Yiguo Li

Offset-free fuzzy model predictive control of a boiler–turbine system based on genetic algorithm

Abstract This paper presents a model predictive control (MPC) strategy based on genetic algorithm to solve the boiler–turbine control problem. First, a Takagi–Sugeno (TS) fuzzy model based on gap values is established to approximate the behavior of the boiler–turbine system, then a specially designed genetic algorithm (GA) is employed to solve the resulting constrained MPC problem. A terminal cost is added into the standard performance index so that a short prediction horizon can be adopted to effectively decrease the on-line computational burden. Moreover, the GA is accelerated by improving the initial population based on the optimal control sequence obtained at the previous sampling period and a local fuzzy linear quadratic (LQ) controller. Simulation results on a boiler–turbine system illustrate that a satisfactory closed-loop performance with offset-free property can be achieved by using the proposed method.

Robust H∞ tracking control of boiler–turbine systems

In this paper, the problem of designing a fuzzy H(infinity) state feedback tracking control of a boiler-turbine is solved. First, the Takagi and Sugeno fuzzy model is used to model a boiler-turbine system. Next, based on the Takagi and Sugeno fuzzy model, sufficient conditions for the existence of a fuzzy H(infinity) nonlinear state feedback tracking control are derived in terms of linear matrix inequalities. The advantage of the proposed tracking control design is that it does not involve feedback linearization technique and complicated adaptive scheme. An industrial boiler-turbine system is used to illustrate the effectiveness of the proposed design as compared with a linearized approach.

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

This paper develops a novel data-driven modeling strategy and predictive controller for boiler-turbine unit using subspace identification and multimodel method. To deal with the nonlinear behavior of boiler-turbine unit, the system is divided into a number of local regions following the analysis of the nonlinearity distribution along the operation range, and then the corresponding measurement data are organized to identify the local models through the subspace method. By transforming local models into the same basis, the resulting multimodel system (MMS) is shown to represent the boiler-turbine unit very closely, and thus, used in designing a multimodel-based model predictive control (MMPC). As an alternative approach, a data-driven direct predictive controller (DDPC) is developed by utilizing the intermediate subspace matrices as local predictors. Online update of the predictor is also implemented on the multimodel structure to make the controller responsive to plant behavior variations. Simulation results demonstrate the feasibility and effectiveness of the proposed approach.

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.

Stable model predictive control based on TS fuzzy model with application to boiler-turbine coordinated system

In this paper, we propose a stable fuzzy model predictive controller based on extended-fuzzy Lyapunov function. The main idea of the proposed approach is to design a free control variable and a non-parallel distributed compensation control law in such a way that an extended-fuzzy Lyapunov function is constructed with minimizing the upper bound of the infinite horizon objective function in the fuzzy model predictive control. Therefore, the predictive controller can guarantee both the stability of the closed-loop fuzzy model predictive control system and input constraints while obtaining the optimal transient performance. It is shown that the controller is obtained by solving a set of linear matrix inequalities. The extended-fuzzy Lyapunov function reduces the conservatism of common Lyapunov function and fuzzy Lyapunov function, and it also enlarges the feasible area of the predictive controller. Moreover, appropriate slack and collection matrices are used in all linear matrix inequalities, which can further reduce the conservatism. The simulations on a numerical example and a nonlinear boiler-turbine coordinated system demonstrate the advantage and effectiveness of the proposed approach.

Stable Model Predictive Tracking Control for Boiler-Turbine Coordinated Control System

Abstract In this paper, a stable model predictive tracking controller (SMPTC) is designed based on the piecewise linear model to control a boiler-turbine system for a wide-range operation with unmeasured state and significant disturbance or modeling mismatch. It is shown that the proposed controller is constructed by the combination of a state and disturbance observer, a steady-state targets calculator as well as a stable model predictive controller, which can be obtained efficiently by solving a set of linear matrix inequalities. The main advantage of the proposed SMPTC is that it can guarantee the stability of the closed-loop model predictive control system with input constraints, while obtaining an offset-free tracking performance in an optimal way. Simulation results demonstrate the advantage and effectiveness of the proposed controller.

Control of boiler-turbine coordinated system using multiple-model predictive approach

We study the control of a boiler-turbine coordinated system using multiple-model predictive strategy which integrates constrained model predictive algorithm with stability assurance into multiple-model approach based on piecewise linearization. The control algorithm is a receding horizon scheme with a quasi-infinite horizon objective function and uses multiple linear models in its predictions. Simulation shows that the boiler-turbine coordinated system can be successfully controlled by this methodology.

Nonlinear model predictive control of ultra-supercritical once through boiler-turbine unit

The coordinated control system (CCS) plays an import role in the operation of ultra-supercritical once-through boiler-turbine unit. To overcome the operating issues of the CCS, such as multivariable coupling, nonlinearity and large-time delay, a collocation method based nonlinear model predictive control is proposed in this paper. The dynamic optimization problem is transcribed into a finite dimensional nonlinear programming problem and solved using interior point-based large scale nonlinear optimization algorithm. Simulation results on a 1000MW ultra-supercritical once-through boiler-turbine unit show the effectiveness of the proposed approach.

Nonlinear dynamic analysis and control design of a solvent-based post-combustion CO 2 capture process

Abstract A flexible operation of the solvent-based post-combustion CO2 capture (PCC) process is of great importance to make the technology widely used in the power industry. However, in case of a wide range of operation, the presence of process nonlinearity may degrade the performance of the pre-designed linear controller. This paper gives a comprehensive analysis of the dynamic behavior and nonlinearity distribution of the PCC process. Three cases are taken into account during the investigation: 1) capture rate change; 2) flue gas flowrate change; and 3) re-boiler temperature change. The investigations show that the CO2 capture process does have strong nonlinearity; however, by selecting a suitable control target and operating range, a single linear controller is possible to control the capture system within this range. Based on the analysis results, a linear model predictive controller is designed for the CO2 capture process. Simulations of the designed controller on an MEA based PCC plant demonstrate the effectiveness of the proposed control approach.

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.