A L1-LEMPC hierarchical control structure for economic load-tracking of super-critical power plants.

The super-critical thermal power plants are undertaking more and more responsibilities for the balance of the power grid intermittency of the renewable energies. However, the frequent wide-range load regulation may deteriorate the operational efficiency of the power plant. To this end, a hierarchical control structure with two layers is proposed in this paper.

Iterative tuning of modified uncertainty and disturbance estimator for time-delay processes: A data-driven approach.

Uncertainty and disturbance widely exist in the process industry, which may deteriorate control performance if not well handled. The uncertainty and disturbance estimator (UDE) emerges as a promising solution by treating the external disturbances and internal uncertainties simultaneously as a lumped term. To overcome its limitation caused by time delay, a modified UDE (MUDE) has been proposed recently.

Preference adjustable multi-objective NMPC: An unreachable prioritized point tracking method.

This paper proposes a new preference adjustable multi-objective model predictive control (PA-MOMPC) law for constrained nonlinear systems. With this control law, a reasonable prioritized optimal solution can be directly derived without constructing the Pareto front by solving a minimal optimization problem, which is a novel development of recently proposed utopia tracking approaches by additionally considering objective preferences with more flexible terminal and stability constraints. The tracking point of the proposed PA-MOMPC law is represented by a parametric vector with the parameters adjustable on the basis of objective preferences.

Fuzzy modeling and predictive control of superheater steam temperature for power plant.

This paper develops a stable fuzzy model predictive controller (SFMPC) to solve the superheater steam temperature (SST) control problem in a power plant. First, a data-driven Takagi-Sugeno (TS) fuzzy model is developed to approximate the behavior of the SST control system using the subspace identification (SID) method. Then, an SFMPC for output regulation is designed based on the TS-fuzzy model to regulate the SST while guaranteeing the input-to-state stability under the input constraints.

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters.