Control of complex dynamic nonlinear loading process for electromagnetic mill

• Autorzy: Ogonowski Szymon , Bismor Dariusz , Ogonowski Zbigniew

Identyfikatory

DOI

10.24425/acs.2020.134674

• Języki publikacji: EN

Abstrakty

EN

Electromagnetic mill installation for dry grinding represents a complex dynamical system that requires specially designed control system. The paper presents model-based predictive control which locates closed loop poles in arbitrary places. The controller performs as gains cheduling prototype where nonlinear model – artificial recurrent neural network, is parameterized with additional measurements and serves as a basis for local linear approximation. Application of such a concept to control electromagnetic mill load allows for stable performance of the installation and assures fulfilment of the product quality as well as the optimization of the energy consumption.

Słowa kluczowe

EN

predictive control; pole placement; nonlinear dynamics; neural modelling; electromagnetic mill

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2020
• Tom: Vol. 30, No. 3
• Strony: 471--500
• Opis fizyczny: Bibliogr. 33 poz., fot., rys., wykr., wzory

Twórcy

autor

Ogonowski Szymon

• Department of Measurements and Control Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland

autor

Bismor Dariusz

• Department of Measurements and Control Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland

autor

Ogonowski Zbigniew

• Department of Measurements and Control Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland

Bibliografia

• [1] C. Shang: Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research, Springer Theses. 2018.
• [2] M. J. Grimble: Industrial Control System Design, Wiley, 2001.
• [3] M. C. Fuerstenau and K. N. Han (Eds.): Principles of Mineral Processing, SME, 2003.
• [4] T. J. Napier-Munn, S. Morrell, and R. D. Morrison: Mineral Comminution Circuits: Their Operation and Optimization, Julius Kruttschnitt Mineral Research Centre, University of Queensland. 1996.
• [5] T. Tumidajski, E. Kasinska-Pilut, T. Gawenda, Z. Naziemiec and R. Pilut: Investigation of grinding process energy consumption and grindability of lithologic components of Polish copper ores, Miner. Resour. Manag., 26 (2010), 61–72.
• [6] D. Hodouin: Methods for automatic control, observation, and optimization in mineral processing plants, Journal of Process Control, 21 (2011), 211–225.
• [7] J. Kolacz and K. L Sandvik: Ultrafine grinding in an air-swept ball mill circuit, Int. J. Miner. Process., 44–45 (1996), 361–371.
• [8] J. Richalet, A. Rault, J. L. Testud and J. Papon: Model algorithmic controlof industrial processes, IFAC Proceedings Volumes, 10(16) (1977), 103–120.
• [9] C. R. Cutler and B. L. Ramaker: Dynamic matrix control – a computer control algorithm, Proceedings Joint Automatic Control Conference, San Francisco, CA, USA, (1980).
• [10] D. W. Clarke, C. Mohtadi and P. S. Tuffs: Generalized predictive control. Part I: The basic algorithm. Automatica, 23(2) (1987), 137–148. Generalized predictive control. Part II: Extensions and interpretations, Automatica, 23(2) (1987), 149–160.
• [11] D. Q. Mayne and H. Michalska: Receding horizon control of nonlinear systems, IEEE Transactions on Automatic Control, 35 (1990), 814–824.
• [12] R. Bitmead, M. Gevers and V. Wertz: Adaptive Optimal Control theThinking Man’s GPC, Prentice-Hall, Englewood Cliffs, NJ, 1990.
• [13] Z. Ogonowski: Discrete Predictive Control with the use of Weighting Function, PhD dissertation. Silesian Unversity of Technology, 1987 (in Polish).
• [14] D. Bismor, K. Jablonski, T. Grychowski and S. Nas: Hardware-In-the-Loop Simulations of a GPC-Based Controller in Different Types of Buildings Using Node-RED, Advanced, Contemporary Control, 1018–1029, Springer International Publishing, 2020. ISBN: 978-3-030-50935-4, doi:10.1007/978-3-030-50936-1.
