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. An economic model predictive controller using a locally linearized model of the plant (LEMPC) is employed in the upper layer to realize an optimal load tracking. A L1 adaptive controller in the lower layer forces the plant to track the optimal trajectory by estimating and compensating the lumped uncertainty between the real plant and the linear model. The tracking performance is theoretically proved to reach the desired transient process. The proposed hierarchical control architecture is validated through simulations on a simplified 1000MW super-critical boiler-turbine unit model with comparison to the other two conventional real-time optimization control approaches. The results show that the proposed L1-LEMPC control system produces better load tracking performance than the other two conventional control strategies with higher operational economy efficiency. Moreover, under the environment of severe external disturbance and parameter perturbation, the proposed control system still maintains satisfactory performance.

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. However, its parameter tuning relies heavily on trial-and-error, thus being time-consuming in balancing the robustness and performance. To this end, this paper aims to develop an automatic tuning procedure for the MUDE-based control system. The quantitative relationship between system performance and the scaled parameters is empirically built. Iterative Feedback Tuning (IFT) is utilized to approximate the nominal model towards actual process. Through the empirical formula and optimized model, an automatic design procedure is proposed after taking into account the system robustness and output performance simultaneously. Simulation results show the superiority of the closed-loop performance over the original MUDE controllers. The experimental results validate the feasibility of the method proposed in this paper, depicting a promising prospect in the practical application.

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. The main result of this paper is that the solution obtained through the proposed PA-MOMPC law is demonstrated to have two important properties. One is the inherent Pareto optimality, and the other is the priority consistency between the solution and the tuning parametric vector. This combination makes the objective priorities tuning process transparent and efficient. The proposed PA-MOMPC law is supported by feasibility analyses, proof of nominal stability, and a numerical case study.

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. The effect of modeling mismatches and unknown plant behavior variations are overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an offset-free tracking of SST can be achieved over a wide range of load variation.

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. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.