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Optimal scheduling strategy of integrated energy systems (IES) with combined cooling, heating and power (CCHP) has become increasingly important. In order to make the scheduling strategy fit to the practical implementation, this paper proposes a variable performance parameters temperature–flowrate scheduling model for IES with CCHP. The novel scheduling model is established by taking flowrate and temperature as decision variables directly. In addition, performance parameters are treated as variables rather than constants in the proposed model. Specifically, the efficiencies of the gas turbine and the waste heating boiler are estimated with the partial load factor, and the coefficient of performance (COP) of the electrical chillers and heat pumps are estimated with the partial load factor and outlet water temperature. Then, to deal with the model nonlinearities caused by considering the variability of COPs, the COP-expansion method is developed by adopting a specific representation of the COP and the expansion of the outlet water temperature. Finally, case studies show that the variable performance parameters’ temperature–flowrate scheduling model can account for the variation of performance parameters, especially the impacts of water temperature and the part load factor on the COP. Therefore, the proposed scheduling model can obtain more adequate and feasible operation strategy, thereby suggesting its applicability in engineering practice.
Hong-Hai Niu; Yang Zhao; Shang-Shang Wei; Yi-Guo Li. A Variable Performance Parameters Temperature–Flowrate Scheduling Model for Integrated Energy Systems. Energies 2021, 14, 5400 .
AMA StyleHong-Hai Niu, Yang Zhao, Shang-Shang Wei, Yi-Guo Li. A Variable Performance Parameters Temperature–Flowrate Scheduling Model for Integrated Energy Systems. Energies. 2021; 14 (17):5400.
Chicago/Turabian StyleHong-Hai Niu; Yang Zhao; Shang-Shang Wei; Yi-Guo Li. 2021. "A Variable Performance Parameters Temperature–Flowrate Scheduling Model for Integrated Energy Systems." Energies 14, no. 17: 5400.
Economy, robustness and computational efficiency are of paramount metrics for an operation strategy of an integrated energy system (IES). To achieve the trade-off of the three metrics, a multi-layer framework is extensively exploited in existing operation strategies. This work, however, proposes a single-layer multi-timescale framework which can coordinate different operation performances associated with various timescales simultaneously. Based on the framework, an improved stochastic model predictive control (SMPC) operation strategy is further developed by embedding the proposed framework into its prediction horizon. To solve the multi-timescale optimization of the improved SMPC, the constraints and cost function are presented in the multi-timescale form, and the supplied and demands are forecast by the least square support vector machine. A simulator of an IES is thereafter constructed to mimic real system and used to evaluate the performance of the proposed strategy by operation cost, accumulative error and computation time with respect to economy, robustness and computational efficiency, respectively. Finally, the improved SMPC strategy is compared with a traditional single-layer and a hierarchical strategy by a case study. The results show that the improved strategy has the best tradeoff performance aforementioned. The multi-timescale framework can be also integrated into other operation strategies.
Shangshang Wei; Xianhua Gao; Yi Zhang; Yiguo Li; Jiong Shen; Zuyi Li. An improved stochastic model predictive control operation strategy of integrated energy system based on a single-layer multi-timescale framework. Energy 2021, 235, 121320 .
AMA StyleShangshang Wei, Xianhua Gao, Yi Zhang, Yiguo Li, Jiong Shen, Zuyi Li. An improved stochastic model predictive control operation strategy of integrated energy system based on a single-layer multi-timescale framework. Energy. 2021; 235 ():121320.
Chicago/Turabian StyleShangshang Wei; Xianhua Gao; Yi Zhang; Yiguo Li; Jiong Shen; Zuyi Li. 2021. "An improved stochastic model predictive control operation strategy of integrated energy system based on a single-layer multi-timescale framework." Energy 235, no. : 121320.
Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.
Honghai Niu; Yu Yang; Lingchao Zeng; Yiguo Li. ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power. Energies 2021, 14, 701 .
AMA StyleHonghai Niu, Yu Yang, Lingchao Zeng, Yiguo Li. ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power. Energies. 2021; 14 (3):701.
Chicago/Turabian StyleHonghai Niu; Yu Yang; Lingchao Zeng; Yiguo Li. 2021. "ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power." Energies 14, no. 3: 701.
