Research Article  Open Access
Pu Han, Miao Liu, Dongfeng Wang, Hao Jia, "Design of RMPC for Boiler Superheated Steam Temperature Based on Memoryless Feedback Multistep Strategy", Mathematical Problems in Engineering, vol. 2017, Article ID 2192393, 9 pages, 2017. https://doi.org/10.1155/2017/2192393
Design of RMPC for Boiler Superheated Steam Temperature Based on Memoryless Feedback Multistep Strategy
Abstract
The collection of superheated steam temperature models of a thermal power plant under different loads can be approximated to “multimodel” linear uncertain systems. After transformation, the tracking system was obtained from “multimodel” linear uncertain systems. For this tracking uncertain system, a mixed robust model predictive control (HRMPC) based on a memoryless feedback multistep strategy is proposed. A multistep control strategy combines the advantages of predictive control rolling optimization with memoryless feedback control thoughts. It could effectively decrease the controller optimization parameter and ensure closedloop system stability, and, at the same time, it also achieved acceptable control performance. Successful application to the superheated steam temperature system of a 300 MW thermal power plant verified the study of the HRMPCP cascade controller design scheme in terms of feasibility and effectiveness.
1. Introduction
The superheated steam temperature of a thermal power plant directly affects the thermal efficiency, safe operation of the superheated pipe, and the steam turbine equipment. A superheated steam system is characterized by large inertia and a substantial amount of lag [1]. Most power plants continue to adopt a conventional steam temperature control system, such as a cascade PID scheme or double circuit control system with a lead steam temperature differential signal. In recent years, a number of advanced control algorithms were being researched for application in superheated steam temperature systems; these include adaptive fuzzy neural network control [2], adaptive predictive control [3], nonlinear generalized predictive control [4], network cascade control [5], and active disturbance rejection control [6].
Model predictive control (MPC), also known as rolling time control, was first applied to the linear timeinvariant (LTI) system [7, 8]. Despite the heavy computational burden incurred in its implementation, MPC is characterized by good robustness, which has been extended to some other important classes of systems, such as distributed systems [9] and nonlinear systems [10]. Its application in industrial contexts is more recent [11]. Uncertainty and disturbance are widespread for the actual system. Using the method of robust control treatment for an uncertain system as reference, robust model predictive control (RMPC) can effectively deal with model uncertainties and disturbance. Furthermore, the controlled system achieves asymptotic stability under the condition of meeting the feasibility. Therefore, as an important branch of model predictive control, RMPC attracts increasing attention [12–14].
Linear matrix inequalities (LMI), just as the name implies, are linear in the matrix variables. Many control problems can be converted into a feasibility problem of an LMI system or a convex optimization problem with LMI constraints [15]. With the development of the LMI toolbox in MATLAB, the LMI approach has been widely used in the field of systems and robust control [16].
Lately, many robust model predictive control algorithms have been proposed [13–17]. Although these algorithms enable the system to approach a stable state, they do not meet the required expectations. Robust tracking controls were investigated for uncertain discrete time systems and application in thermal power plant systems [18–23]. However, the designs were too complicated and some designs had to undergo online optimization control as independent optimization variables to guarantee the constraint conditions for the control law.
In this paper, we designed a mixed robust model predictive control (HRMPC) based on a memoryless feedback multistep strategy for superheated steam temperature “multimodel” or “multipacket” linear uncertain system of a thermal power plant. Section 2 states the problem to be solved and introduces some standard assumptions. Section 3 discusses the HRMPC method, which adopts a closedloop multistep control strategy and the memoryless feedback control thought to ensure the stability of the closed loop. At the same time, it could also achieve good control performance. In addition, considering simplified design, robust stability, and practical application, we studied a HRMPCP cascade controller design scheme with cascade control structure in Section 4. Simulation and experimental results are discussed in Section 5. Lastly, concluding remarks are presented in Section 6.
Preliminaries are as follows:(1).(2)Schur Complement Lemma: consider the partitioned matrix (a)When is nonsingular, is called the Schur complement of in .(b)When is nonsingular, is called the Schur complement of in .
2. Problem Statement
Consider the following multipacket uncertain systems:where is the realtime state, is the control input, is the plant output, and is the unknown but bounded disturbance. , , and
represent a state model of the system under a particular operating condition.
In this paper, we consider Euclidean norm bounds and componentwise peak bounds on the input , given, respectively, as with a known bound .
Assume that are controllable, and the system external disturbance satisfying (6) is said to be an admissible disturbancewith a known upper bound .
The RMPC system reference signal is produced by the following system:where are the reference states, are the expected input values, is the Hurwitz matrix with an appropriate dimension, and , are a constant matrices with an appropriate dimension.
In order to ensure that the state variables of system (2) track the RMPC system reference signal , we designed a linear memoryless feedback controller as follows:where is the controller state and , are feedback controller gains that need to be solved.
Substituting the memoryless feedback controller (8) into system (2), we have the closeloop system
By completing formulas (8) and (9), the tracking system can be obtainedwhere Therefore, the tracking system (10) is also a multipacket uncertain system. And the memoryless state feedback control law (8) can be rewritten as
Control law (12) is required to make the tracking system (10) meet the following performance index.
(a) Performance Index. Given the multiple packet uncertain timevarying discrete system (2) and a positive scalar , design a memoryless state feedback control law in the form of (12) such that the closedloop tracking system (10) is asymptotically stable and satisfies where is the transfer function from to .
(b) Performance Index. Given the multiple packet uncertain timevarying discrete system (2) and a scalar , design a memoryless state feedback control law in the form of (12) such that the output of the tracking system (10) is asymptotically stable and satisfies
3. Design of Multistep Robust Predictive Controller
3.1. Performance Index
In order to reduce the conservativeness of the design, the multistep control set is utilized [11], and the control strategy (15) as the system control law in the futurewhere , are predicted feedback gains of the tracking system at sampling time . is control horizon.
With respect to the input constraints, we can getwhere is the th element of the control input corresponding to the feedback control gain of , and is the th row of dimensional identity matrix.
For this control strategy, we select the Lyapunov function as follows:where is the prediction horizon and the length of indicates steps from time , the predicted output value close to the expected value. , when , .
Denote
We can obtain thatDenotethen
Lemma 1. For tracking system (10), denote , where , , and then a memoryless state feedback control law exists such that if there exist , and satisfyingwhere .
Proof. We sum both sides of equality (19) from to . Actually, when the system output is close to the expected value and ; then we obtain the following equation:Equation (25) can be rewritten as(1) While , the performance index (13) is equivalent toThus, from (26) it is known that if , then (27) is satisfied. In other words, performance index is met.
(2) From (26), if , satisfying the following conditions, then performance index is met:For , (21) can be written asFurther, using the Schur complement in (29), we haveMultiply both sides of inequality (30) by using Denote that , ; condition (23) can be obtained.
In the same way, using the Schur complement in (28) , we haveMultiplying both sides of inequality (32) by usingthen condition (24) can be obtained.
3.2. Constraint Condition
For constraint (5), we have the following lemma.
Lemma 2. If there exist and , such that Lemma 1 is established, and there exist symmetric matrices such that the following inequality is satisfied, then constraint (5) is met
Proof. At sample time , consider the Euclidean norm constraint (5).Following controller (12), we havewhere ; using the Schur complement, we haveIf the inequality is establishedat sample time using the CauchySchwarz inequality [24], we haveThus, symmetric matrix satisfieswhere are diagonal elements of matrix .
In conclusion, for tracking system (10), we have the memoryless feedback multistep robust model predictive control algorithm from Lemmas 1 and 2. At sample time , solve the following optimization problem:The current system input of the controller is
3.3. Robust Stability
Theorem 3. Consider the multipacket uncertain tracking system (14). If the optimization problem is feasible with the system state , then system (14) in closeloop is robustly stable.
Proof. At sample time , optimization problem (42) is feasible with the optimal solutionWhile disturbance exists at sample time , the optimization problem (42) has the optimal solutionwhere , and by the principle of optimization, we haveWhen disturbance disappears, due to the fact that , , it means that will decay to 0. Thus, the closedloop system has robust stability.
4. HRMPCP Cascade Controller Design
The optimization goal of the inner loop is to quickly eliminate interference from the spraying system or burning system, and the optimization goal does not require difference. As a result, the proportional controller is often used in the inner loop, which is regarded as a quick followup system. In this paper, we study HRMPCP controller design with a cascade control structure by considering a simplified design, robust stability, and practical application. A proportional controller is adopted for the inner loop. The HRMPCP cascade control structure is as shown in Figure 1.
Assume that the secondary superheated transfer functions are known, including both the leading and inertia segments. The HRMPCP cascade control design steps are as follows:(1)Select appropriate rolling optimization steps , control horizon , and . The feasible regions of the controller broaden as increases [11].(2)Optimize the inner loop PID controller; in this work, the proportional controller simply needs to optimize the proportional gain .(3)Set expectations and the reference system.(4)At sample time , when , solve optimization problem (42), and obtain the optimal solution(5), until , and obtain the corresponding optimal solution(6)Determine the current moment controller output (7)Taking into the inner loop discrete state space model (50), we have where is the leading segment state, is the proportional control output, is the leading segment output.(8)Taking into the tracking system (10), we have the state .(9)Make , jump to Step (4).
5. Simulation
Choose two secondary superheated system transfer functions under different loads of 180 MW and 250 MW of a 300 MW power unit [21]. Design the controller to use the method discussed in Sections 4 and 5.
The first part is a leading segment transfer function and the last part is the inertia segment transfer function for each transfer function.
Using a sampling time s, convert the transfer function mode into discrete state space mode. After discretization, the leading segment and the inertia segment are as in Table 1.

In each state , ,
The step responses of the system with different values of are shown in Figure 2. Choose the transfer function as an example, set the value of and , and we have the step responses of the system. As is shown in the figure, the ability to restrain the interference of the system is increased with the decrease of .
The tracking performance of different controllers is shown in Figure 5. Choose the transfer function as an example. We compared the control quality of HRMPCP cascade control with the conventional cascade PID control (PIDP) and dynamic matrix control [6] (DMCP) through the system output tracking situation. The control laws and control structures are illustrated as follows.
(1) PID Controllerwhere , , and all nonnegative, denoting the coefficients for the proportional, integral, and derivative terms; is the difference between a desired set point and a measured process variable. The PIDP control structure is shown in Figure 3.
(2) DMC Controllerwhere is dynamic matrix; is process matrix which is composed of dynamic coefficients; and are the weighting matrices. means the th row of , if the current moment . The DMCP control structure is shown in Figure 4.
(3) HRMPCP Controller. The control law and control structure refer to (12) and Figure 1.
As is shown in Figure 5, the HRMPCP cascade control enables the system to have good tracking performance such that the system output can meet the expectations faster. The inputs of the different controllers are shown in Figure 6(a). The inputs of HRMPC controller, DMC controller, and PID controller, respectively, are system states, control increment, and the difference between desired set points and measured process variables. The outputs of the different controllers are shown in Figure 6(b). The HRMPCP controller has a smaller output range and faster adjusting speed and can complete the adjustment in about 200 s.
(a) Input of different controllers
(b) Output of different controllers
As is shown in Figure 7, the load command changes from 180 MW to 250 MW when s. Under this condition, when s the system model translates from to . The robustness of different controllers is shown in Figure 8. HRMPCP cascade control still has an enhanced tracking effect, which can quickly adjust the system to the expected value while the system changes. Thus, HRMPCP cascade control structure has stronger robustness.
Figure 9 shows the output of different controllers while the load command changes. The HRMPCP controller also has a smaller output range and faster adjusting speed. The variation tendency of HRMPC controller gains and is shown in Figure 10. The variation range of these two gain groups is small, and they adjust quickly as well.
6. Concluding Remarks
This paper presents a design strategy for a memoryless feedback multistep RMPC controller for the superheated steam temperature of a thermal power unit. In this study, the collection of superheated steam temperature model under different load is approximated to “multimodel” linear uncertain systems. After transformation, the tracking system is obtained. Then through deduction, we have the HRMPC control flow. Moreover, successful application of the proposed HRMPCP cascade control scheme to the superheated steam temperature system of a thermal power plant and improved control qualities are achieved. The HRMPCP cascade control scheme is able to adapt to the whole operating range of the superheated steam temperature system of thermal power plants. Moreover, the decrease in the number of controller parameters may serve as a reference for actual engineering projects.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This research was supported by the National Natural Fund of China under Grant no. 71471060 and the Coal Based Key Scientific and Technological Project of Shanxi Province, China, under Grant no. MD2014030602.
References
 D. Wang and S. Yuan, “Identification of LPV model for superheated steam temperature system using AQPSO,” Simulation Modelling Practice and Theory, vol. 69, pp. 1–13, 2016. View at: Publisher Site  Google Scholar
 X. Dong, Y. Zhao, H. R. Karimi, and P. Shi, “Adaptive variable structure fuzzy neural identification and control for a class of MIMO nonlinear system,” Journal of the Franklin Institute, vol. 350, no. 5, pp. 1221–1247, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 W. Yao, H. Sun, J. Wen, and S. Cheng, “An adaptive predictive PI control system of superheated steam temperature based on Laguerre model,” Proceedings of the Chinese Society of Electrical Engineering, vol. 32, no. 5, pp. 119–125, 2012. View at: Google Scholar
 B. Xiao, X. Zhang, and X. Dong, “Superheated steam temperature control research of the improved implicit generalized predictive algorithm based on the soft coefficient matrix,” Journal of Computational and Theoretical Nanoscience, vol. 9, no. 10, pp. 1733–1740, 2012. View at: Publisher Site  Google Scholar
 J. Zhang, S. Zhou, M. Ren, and H. Yue, “Adaptive neural network cascade control system with entropybased design,” IET Control Theory & Applications, vol. 10, no. 10, pp. 1151–1160, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 G. Liang, W. Li, and Z. Li, “Control of superheated steam temperature in largecapacity generation units based on active disturbance rejection method and distributed control system,” Control Engineering Practice, vol. 21, no. 3, pp. 268–285, 2013. View at: Publisher Site  Google Scholar
 F. Blanchini, D. Casagrande, G. Giordano, and U. Viaro, “Robust constrained model predictive control of fast electromechanical systems,” Journal of the Franklin Institute, vol. 353, no. 9, pp. 2087–2103, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 Y. Ding, Z. Xu, J. Zhao, and Z. Shao, “Fast model predictive control combining offline method and online optimization with KD tree,” Mathematical Problems in Engineering, vol. 2015, Article ID 982041, 10 pages, 2015. View at: Publisher Site  Google Scholar  MathSciNet
 S. Yan, F. Xiaosheng, and D. Qingda, “Mixed H_{2}/H_{∞} distributed robust model predictive control for polytopic uncertain systems subject to actuator saturation and missing measurements,” International Journal of Systems Science, vol. 47, no. 4, pp. 777–790, 2016. View at: Google Scholar
