Jiong Shen

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.

Guaranteed cost nonlinear tracking control of a boiler-turbine unit: an LMI approach

This article addresses the problem of designing a guaranteed cost nonlinear state feedback tracking control for a boiler-turbine unit. First, the nonlinear boiler-turbine is re-expressed as a linear system with norm bounded uncertainties via a nonlinear transformation function. Then, based on this linear model a sufficient condition for the existence of a guaranteed cost nonlinear state feedback tracking control is derived in terms of linear matrix inequalities. The advantage of the proposed tracking control design is that only a simple nonlinear controller is constructed and it does not involve feedback linearisation technique and complicated adaptive or fuzzy schemes. An industrial boiler-turbine system is used to illustrate the effectiveness of the proposed design as compared with a linearised approach.

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.

L 1 adaptive control for improving load-following capability of nonlinear boiler–turbine units in the presence of unknown uncertainties

Abstract Uncertain operating conditions, along with the load demand for a wider and faster response, bring new challenges to the nonlinear boiler–turbine unit control. In order to achieve safe and efficient operations, this paper proposes a novel L 1 adaptive state feedback controller for the multivariable nonlinear boiler–turbine systems with unknown uncertainties. This L 1 adaptive control approach can achieve arbitrarily close tracking of the reference signal while ensuring closed-loop stability in the presence of strong nonlinearities, internal un-modeled dynamics, time-varying unknown parameters and uncertain dead time within its time-delay margins. In this study, a boiler–turbine unit simulation model is first built in order to verify the algorithm; then the L 1 adaptive control approach is presented followed by its main results and proof; only based on one group of the equilibrium working point data, the L 1 adaptive controller is designed for the overall working range of the boiler–turbine unit. For a more reliable evaluation on the L 1 adaptive controller, the offset-free input-to-state stable fuzzy model predictive controller (OFISS-MPC) – which has all-sided performances – is briefly introduced and functions as a reference for our results. Finally, the L 1 adaptive controller is tested in a series of challenging simulation scenarios and then compared with the OFISS-MPC controller. The results validate the expected performances of the L 1 adaptive controller for the boiler–turbine unit with unknown uncertainties.

Coordinated Control Strategies for SOFC Power Plant in a Microgrid

Solid-oxide fuel cell (SOFC) power plant plays a vital role in a hybrid alternative energy based microgrid due to its reliability and flexibility in power supply. However, the control of SOFC is challenging in terms of providing a fast load tracking while maintaining the fuel utilization rate within a safe range. To this end, this paper builds two basic coordinated control strategies (CCS) for power management of SOFC-based microgrid. The first is “Fuel Cell follows Inverter” scheme, where fast tracking is preferred while SOFC may operate on the edge of the safety range. The second is “Inverter follows Fuel Cell” scheme, where high security is guaranteed by sacrificing performance in load tracking. To obtain a robust and simple scheme, energy balance principle is used in CCS such that PI controller is sufficient to fulfill the basic duties. Moreover, a simple supervisory control strategy is proposed for microgrid to provide a reasonable power reference for SOFC. The control system is designed and tuned based on an SOFC plant with inverters average model. The efficiency of the proposed strategies is validated via a grid-connected SOFC/photovoltaic microgrid.

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.