A scheduling model is a prerequisite for an operation strategy of integrated energy system (IES). Existing scheduling models of IES, however, are typically based on heat‐transfer variables either completely or partially, which oversimplify detailed thermal characteristics. To this end, a novel scheduling model is proposed where all thermal processes are modeled by temperature and flowrate of working fluids. This improvement renders the capability to the scheduling model to incorporate different thermal processes. Furthermore, the nonlinear product terms of temperature and flowrate in the proposed model are linearized by the binary expansion method. Based on the linearized scheduling model, a stochastic model predictive control (SMPC) operation strategy is exploited to optimize the economic performance by energy forecast, scenario reduction, rolling optimization, and feedback correction. Afterwards, four operation modes considering different temperature changes of the devices, networks, and the environment are performed and compared. The results found that thermal characteristics will affect device operation results and the degree of influence varies. The network temperature changes have the broadest influence, followed by the device and the ambient temperature changes. Moreover, system operation costs are also affected by detailed thermal characteristics. The total cost, the gas cost, and the electricity cost under Mode 2 are almost the same to those of Mode 1. However, the first two costs are reduced by 3.4% and 5.3% under Mode 3, and are reduced by 2.7% and 4% under Mode 4, despite that the electricity cost increases by 0.2% under Mode 3 and remains almost the same under Mode 4. These indicate that reliability and economy of an IES are affected by thermal characteristics, and it is thus the necessity to consider detailed thermal characteristics in an operation. Moreover, the results demonstrate the capability of the generalized temperature‐flowrate based scheduling model and the effectiveness of the SMPC operation strategy.
Shangshang Wei; Yiguo Li; Li Sun; Junli Zhang; Jiong Shen; Zuyi Li. Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics. International Journal of Energy Research 2020, 45, 4081 -4097.
AMA StyleShangshang Wei, Yiguo Li, Li Sun, Junli Zhang, Jiong Shen, Zuyi Li. Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics. International Journal of Energy Research. 2020; 45 (3):4081-4097.
Chicago/Turabian StyleShangshang Wei; Yiguo Li; Li Sun; Junli Zhang; Jiong Shen; Zuyi Li. 2020. "Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics." International Journal of Energy Research 45, no. 3: 4081-4097.
An analytical dynamic model can deepen our understanding of system characteristics. However, no system-scale dynamic model of integrated parabolic trough concentrating solar power (CSP) plants currently exists due to lack of a dynamic model of heat exchanger trains and proper integration of different subsystems. To solve the problems, this work developed a new analytical dynamic model of heat exchanger trains. Furthermore, a simplified analytical model of an integrated parabolic trough CSP consisting of a parabolic trough solar field, thermal energy storage, and power block subsystem is first proposed here. To demonstrate the fidelity of the integrated model, the simulation presented here was compared with the simulation built on a software platform and the corresponding measured data from the Andasol II plant. All the validation results of a steady-state case and two dynamic cases prove that the proposed model can capture the system dominant processes with satisfactory accuracy and computational efficiency. Considering the advantages of reliability, simplicity, and robustness, the integrated model can be applied to design and test system controllers of CSP plants, and various other areas related to the CSP.
Shangshang Wei; Xiufan Liang; Taimoor Mohsin; Xiao Wu; Yiguo Li. A simplified dynamic model of integrated parabolic trough concentrating solar power plants: Modeling and validation. Applied Thermal Engineering 2020, 169, 114982 .
AMA StyleShangshang Wei, Xiufan Liang, Taimoor Mohsin, Xiao Wu, Yiguo Li. A simplified dynamic model of integrated parabolic trough concentrating solar power plants: Modeling and validation. Applied Thermal Engineering. 2020; 169 ():114982.
Chicago/Turabian StyleShangshang Wei; Xiufan Liang; Taimoor Mohsin; Xiao Wu; Yiguo Li. 2020. "A simplified dynamic model of integrated parabolic trough concentrating solar power plants: Modeling and validation." Applied Thermal Engineering 169, no. : 114982.
Solid oxide fuel cell (SOFC) is of great importance to renewable energy generation system. In practice its output voltage should be held constant and fuel utilization rate should be guaranteed in a reasonable range respectively when the resistance load varies over a large area. In order to overcome the issues in practice, a fuzzy model predictive control with zone tracking for a SOFC power generation system is proposed. The nonlinearity and multivariable coupling are mitigated by fuzzy model and predictive control approaches respectively. The feedforward compensation is adopted to improve with the dynamic response. Zone control is integrated with fuzzy model predictive control for the purposes of satisfying fuel utilization within a desired range. A performance index with a weight function is developed to optimize controlled variables trajectory in the desired range so that the undulations of the controlled variables can be alleviated within the range. The advantages of the proposed method are manifested by simulations.