• [15] Y. Wang and S. Boyd: Fast model predictive control using online optimization, IEEE Transactions on Control Systems Technology, 18(2), (2010), 267–278.
• [16] R. Nebeluk and P. Marusak: Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors, Archives of Control Sciences, 30(2), (2020), 325–363.
• [17] A. Bemporad, M. Morari, V. Dua and E. Pistikopoulos:The explicit linear quadratic regulator for constrained systems, Automatica, 38(1), (2002), 3–20.
• [18] M. Lawrynczuk: Computationally Efficient Model Predictive Control Algorithms: a Neural Network Approach, Studies in Systems, Decision and Control, 3 Springer, Cham, 2014.
• [19] M. Lawrynczuk: Nonlinear state-space predictive control with on-line linearisation and state estimation, International Journal of Applied Mathematics and Computer Science, 25(4), (2015), 833–847.
• [20] K. Slawinski, M. Gandor, K. Knas, B. Balt and W. Nowak Electromagnetic mill and its application for coal drying process, Rynek Energii, 1 (2014), 140–150.
• [21] M. Pawelczyk, Z. Ogonowski, S. Ogonowski, D. Foszcz, D. Saramak,T. Gawenda and D. Krawczykowski: Method for Dry Grinding in Electromagnetic Mill. Patent PL413041, 06.07.2015.
• [22] S. Ogonowski, Z. Ogonowski and M. Pawelczyk: Model of the air stream ratio for an electromagnetic mill control system. In Proceedings of the 21st International Conference on Methods and Models in Automation and Robotics (MMAR 2016), Miedzyzdroje, Poland, (2016), 901–906. doi: 10.1109/MMAR.2016.7575257.
• [23] M. Wolosiewicz-Glab, S. Ogonowski, D. Foszcz and T. Gawenda: Assessment of classification with variable air flow for inertial classifier in dry grinding circuit with electromagnetic mill using partition curves, Physicochem. Probl. Miner. Process., 54(2018), 440–447, doi:10.5277/ppmp1867.
• [24] S. Ogonowski, Z. Ogonowski and M. Pawelczyk: Multi-objective andmulti-rate control of the grinding and classification circuit with electromagnetic mill. Appl. Sci., 8 (2018), 506, doi:10.3390/app8040506.
• [25] A. Casali, G. Gonzalez, F. Torres, G. Vallebuona, L. Castelli and P. Gimenez: Particle size distribution soft-sensor for a grinding circuit, Powder Technology, 99 (1998), 15–21. doi:10.1016/S0032-5910(98)00084-9.
• [26] A. K. Pani and H. K. Mohanta: Soft sensing of particle size in a grinding process: Application of support vector regression, fuzzy inference and adaptive neuro fuzzy inference techniques for online monitoring of cement fineness. Powder Technology, 264 (2014), 484–497, doi:10.1016/j.powtec.2014.05.051.
• [27] S. Ogonowski, Z. Ogonowski, M. Swierzy and M. Pawelczyk: Control system of electromagnetic mill load, 25th International Conference on Systems Engineering (ICSEng), Las Vegas, NV, USA, (2017), 69–76, doi:10.1109/ICSEng.2017.23.
• [28] M. Pawelczyk, Z. Ogonowski and S. Ogonowski: Control of electromagnetic mill working chamber load in pneumatic transport circuit. Polish Patent Application P.421160, 15.03.2017.
• [29] H. J. Nussbaumer: Fast Fourier Transform and Convolution Algorithms, Springer Verlag, New York, 1982.
• [30] J. Moscinski and Z. Ogonowski (Ed.): Advanced Control with Matlab and Simulink, Prentice Hall Inc. 1995.
• [31] Z. Ogonowski: Application of predictive control to nonlinear MIMO systems, IFAC Work-shop on New Trends in Design of Control Systems, Smolenice, Slovak Republic (1994).
• [32] R. J. Evans, A. Cantoni and K. J. Ahmed: Envelope-constrained filter with uncertain input ,Circuits Syst. Signal Processing, 2(2), (1983), 131–154.
• [33] A. Niederlinski, J. Moscinski and Z. Ogonowski: Adaptive Control, PWN, Warsaw, 1995 (in Polish).