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Adaptive Simplified Model Predictive Control with Tuning Considerations | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 7، دوره 46، شماره 2، بهمن 2014، صفحه 57-63 اصل مقاله (675.89 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2015.514 | ||
| نویسندگان | ||
| A.S. Ashtari1؛ A. Khaki Sedigh* 2 | ||
| 1MSc. Student, APAC Research Group, Industrial Control Center of Excellence, K.N.Toosi University of Technology, Tehran, Iran | ||
| 2Professor, APAC Research Group, Industrial Control Center of Excellence, K.N.Toosi University of Technology, Tehran, Iran | ||
| چکیده | ||
| Model predictive controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly calculated. The proposed method is validated via simulations for both slow and fast time varying systems. Simulation results indicate the controller ability for tracking references in the presence of plant’s time varying parameters. In addition, an analytical tuning method for adjusting prediction horizon is proposed based on optimization of the objective function. It leads to a simple formula including the model parameters, and an indirect adaptive controller can be designed based on the analytical formula. Simulation results indicate a better performance for the tuned controller. Finally, experimental tests are performed to show the effectiveness of the proposed methodologies. | ||
| کلیدواژه‌ها | ||
| Adaptive Model Predective Control؛ Simplified Model Predective Control؛ Tunning | ||
| عنوان مقاله [English] | ||
| Adaptive Simplified Model Predictive Control with Tuning Considerations | ||
| نویسندگان [English] | ||
| A.S. Ashtari1؛ A. Khaki Sedigh2 | ||
| 1MSc. Student, APAC Research Group, Industrial Control Center of Excellence, K.N.Toosi University of Technology, Tehran, Iran | ||
| 2Professor, APAC Research Group, Industrial Control Center of Excellence, K.N.Toosi University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| Model predictive controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly calculated. The proposed method is validated via simulations for both slow and fast time varying systems. Simulation results indicate the controller ability for tracking references in the presence of plant’s time varying parameters. In addition, an analytical tuning method for adjusting prediction horizon is proposed based on optimization of the objective function. It leads to a simple formula including the model parameters, and an indirect adaptive controller can be designed based on the analytical formula. Simulation results indicate a better performance for the tuned controller. Finally, experimental tests are performed to show the effectiveness of the proposed methodologies. | ||
| کلیدواژه‌ها [English] | ||
| Adaptive Model Predective Control, Simplified Model Predective Control, Tunning | ||
| مراجع | ||
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[1] Richalet, J. (1993). Industrial applications of model based predictive control. Automatica, 29(5), 1251-1274. [2] Qin, S. J., & Badgwell, T. A. (2003). A survey of industrial model predictive control technology. Control engineering practice, 11(7), 733-764. [3] Morari, M., & Lee, J. H. (1999). Model predictive control: past, present and future. Computers & Chemical Engineering, 23(4), 667-682. [4] Cutler, C. R., & Ramaker, B. L. (1980, August). Dynamic matrix control-a computer control algorithm. In Proceedings of the joint automatic control conference (Vol. 1, pp. Wp5-B). Piscataway, NJ: American Automatic Control Council. [5] Gupta, Y. P. (1993). A simplified predictive control approach for handling constraints through linear programming. Computers in industry, 21(3), 255-265. [6] Abou-Jeyab, R. A., Gupta, Y. P., Gervais, J. R., Branchi, P. A., & Woo, S. S. (2001). Constrained multivariable control of a distillation column using a simplified model predictive control algorithm. Journal of Process Control, 11(5), 509-517. [7] Abou-Jeyab, R. A., & Gupta, Y. P. (2001). Constrained multivariable control of fluidized catalytic cracking process using linear programming. Chemical Engineering Research and Design, 79(3), 274-282. [8] Gupta, Y. P. (1993). Characteristic equations and robust stability of a simplified predictive control algorithm. The Canadian Journal of Chemical Engineering, 71(4), 617-624. [9] Fukushima, H., Kim, T. H., & Sugie, T. (2007). Adaptive model predictive control for a class of constrained linear systems based on the comparison model. Automatica, 43(2), 301-308. [10] Kim, J. S. (2010, October). Recent advances in adaptive MPC. In Proceedings of the International Conference on Control Automation and Systems (pp. 218-222). [11] Kim, J. S., Yoon, T. W., Shim, H., & Seo, J. H. (2008). Switching adaptive output feedback model predictive control for a class of input-constrained linear plants. IET Control Theory & Applications, 2(7), 573-582. [12] Bagheri, P., Khaki Sedigh, A. (2013). Analytical approach to tuning of model predictive control for first-order plus dead time models. IET Control Theory & Applications, 7(14), 1806-1817. [13] Bagheri, P., Khaki-Sedigh, A. (2010). An ANOVA Based Analytical Dynamic Matrix Controller Tuning Procedure for FOPDT Models. Amirkabir International Journal of Modeling, Identification, Simulation and Control, Vol. 42, No. 2. | ||
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