Nonlinear model predictive control of pneumatic conveying and drying process

Paper deals with development and implementation of nonlinear model predictive control (NMPC) techniques to determine optimal temperature and flow control strategies for a scale down industrial pneumatic conveying and drying system in order to achieve an improved drying characteristics and quick transportation of materials. In this paper predictive control approach is employed using a multivariable Hammerstein model structure. The input nonlinearities involves with the multivariable Hammerstein model are transformed into polytopic description in order to ease the constraint handling in NMPC design. The performance of the predictive controller is studied with stringent design specifications (constraints) and dynamic operating condition of the process under investigation. The results show the effectiveness and precision of the proposed control to track reference signals and a significantly better closed-loop response with the use of minimum control effort.

[1]  Er-Wei Bai,et al.  Iterative identification of Hammerstein systems , 2007, Autom..

[2]  J. Richalet,et al.  Model predictive heuristic control: Applications to industrial processes , 1978, Autom..

[3]  H. Bloemen,et al.  Model-based predictive control for Hammerstein systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[4]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[5]  David Mills,et al.  Pneumatic Conveying Design Guide , 1989 .

[6]  Johannes P. Schlöder,et al.  Real-Time Optimization for Large Scale Processes: Nonlinear Model Predictive Control of a High Purity Distillation Column , 2001 .

[7]  Chiranjib Koley,et al.  Modelling simulation and validation of duct air heating system , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[8]  Sirish L. Shah,et al.  Constrained nonlinear MPC using hammerstein and wiener models: PLS framework , 1998 .

[9]  Wenfu Wu,et al.  Parameters Online Detection and Model Predictive Control during the Grain Drying Process , 2013 .

[10]  Feng Ding,et al.  Identification methods for Hammerstein nonlinear systems , 2011, Digit. Signal Process..

[11]  Biplab Satpati,et al.  Modeling Identification and Control of an Air Preheating Furnace of a Pneumatic Conveying and Drying Process , 2014 .

[12]  A. Palazoglu,et al.  Nolinear model predictive control using Hammerstein models , 1997 .

[13]  Mustafa Türker,et al.  Dynamic Neural-Network-Based Model-Predictive Control of an Industrial Baker's Yeast Drying Process , 2008, IEEE Transactions on Neural Networks.

[14]  Junfei Qiao,et al.  Nonlinear Model-Predictive Control for Industrial Processes: An Application to Wastewater Treatment Process , 2014, IEEE Transactions on Industrial Electronics.

[15]  W. Greblicki,et al.  Identification of discrete Hammerstein systems using kernel regression estimates , 1986 .

[16]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[17]  H. Bloemen,et al.  Model-based predictive control for Hammerstein?Wiener systems , 2001 .

[18]  Jie Bao,et al.  Model Predictive Control of Hammerstein Systems with Multivariable Nonlinearities , 2007 .

[19]  Liuping Wang,et al.  Identification of Continuous-time Models from Sampled Data , 2008 .

[20]  Susanne Zaglauer,et al.  Design of Experiments for nonlinear dynamic system identification , 2011 .

[21]  Christopher G. J. Baker,et al.  Industrial Drying of Foods , 1997 .

[22]  K. Narendra,et al.  An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .