A predictive control economic optimiser and constraint governor based on neural models

This paper discusses a Model Predictive Control (MPC) structure for economic optimisation of nonlinear technological processes. It contains two parts: an MPC economic optimiser/constraint governor and an unconstrained MPC algorithm. Two neural models are used: a dynamic one for control and a steady-state one for economic optimisation. Both models are linearised on-line. As a result, an easy to solve on-line one quadratic programming problem is formulated. Unlike the classical multilayer control system structure, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Robert B. Fisher,et al.  Incremental One-Class Learning with Bounded Computational Complexity , 2007, ICANN.

[3]  Piotr Tatjewski,et al.  Iterative Algorithms For Multilayer Optimizing Control , 2005 .

[4]  Maciej Ławryńczuk,et al.  Neural models in computationally efficient predictive control cooperating with economic optimisation , 2007 .

[5]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[6]  Jacek M. Zurada,et al.  Artificial Intelligence and Soft Computing - ICAISC 2008, 9th International Conference, Zakopane, Poland, June 22-26, 2008, Proceedings , 2008, ICAISC.

[7]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[8]  Aldo Cipriano,et al.  Optimization of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants , 2001 .

[9]  Maciej Lawrynczuk,et al.  Efficient Predictive Control Integrated with Economic Optimisation Based on Neural Models , 2006, ICAISC.

[10]  Piotr Tatjewski,et al.  Advanced Control of Industrial Processes: Structures and Algorithms , 2006 .

[11]  John R. Beaumont,et al.  Control and Coordination in Hierarchical Systems , 1981 .

[12]  Junghui Chen,et al.  Applying neural networks to on-line updated PID controllers for nonlinear process control , 2004 .

[13]  Mohamed Azlan Hussain,et al.  Review of the applications of neural networks in chemical process control - simulation and online implementation , 1999, Artif. Intell. Eng..

[14]  Aldo Cipriano,et al.  Optimisation of Industrial Processes at Supervisory Level , 2002 .

[15]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[16]  Piotr Tatjewski,et al.  Soft computing in modelbased predictive control footnotemark , 2006 .

[17]  Maciej Ławryńczuk,et al.  A Family of Model Predictive Control Algorithms With Artificial Neural Networks , 2007, Int. J. Appl. Math. Comput. Sci..