Abstract
In this chapter, a coal gasification optimal tracking control problem is solved through a data-based optimal learning control scheme using iterative adaptive dynamic programming (ADP) approach. According to the system data, neural networks (NNs) are used to construct the dynamics of coal gasification process , the coal quality function, and the reference control, respectively, where the mathematical model of the system is unnecessary. The approximation errors from NN construction of the disturbance and the controls are both considered. Via system transformation, the optimal tracking control problem with approximation errors and disturbances is effectively transformed into a two-person zero-sum game. An iterative ADP algorithm is then developed to obtain the optimal control laws for the transformed system. Convergence property is developed to guarantee that the cost function converges to a finite neighborhood of the optimal cost function, and the convergence criterion is also obtained. Finally, numerical results are given to illustrate the performance of the present method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abani N, Ghoniem AF (2013) Large eddy simulations of coal gasification in an entrained flow gasifier. Fuel 104:664–680
Basar T, Bernard P (1995) \(H_\infty \) Optimal control and related minimax design problems. Birkhauser, Boston
Bhasin S, Kamalapurkar R, Johnson M, Vamvoudakis KG, Lewis FL, Dixon WE (2013) A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems. Automatica 49(1):82–92
Chen Y, Li Z, Zhou M (2014) Optimal supervisory control of flexible manufacturing systems by petri nets: a set classification approach. IEEE Trans Autom Sci Eng 11(2):549–563
Gopalsami N, Raptis AC (1984) Acoustic velocity and attenuation measurements in thin rods with application to temperature profiling in coal gasification systems. IEEE Trans Sonics Ultrason 31(1):32–39
Guo R, Cheng G, Wang Y (2006) Texaco coal gasification quality prediction by neural estimator based on dynamic PCA. In: Proceedings of the IEEE international conference on mechatronics and automation, pp 2241–2246
Jia QS (2011) An adaptive sampling algorithm for simulation-based optimization with descriptive complexity preference. IEEE Trans Autom Sci Eng 8(4):720–731
Jin X, Hu SJ, Ni J, Xiao G (2013) Assembly strategies for remanufacturing systems with variable quality returns. IEEE Trans Autom Sci Eng 10(1):76–85
Kang Q, Zhou M, An J, Wu Q (2013) Swarm intelligence approaches to optimal power flow problem with distributed generator failures in power networks. IEEE Trans Autom Sci Eng 10(2):343–353
Kostur K, Kacur J (2012) Developing of optimal control system for UCG. In: Proceedings of the international carpathian control conference, pp 347–352
Liu D, Wei Q (2013) Finite-approximation-error-based optimal control approach for discrete-time nonlinear systems. IEEE Trans Cybern 43(2):779–789
Matveev IB, Messerle VE, Ustimenko AB (2009) Investigation of plasma-aided bituminous coal gasification. IEEE Trans Plasma Sci 37(4):580–585
Ruprecht P, Schafer W, Wallace P (1988) A computer model of entrained coal gasification. Fuel 67(6):739–742
Serbin SI, Matveev IB (2010) Theoretical investigations of the working processes in a plasma coal gasification system. IEEE Transactions on Plasma Science 12(38):3300–3305
Si J, Wang YT (2001) Online learning control by association and reinforcement. IEEE Trans Neural Netw 12(2):264–276
Wei Q, Liu D (2014) Adaptive dynamic programming for optimal tracking control of unknown nonlinear systems with application to coal gasification. IEEE Trans Autom Sci Eng 11(4):1020–1036
Wigstrom O, Lennartson B, Vergnano A, Breitholtz C (2013) High-level scheduling of energy optimal trajectories. IEEE Trans Autom Sci Eng 10(1):57–64
Wilson JA, Chew M, Jones WE (2006) State estimation-based control of a coal gasifier. IEE Proc-Control Theory Appl 153(3):268–276
Xu J, Qiao L, Gore J (2013) Multiphysics well-stirred reactor modeling of coal gasification under intense thermal radiation. Int J Hydrog Energy 38(17):7007–7015
Yang Q, Jagannathan S (2012) Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators. IEEE Trans Syst, Man, Cybern-Part B: Cybern 42(2):377–390
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Liu, D., Wei, Q., Wang, D., Yang, X., Li, H. (2017). Adaptive Dynamic Programming for Optimal Control of Coal Gasification Process. In: Adaptive Dynamic Programming with Applications in Optimal Control. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-50815-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-50815-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50813-9
Online ISBN: 978-3-319-50815-3
eBook Packages: EngineeringEngineering (R0)