 G. V. Raffo, M. G. Ortega, and F. R. Rubio, “An integral predictive/nonlinear ${H}_{\infty}$ control structure for a quadrotor helicopter,” Automatica, vol. 46, no. 1, pp. 29–39, 2010. View at: Publisher Site  Google Scholar  MathSciNet
 H. Han and J. Qiao, “Nonlinear modelpredictive control for industrial processes: an application to wastewater treatment process,” IEEE Transactions on Industrial Electronics, vol. 61, no. 4, pp. 1970–1982, 2014. View at: Publisher Site  Google Scholar
 D. Q. Mayne, “Model predictive control: recent developments and future promise,” Automatica, vol. 50, no. 12, pp. 2967–2986, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 H. Li and Y. Shi, “Eventtriggered robust model predictive control of continuoustime nonlinear systems,” Automatica, vol. 50, no. 5, pp. 1507–1513, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 A. J. Del Real, A. Arce, and C. Bordons, “Combined environmental and economic dispatch of smart grids using distributed model predictive control,” International Journal of Electrical Power and Energy Systems, vol. 54, pp. 65–76, 2014. View at: Publisher Site  Google Scholar
 M. J. Park, O. M. Kwon, and E. J. Cha, “On stability analysis for generalized neural networks with timevarying delays,” Mathematical Problems in Engineering, vol. 2015, Article ID 387805, 11 pages, 2015. View at: Publisher Site  Google Scholar
 R. Rao, X. Wang, and S. Zhong, “LMIbased stability criterion for impulsive delays markovian jumping timedelays reactiondiffusion BAM neural networks via gronwallbellmantype impulsive integral inequality,” Mathematical Problems in Engineering, vol. 2015, Article ID 185854, 11 pages, 2015. View at: Publisher Site  Google Scholar  MathSciNet
 H. He, D. Li, and X. Yugeng, “On design of mixed ${H}_{2}$/${H}_{\infty}$ RMPC based on multistep control strategy,” Acta Automatica Sinica, vol. 38, no. 6, pp. 944–950, 2012. View at: Google Scholar
 J. Ding, B. Cichy, K. Galkowski, E. Rogers, and H. Yang, “Parameterdependent Lyapunov functionbased robust iterative learning control for discrete systems with actuator faults,” International Journal of Adaptive Control and Signal Processing, vol. 30, no. 12, pp. 1714–1732, 2016. View at: Publisher Site  Google Scholar
 T. Zhongliang, G. S. Sam, T. K. Peng, and H. Wei, “Robust adaptive neural tracking control for a class of perturbed uncertain nonlinear systems with state constraints,” IEEE Transactions on Systems Man CyberneticsSystems, vol. 46, no. 12, pp. 1618–1629, 2016. View at: Google Scholar
 M. Yu, D. Huang, and W. He, “Robust adaptive iterative learning control for discretetime nonlinear systems with both parametric and nonparametric uncertainties,” International Journal of Adaptive Control and Signal Processing, vol. 30, no. 7, pp. 972–985, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 C.Q. Zhang, F. Yang, J.Z. Xue, and Y.X. Huang, “Design of the ${H}_{\infty}$ performance state observer for the boiler steam temperature control,” Proceedings of the Chinese Society of Electrical Engineering, vol. 26, no. 14, pp. 109–113, 2006. View at: Google Scholar
 W. Jiang, H.l. Wang, J.h. Lu, W.w. Qin, and G.b. Cai, “Nonfragile robust model predictive control for uncertain constrained systems with timedelay compensation,” Mathematical Problems in Engineering, vol. 2016, Article ID 1945964, 14 pages, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 Z. Guoqiang and L. Jinkun, “Neural networkbased adaptive backstepping control for hypersonic flight vehicles with prescribed tracking performance,” Mathematical Problems in Engineering, vol. 2015, Article ID 591789, 10 pages, 2015. View at: Publisher Site  Google Scholar  MathSciNet
 M. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrained model predictive control using linear matrix inequalities,” Automatica, vol. 32, no. 10, pp. 1361–1379, 1996. View at: Publisher Site  Google Scholar  MathSciNet
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Copyright © 2017 Pu Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.