Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system

Iterative Learning Control Integrated with Model Predictive Control for Real-Time Disturbance Rejection of Batch Processes

“…In most studies of ILMPC, the state vector consists of the entire error sequences of a batch (Lee et al, 2000;Xiong et al, 2005;Liu and Kong, 2013), and a prediction horizon is fixed as an entire batch horizon; therefore, the control calculation might not be performed within a sample time. Several studies combine a time-wise feedback controller and batchwise feed-forward controller separately (Chin et al, 2004;Lu et al, 2015).…”

Multivariable robust adaptive sliding mode control of an industrial boiler–turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller

“…Also, other methods including cascade control of superheated steam temperature with neuro-PID controller [28], predictive control via fuzzy clustering and subspace methods [29], nonlinear genetic algorithm (GA) [30], data-driven modeling [31] and local model networks [32] have been investigated.…”

Fuzzy modeling and stable model predictive tracking control of large-scale power plants

“…Therefore, power plants present a challenging control problem owing to the behaviors such as: severe nonlinearity over a wide operation range, tight operating constraints, strong coupling among the multitude of variables, unknown disturbances and plant parameter variations. Consequently, the conventional PI/PID based controllers are no longer sufficient in meeting performance specifications, even if they are well tuned at a given load level; thus, various control strategies have been extensively studied [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

“…To overcome these issues, the fuzzy modeling technique [21], which uses a combination of several linear models to approximate the nonlinear behavior of the plant, has been widely used in controller design for a wide range power plant operation [14][15][16][17][18][19], resulting in better performance than the conventional MPC methods. In [14][15][16][17][18], various kinds of MPCs are proposed on the Takagi-Sugeno (TS) fuzzy model for the boiler-turbine coordinated system, utilizing different computational algorithms, such as linear matrix inequalities (LMIs) [14], genetic algorithm (GA) [15,17], quadratic programming (QP) [16], and iterative learning [18].…”

“…In [14][15][16][17][18], various kinds of MPCs are proposed on the Takagi-Sugeno (TS) fuzzy model for the boiler-turbine coordinated system, utilizing different computational algorithms, such as linear matrix inequalities (LMIs) [14], genetic algorithm (GA) [15,17], quadratic programming (QP) [16], and iterative learning [18]. In [19], a generalized predictive controller (GPC) is designed on the basis of neuro-fuzzy network model to control the steam temperature system of a 200 MW power plant.…”