2011
DOI: 10.1109/tns.2011.2142192
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Saturated Output Feedback Dissipation Steam Temperature Control for the OTSG of MHTGRs

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Cited by 16 publications
(9 citation statements)
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“…There have been many process control system design approaches such as the relative gain analysis-(RGA-) based method. In this paper, both the feedback loops and control laws of HTR-PM control system are designed by the use of the physicsbased NSSS control method proposed in [30][31][32][33][34][35][36] and module coordination control method proposed in [28,29], which provide the globally asymptotic closed-loop stability through guaranteeing the convergence of Lyapunov functions determined by the shifted-ectropies of neutron kinetics and thermodynamics as well as the kinetic energy stored in secondary-loop FFN.…”
Section: Control Design Methodmentioning
confidence: 99%
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“…There have been many process control system design approaches such as the relative gain analysis-(RGA-) based method. In this paper, both the feedback loops and control laws of HTR-PM control system are designed by the use of the physicsbased NSSS control method proposed in [30][31][32][33][34][35][36] and module coordination control method proposed in [28,29], which provide the globally asymptotic closed-loop stability through guaranteeing the convergence of Lyapunov functions determined by the shifted-ectropies of neutron kinetics and thermodynamics as well as the kinetic energy stored in secondary-loop FFN.…”
Section: Control Design Methodmentioning
confidence: 99%
“…, 5 and < ) is the length between points and in Figure 4, and s , s , ℎ s s , and s are, respectively, the flow density (kg/m 3 ), flowrate (kg/s), enthalpy per unit mass (J/kg), heat flux from the primary side, and cross section (m 2 ) of node of the secondary side. Here, 13 is the length of the subcooled section, 35 is that of the boiling section, and ℎ 1s is specific enthalpy of the feedwater. Due to the high speed of the steam flow, it is assumed that…”
Section: Secondary Sidementioning
confidence: 99%
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“…The physics-based control (PBC) method is also an effective way to design nonlinear reactor control laws by retaining or strengthening stable subdynamics and by cancelling or suppressing unstable subdynamics, which has been applied to the load-following control design for the PWRs (Pressurized Water Reactors) [11][12][13] and MHTGRs [14,15] recently. From the aspect of SG controller design, some advanced control approaches such as the model predictive control (MPC) [16], neurofuzzy [17] and feedback-dissipation [18] methods have all been applied to improve the regulation performance of SGs.…”
Section: Introductionmentioning
confidence: 99%
“…However, these control laws have the drawback of heavy model-reliance. Some control laws needs the state prediction or observation directly based upon the reactor or SG models [9][10][11][12][14][15][16][17], and the control parameters also vary with the reactor or SG physical and thermal-hydraulic parameters [9][10][11][12][13][14][15][16][17][18], which leads to the difficulty in practically tuning and implementation. Therefore, it is necessary to develop model-free control methods to relieve the heavy model reliance of the designed control strategies.…”
Section: Introductionmentioning
confidence: 99%