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research-article

Model free adaptive support vector regressor controller for nonlinear systems

Published: 01 May 2019 Publication History

Abstract

In this study, a novel model free support vector regressor controller (MF-SVRcontroller) is introduced for nonlinear dynamical systems. For the adaptation mechanism, a model free closed-loop margin which is a function of tracking error is derived and it is used to optimize the parameters of MF-SVRcontroller. The effectiveness of the adjustment mechanism and closed-loop performance of the MF-SVRcontroller have been examined by simulations performed on continuously stirred tank reactor (CSTR) and bioreactor benchmark systems. In order to observe the impacts of the removal of the model estimation block in control architecture, the performance of the MF-SVRcontroller is compared with a model based support vector regressor controller (MB-SVRcontroller) and SVM-based PID controller. The results indicate that MF-SVRcontroller diminishes the computational load of MB-SVRcontroller at the cost of a small amount of decrease in tracking performance.

Highlights

The gradient effects which occur in NN and ANFIS are vanished in SVR and global extremum is ensured.
In SVR, a non-convex optimization problem in primal form is tranformed to a new form called as dual form which is a convex objective function with linear constraints.
A novel model free adaptive SVR controller (MF-SVRcontroller) is proposed to directly control nonlinear dynamical systems.
The main contribution of MF-SVRcontroller is adjusting the parameters of SVR controller via tracking error without using the system model.
The most significant strength of MF-SVRcontroller is that the structure does not require any system identification phase.

