Abstract
Input–output models are common in industrial processes, and they can be easily obtained by model identification algorithms, such as recursive least-square identification, neural network modeling, support vector machine, etc. (Abbaszadeh et al. in IET Electric Power Applications 11: 847–856, [1]; Cao et al. in Journal of Process Control 24: 871–879, [2]; Wang et al. in IET Control Theory and Applications 12: 446–445, [3]; Zhang and Tao in IEEE Transactions on Industrial Electronics 64: 5882–5892, [4]; Mao et al. in IEEE Transactions on Industrial Electronics 65: 5704–5711,[5].
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
Abbaszadeh, A., Khaburi, D. A., & Rodriguez, J. (2017). Predictive control of permanent magnet synchronous motor with non-sinusoidal flux distribution for torque ripple minimisation using the recursive least square identification method. IET Electric Power Applications, 11(5), 847–856.
Cao, Z. X., Yang, Y., Lu, J. Y., & Gao, F. R. (2014). Constrained two dimensional recursive least squares model identification for batch processes. Journal of Process Control, 24(6), 871–879.
Wang, Q. J., Dai, W., Ma, X. P., & Yang, C. Y. (2018). Multiple models and neural networks based adaptive PID decoupling control of mine main fan switchover system. IET Control Theory and Applications, 12(4), 446–455.
Zhang, R. D., & Tao, J. (2018). A nonlinear fuzzy neural network modeling approach using an improved genetic algorithm. IEEE Transactions on Industrial Electronics, 65(7), 5882–5892.
Mao, X. F., Wang, Y. J., Liu, X. D., & Guo, Y. G. (2018). A hybrid feedforward-feedback hysteresis compensator in piezoelectric actuators based on least-squares support vector machine. IEEE Transactions on Industrial Electronics, 65(7), 5704–5711.
Abbas, H. S., Hanema, J., Toth, R., Mohammadpour, J., & Meskin, N. (2018). An improved robust model predictive control for linear parameter-varying input-output models. International Journal of Robust and Nonlinear Control, 28(3), 859–880.
Ding, B. C., & Zou, T. (2014). A synthesis approach for output feedback robust model predictive control based-on input-output model. Journal of Process Control, 24(3), 60–72.
Chen, J. H., & Lin, Y. H. (2012). Multibatch model predictive control for repetitive batch operation with input-output linearization. Industrial & Engineering Chemistry Research, 51(28), 9598–9608.
Tao, J. L., Zhu, Y., & Fan, Q. R. (2014). Improved state space model predictive control design for linear systems with partial actuator failure. Industrial & Engineering Chemistry Research, 53(9), 3578–3586.
Gallego, A. J., & Camacho, E. F. (2012). Adaptative state-space model predictive control of a parabolic-trough field. Control Engineering Practice, 20(9), 904–911.
Falugi, P., Olaru, S., & Dumur, D. (2010). Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law. International Journal of Control, 83(8), 1548–1563.
Miranda, H., Cortes, P., Yuz, J. I., & Rodriguez, J. (2009). Predictive torque control of induction machines based on state-space models. IEEE Transactions on Industrial Electronics, 56(6), 1916–1924.
Simkoff, J. M., Wang, S., Baldea, M., Chiang, L. H., Castillo, I., Bindlish, R., et al. (2018). Plant-model mismatch estimation from closed-loop data for state-space model predictive control. Industrial & Engineering Chemistry Research, 57(10), 3732–3741.
Zou, T., Wu, S., & Zhang, R. D. (2018). Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization. ISA Transactions, 73, 147–153.
Lawrynczuk, M. (2017). Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation. ISA Transactions, 67, 476–495.
Zhang, R. D., Cao, Z. X., Bo, C. M., Li, P., & Gao, F. R. (2014). New PID controller design using extended nonminimal state space model based predictive functional control structure. Industrial & Engineering Chemistry Research, 53(8), 3283–3292.
Zhang, R. D., Zou, Q., Cao, Z. X., & Gao, F. R. (2017). Design of fractional order modeling based extended non-minimal state space MPC for temperature in an industrial electric heating furnace. Journal of Process Control, 56, 13–22.
Wang, L. P., & Young, P. C. (2006). An improved structure for model predictive control using non-minimal state space realisation. Journal of Process Control, 16(4), 355–371.
Zhang, R. D., Xue, A. K., Wang, S. Q., & Ren, Z. Y. (2011). An improved model predictive control approach based on extended non-minimal state space formulation. Journal of Process Control, 21(8), 1183–1192.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Zhang, R., Xue, A., Gao, F. (2019). Model Predictive Control Based on Extended Non-minimal State Space Model. In: Model Predictive Control. Springer, Singapore. https://doi.org/10.1007/978-981-13-0083-7_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-0083-7_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0082-0
Online ISBN: 978-981-13-0083-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)