Long Wu; Xiao Wu; Lei Pan; Jiong Shen; Yiguo Li; Junli Zhang. Fuzzy Model Predictive Control of Solid Oxide Fuel Cell with Zone Tracking. IFAC-PapersOnLine 2019, 52, 210 -215.
AMA StyleLong Wu, Xiao Wu, Lei Pan, Jiong Shen, Yiguo Li, Junli Zhang. Fuzzy Model Predictive Control of Solid Oxide Fuel Cell with Zone Tracking. IFAC-PapersOnLine. 2019; 52 (4):210-215.
Chicago/Turabian StyleLong Wu; Xiao Wu; Lei Pan; Jiong Shen; Yiguo Li; Junli Zhang. 2019. "Fuzzy Model Predictive Control of Solid Oxide Fuel Cell with Zone Tracking." IFAC-PapersOnLine 52, no. 4: 210-215.
Concentrating solar power (CSP) is a promising technology for exploiting solar energy. A major advantage of CSP plants lies in their capability of integrating with thermal energy storage; hence, they can have a similar operability to that of fossil-fired power plants, i.e., their power output can be adjusted as required. For this reason, the power output of such CSP plants is generally scheduled to maximize the operating revenue by participating in electric markets, which can result in frequent changes in the power reference signal and introduces challenges to real-time power tracking. To address this issue, this paper systematically studies the execution-level power tracking control strategy of an CSP plant, primarily aiming at coordinating the control of the sluggish steam generator (including the economizer, the boiler, and the superheater) and the fast steam turbine. The governing equations of the key energy conversion processes in the CSP plant are first presented and used as the simulation platform. Then, the transient behavior of the CSP plant is analyzed to gain an insight into the system dynamic characteristics and control difficulties. Then, based on the step-response data, the transfer functions of the CSP plant are identified, which form the prediction model of the model predictive controller. Finally, two control strategies are studied through simulation experiments: (1) the heuristic PI control with two operation modes, which can be conveniently implemented but cannot coordinate the control of the power tracking speed and the main steam parameters, and (2) advanced model predictive control (MPC), which overcomes the shortcoming of PI (Proportional-Integral) control and can significantly improve the control performance.
Xiufan Liang; Yiguo Li. Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant. Energies 2019, 12, 1564 .
AMA StyleXiufan Liang, Yiguo Li. Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant. Energies. 2019; 12 (8):1564.
Chicago/Turabian StyleXiufan Liang; Yiguo Li. 2019. "Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant." Energies 12, no. 8: 1564.
For environmental protection, solvent-based post-combustion carbon capture (PCC) process has been applied to the coal-fired power plant as an end-of-pipe approach. Power plant needs to provide steam for PCC process and this will significantly influence the net power output. In this regard, this paper aims to: 1) provide an integrated model of small-scale PCC process with small-scale coal-fired power plant; 2) propose feed-back control structures for the integrated system and 3) develop a feed-forward controller to manipulate the steam draw-off flowrate in order to satisfy the rapid response to grid demand. Case studies were presented to investigate the system performance in the face of setpoints tracking and response to load change. The simulations were carried out in gCCS toolkit. Results demonstrate that the feed-forward controller can satisfactorily lower the time needed for power output target tracking.
Peizhi Liao; Xiao Wu; Yiguo Li; Meihong Wang; Jiong Shen; Bo Sun; Lei Pan. Flexible operation of coal-fired power plant integrated with post-combustion CO2 capture. Energy Procedia 2019, 158, 4810 -4815.
AMA StylePeizhi Liao, Xiao Wu, Yiguo Li, Meihong Wang, Jiong Shen, Bo Sun, Lei Pan. Flexible operation of coal-fired power plant integrated with post-combustion CO2 capture. Energy Procedia. 2019; 158 ():4810-4815.
Chicago/Turabian StylePeizhi Liao; Xiao Wu; Yiguo Li; Meihong Wang; Jiong Shen; Bo Sun; Lei Pan. 2019. "Flexible operation of coal-fired power plant integrated with post-combustion CO2 capture." Energy Procedia 158, no. : 4810-4815.