References

[1]
Aidong, X., Yangbo, Z., Haibin, Y., 2009. Research on the application of model free adaptive (MFA) control in gas turbine. In: 9th International Conference on Electronic Measurement and Instruments, ICEMI’09, Beijing.
[2]
Boegli, M., Stauffer, Y., 2017. SVR based PV models for MPC based energy flow management. In: 7th International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale, CISBAT, Switzerland.
[3]
Camacho E.F., Constrained generalized predictive control, IEEE Trans. Automat. Control 38 (2) (1993) 327–332,.
[4]
Camacho E.F., Bordons C., Model Predictive Control, Springer-Verlag, New York, 1999.
[5]
Cheng G.S., Model-free adaptive(MFA) control, in: Lipták B.G. (Ed.), Instrument Engineer’s Handbook: Process Control and Optimization, fourth ed., CRC Press, 2006, pp. 224–-233.
[6]
Clarke D.W., Mohtadi C., Properties of generalized predictive control, Automatica 25 (6) (1989) 859–875,.
[7]
Clarke D.W., Mohtadi C., Tuffs P., Generalized predictive control-part I. The basic algorithm, Automatica 23 (2) (1987) 137–148,.
[8]
Coelho L.D., Coelho A.A.R., Model-free adaptive control optimization using a chaotic particle swarm approach, Chaos Solitons Fractals 41 (4) (2009) 2001–2009,.
[9]
Coelho L.D., Pessoa M.W., Sumar R.R., Coelho A.A.R., Model-free adaptive control design using evolutionary-neural compensator, Expert Syst. Appl. 37 (1) (2010) 499–508,.
[10]
Cristianini N., Shawe-Taylor J., An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Cambridge, 2000.
[11]
Cui X.Z., Shin K.G., Direct control and coordination using neural networks, IEEE Trans. Syst. Man Cybern. 23 (3) (1993) 686–697.
[12]
Efe, M.O., 2007. Discrete time fuzzy sliding mode control of a biochemical process. In: 9th WSEAS International Conference on Automatic Control, Modeling and Simulation, ACMOS’07, Istanbul.
[13]
Efe M.O., Abadoglu E., Kaynak O., A novel analysis and design of a neural network assisted nonlinear controller for a bioreactor, Internat. J. Robust Nonlinear Control 9 (11) (1999) 799–815,.
[14]
Erol O.K., Eksin I., A new optimization method: Big Bang Big Crunch, Adv. Eng. Softw. 37 (2) (2006) 106–111,.
[15]
Iplikci S., Online trained support vector machines-based generalized predictive control of non-linear systems, Internat. J. Adapt. Control Signal Process. 20 (10) (2006) 599–621,.
[16]
Iplikci S., Support vector machines-based generalized predictive control, Internat. J. Robust Nonlinear Control 16 (17) (2006) 843–862,.
[17]
Iplikci S., A comparative study on a novel model-based PID tuning and control mechanism for nonlinear systems, Internat. J. Robust Nonlinear Control 20 (13) (2010) 1483–1501,.
[18]
Kravaris C., Palanki S., Robust nonlinear state feedback under structured uncertainty, AIChe J. 34 (7) (1988) 1119–1127,.
[19]
Levenspiel O., Chemical Reaction Engineering, John Wiley and Sons, USA, 1999.
[20]
Lightbody G., Irwin G.W., Direct neural model reference adaptive control, IEE Proc. 142 (1) (1995) 31–43,.
[21]
Ma J., Theiler J., Perkins S., Accurate online support vector regression, Neural Comput. 15 (11) (2003) 2683–2703,.
[22]
Mario, M., 2002. On-line support vector machine regression. In: 13th European Conference on Machine Learning, ECML 2002, Helsinki.
[23]
Naghash-Almasi O., Khooban M.H., PI Adaptive LS-SVR control scheme with disturbance rejection for a class of uncertain nonlinear systems, Eng. Appl. Artif. Intell. 52 (2016) 135–144,.
[24]
Psaltis D., Sideris A., Yamamura A.A., A multilayered neural network controller, IEEE Control Syst. Mag. 8 (2) (1988) 17–21,.
[25]
Psichogios D.C., Ungar L.H., Direct and indirect model based control using artificial neural networks, Ind. Eng. Chem. Res. 30 (12) (1991) 2564–2573,.
[26]
Puskorius G.V., Feldkamp L.A., Neurocontrol of nonlinear dynamical systems with kalman filter trained recurrent network, IEEE Trans. Neural Netw. 5 (2) (1994) 279–297,.
[27]
Saerens, M., Soquet, A., 1989. A neural controller. In: First IEE International Conference on Artificial Neural Networks, London.
[28]
Saerens M., Soquet A., Neural controller based on back-propagation algorithm, IEE Proc. F 138 (1) (1991) 55–62.
[29]
Shamsollahi, P., Malik, O.P., 1997. Direct neural adaptive control applied to synchronous generator. In: IEEE International Electric Machines and Drives Conference, IEMDC 97, Milwaukee.
[30]
Shin J., Jin Kim H., Park S., Kim Y., Model predictive flight control using adaptive support vector regression, Neurocomputing 73 (4–6) (2010) 1031–1037,.
[31]
Smola A.J., Schölkopf B., A tutorial on support vector regression, Stat. Comput. 14 (3) (2004) 199–222,.
[32]
Sun, C., Song, J., 2007. An adaptive internal model control based on LS-SVM. In: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing.
[33]
Teshnehlab M., Keigo W., Intelligent Control Based on Flexible Neural Networks, Springer-Science+Business Media, B.V., 1999.
[34]
Uçak K., Öke Günel, An adaptive support vector regressor controller for nonlinear systems, Soft Comput. 20 (7) (2016) 2531–2556,.
[35]
Uçak K., Öke Günel G., A novel adaptive NARMA-L2 controller based on online support vector regression for nonlinear systems, Neural Process. Lett. 44 (3) (2016) 857–886,.
[36]
Uçak K., Öke Günel G., Generalized self-tuning regulator based on online support vector regression, Neural Comput. Appl. 28 (Suppl 1) (2017) S775–S801,.
[37]
Ünal, B., Efe, M.O., 2007. Robust Neurocontrol of a bioreactor system. In: Turkish National Committee for Automatic Control, TOK ’07, Istanbul.
[38]
Ungar L.H., Neural networks for control, in: Miller I.I.I. W.T., Sutton R.S., Werbos P.J. (Eds.), A Bioreactor Benchmark for Adaptive Network based Process Control, MIT Press, USA, 1990, pp. 387–402.
[39]
VanDoren V.J., Techniques for Adaptive Control, Elsevier, 2003.
[40]
Wanfeng, S., Shengdun, Z., Yajing, S., 2008. Adaptive PID controller based on online lssvm identification. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2008, Xian.
[41]
Wang H., Pi D., Sun Y., Online SVM regression algorithm-based adaptive inverse control, Neurocomputing 70 (4–6) (2007) 952–959,.
[42]
Wu W., Chou Y.S., Adaptive feedforward and feedback control of non-linear time-varying uncertain systems, Internat. J. Control 72 (12) (1999) 1127–1138,.
[43]
You K., Adaptive control, in: In-Tech, 2009.
[44]
Yuan M., Poo A.N., Hong G.S., Direct neural control system: Nonlinear extension of adaptive control, IEE Proc. 142 (6) (1995) 661–667.
[45]
Zhang Y., Sen P., Hearn G.E., An on-line trained adaptive neural controller, IEEE Control Syst. 15 (5) (1995) 67–75,.
[46]
Zhicheng Z., Zhiyuan L., Zhimin X., Jinggang Z., Internal model control based on LS-SVM for a class of nonlinear process, Physics Procedia 25 (2012) 1900–1908,.
[47]
Zhiying, D., Xianfang, W., 2008. Nonlinear generalized predictive control based on online SVR. In: 2nd International Symposium on Intelligent Information Technology Application, Shanghai.
[48]
Zhong, W., Pi, D., Sun, Y., Xu, C., Chu, S., 2006. SVM based internal model control for nonlinear systems. In: 3rd International Symposium on Neural Networks, ISNN 2006, Chengdu.

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  • (2024)Adaptive stable backstepping controller based on support vector regression for nonlinear systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107533129:COnline publication date: 16-May-2024

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Published In

cover image Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence  Volume 81, Issue C
May 2019
468 pages

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 May 2019

Author Tags

  1. Adaptive control
  2. Direct adaptive control
  3. Model free adaptive control(MFAC)
  4. Model free SVR controller
  5. Online support vector regression
  6. SVR controller

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  • (2024)Adaptive stable backstepping controller based on support vector regression for nonlinear systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107533129:COnline publication date: 16-May-2024

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