exhaust emissions of the gasoline engine, whose accurate model is the foundation of accuracy air fuel ratio control. In the paper, at first, it has studied the Elman neural network (NN) simulation model of Air Fuel ratio physical model of automotive engine. Second, employing the SI-V8 in en-DYNA engine model as experimental device, the paper discussed the structure determination of Elman neural network; finally, it compared model identification performance between Elman and BP neural network. Experiment results show the generalization performance of neural network does not have a linear relationship to the neurons in hidden layer of Elman NN, and the air fuel ratio based on Elman neural network is better than the air fuel ratio model based on BP neural network. The average relative error of Elman NN air fuel ratio model is less than 0.5%, however, which of BP NN is more than 1%.

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