Efficient nonlinear predictive control of a biochemical reactor using neural models.
This paper describes the application of artificial neural networks to modelling and control of a continuous fermentor. A computationally efficient nonlinear model predictive control (MPC) algorithm with nonlinear prediction and linearisation (MPC-NPL) which needs solving on-line a quadratic programming problem is developed. It is demonstrated that the algorithm results in closed-loop control performance similar to that obtained in nonlinear MPC, which hinges on full on-line non-convex optimisation. The computational complexity of the MPC-NPL algorithm is shown, control accuracy and robustness are also demonstrated in the case of noisy measurements and disturbances affecting the process.
Lawryńczuk
Maciej
M
Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warszawa, Poland. M.Lawrynczuk@ia.pw.edu.pl
D018407:Bacterial Physiological Phenomena; D001704:Biopolymers / Q000378:metabolism*; D019149:Bioreactors / Q000382:microbiology*; D018929:Cell Culture Techniques / Q000295:instrumentation / Q000379:methods; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics
D001704:Biopolymers