The monoethanolamine (MEA)-based post-combustion CO2 capture plant must operate flexibly under the variation of the power plant load and the desired CO2 capture rate. However, in the presence of process nonlinearity, conventional linear control strategy cannot achieve the best performance under a wide operation range. Considering this problem, this paper systematically studies the multi-model modeling of the MEA-based CO2 capture process for the purpose of (1) implementing well-developed linear control techniques to the design of an advanced controller and (2) achieving a wide-range flexible operation of the CO2 capture process. The local linear models of the CO2 capture process are firstly established at given operating points using the method of subspace identification. Then the nonlinearity distribution at different loads of an upstream power plant and different CO2 capture rates is investigated via the gap metric. Finally, based on the nonlinearity investigation results, the suitable linear models are selected and combined together to form the multi-model system. The proposed model is validated using the measurement data, which is generated from a post-combustion CO2 capture model developed in the go-carbon capture and storage (gCCS) simulation platform. As the proposed multi-linear model has a simple mathematical expression and high prediction accuracy, it can be directly employed as the control model of a practical advanced control strategy to achieve a wide operating range control of the CO2 capture process.
Xiufan Liang; Yiguo Li; Xiao Wu; Jiong Shen; Kwang Y. Lee. Nonlinearity Analysis and Multi-Model Modeling of an MEA-Based Post-Combustion CO2 Capture Process for Advanced Control Design. Applied Sciences 2018, 8, 1053 .
AMA StyleXiufan Liang, Yiguo Li, Xiao Wu, Jiong Shen, Kwang Y. Lee. Nonlinearity Analysis and Multi-Model Modeling of an MEA-Based Post-Combustion CO2 Capture Process for Advanced Control Design. Applied Sciences. 2018; 8 (7):1053.
Chicago/Turabian StyleXiufan Liang; Yiguo Li; Xiao Wu; Jiong Shen; Kwang Y. Lee. 2018. "Nonlinearity Analysis and Multi-Model Modeling of an MEA-Based Post-Combustion CO2 Capture Process for Advanced Control Design." Applied Sciences 8, no. 7: 1053.
Fuel preparation is the control bottleneck in coal-fired power plants due to the unmeasurable nature or inaccurate measurement of key controlled variables. This paper proposes an inferential multi-model predictive control scheme based on moving horizon estimation for the fuel preparation system in coal-fired power plants, i.e., the pulverizing system, aimed at improving control precision of key operating variables that are unmeasurable or inaccurately measured, and improving system tracking performance across a wide operating range. We develop a first principle model of the pulverizing system considering the nonlinear dynamics of primary air, and then employ the genetic algorithm to identify the unknown model parameters. The outputs of the identified first principle model agree well with measured data from a real pulverizing system. Thereafter we derive a moving horizon estimation approach to estimate the desired, but unmeasurable or inaccurately measured, controlled variables. Estimation constraints are explicitly considered to reduce the influence of measurement uncertainty. Finally, nonlinearity of the pulverizing system is analyzed and a multi-model inferential predictive controller is developed using the extended input-output state space model to achieve offset-free performance. Simulation results show that the proposed soft sensor can provide improved estimates than conventional extended Kalman filter, and the proposed inferential control scheme can significantly improve performance of the pulverizing system.
Xiufan Liang; Yiguo Li; Xiao Wu; Jiong Shen. Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation. Energies 2018, 11, 589 .
AMA StyleXiufan Liang, Yiguo Li, Xiao Wu, Jiong Shen. Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation. Energies. 2018; 11 (3):589.
Chicago/Turabian StyleXiufan Liang; Yiguo Li; Xiao Wu; Jiong Shen. 2018. "Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation." Energies 11, no. 3: 589.
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.
Huirong Zhao; Jiong Shen; Yiguo Li; Joseph Bentsman. Preference adjustable multi-objective NMPC: An unreachable prioritized point tracking method. ISA Transactions 2017, 66, 134 -142.
AMA StyleHuirong Zhao, Jiong Shen, Yiguo Li, Joseph Bentsman. Preference adjustable multi-objective NMPC: An unreachable prioritized point tracking method. ISA Transactions. 2017; 66 ():134-142.
Chicago/Turabian StyleHuirong Zhao; Jiong Shen; Yiguo Li; Joseph Bentsman. 2017. "Preference adjustable multi-objective NMPC: An unreachable prioritized point tracking method." ISA Transactions 66, no. : 134-142.
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.
Xiao Wu; Jiong Shen; Yiguo Li; Kwang Y. Lee. Fuzzy modeling and predictive control of superheater steam temperature for power plant. ISA Transactions 2015, 56, 241 -251.
AMA StyleXiao Wu, Jiong Shen, Yiguo Li, Kwang Y. Lee. Fuzzy modeling and predictive control of superheater steam temperature for power plant. ISA Transactions. 2015; 56 ():241-251.
Chicago/Turabian StyleXiao Wu; Jiong Shen; Yiguo Li; Kwang Y. Lee. 2015. "Fuzzy modeling and predictive control of superheater steam temperature for power plant." ISA Transactions 56, no. : 241-251.
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.
Xiao Wu; Jiong Shen; Yiguo Li; Kwang Y. Lee. Data-driven modeling and predictive control for boiler–turbine unit using fuzzy clustering and subspace methods. ISA Transactions 2014, 53, 699 -708.
AMA StyleXiao Wu, Jiong Shen, Yiguo Li, Kwang Y. Lee. Data-driven modeling and predictive control for boiler–turbine unit using fuzzy clustering and subspace methods. ISA Transactions. 2014; 53 (3):699-708.
Chicago/Turabian StyleXiao Wu; Jiong Shen; Yiguo Li; Kwang Y. Lee. 2014. "Data-driven modeling and predictive control for boiler–turbine unit using fuzzy clustering and subspace methods." ISA Transactions 53, no. 3: 699-708.
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.
Yiguo Li; Jiong Shen; Kwang Y. Lee; Xichui Liu. Offset-free fuzzy model predictive control of a boiler–turbine system based on genetic algorithm. Simulation Modelling Practice and Theory 2012, 26, 77 -95.
AMA StyleYiguo Li, Jiong Shen, Kwang Y. Lee, Xichui Liu. Offset-free fuzzy model predictive control of a boiler–turbine system based on genetic algorithm. Simulation Modelling Practice and Theory. 2012; 26 ():77-95.
Chicago/Turabian StyleYiguo Li; Jiong Shen; Kwang Y. Lee; Xichui Liu. 2012. "Offset-free fuzzy model predictive control of a boiler–turbine system based on genetic algorithm." Simulation Modelling Practice and Theory 26, no. : 77-95.
In this paper, the problem of designing a controller for a highly coupled constrained nonlinear boiler-turbine system is addressed with a predictive controller. First, a nonlinear predictive control is implemented by genetic algorithm. Second, to guarantee fast output stabilization, an H-infinity fuzzy state-feedback tracking control is applied with a designed switching principle. The success of such a control structure is based on taking advantage of the optimal input sequence derived from the nonlinear predictive control based on artificial intelligent while ensuring a fast decay of the steady state error. Simulation results of the proposed design are given to illustrate its effectiveness and compared to other control schemes.
Jie Wu; Jiong Shen; Mattias Krug; Sing Kiong Nguang; Yiguo Li. GA-based nonlinear predictive switching control for a boiler-turbine system. Journal of Control Theory and Applications 2011, 10, 100 -106.
AMA StyleJie Wu, Jiong Shen, Mattias Krug, Sing Kiong Nguang, Yiguo Li. GA-based nonlinear predictive switching control for a boiler-turbine system. Journal of Control Theory and Applications. 2011; 10 (1):100-106.
Chicago/Turabian StyleJie Wu; Jiong Shen; Mattias Krug; Sing Kiong Nguang; Yiguo Li. 2011. "GA-based nonlinear predictive switching control for a boiler-turbine system." Journal of Control Theory and Applications 10, no. 1: 100-106.
In this paper, the problem of designing a fuzzy H∞ 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∞ 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.
J. Wu; S.K. Nguang; J. Shen; G. Liu; Y.G. Li. Robust tracking control of boiler–turbine systems. ISA Transactions 2010, 49, 369 -375.
AMA StyleJ. Wu, S.K. Nguang, J. Shen, G. Liu, Y.G. Li. Robust tracking control of boiler–turbine systems. ISA Transactions. 2010; 49 (3):369-375.
Chicago/Turabian StyleJ. Wu; S.K. Nguang; J. Shen; G. Liu; Y.G. Li. 2010. "Robust tracking control of boiler–turbine systems." ISA Transactions 49, no. 3: 369-375.