Nonlinear model predictive control of a cutting process

Nonlinear model predictive control (MPC) of a simulated chaotic cutting process is presented. The nonlinear MPC combines a neural-network model and a genetic-algorithm-based optimizer. The control scheme comprises a process, a model, an optimizer, a controller and a corrector. Neural networks are used to build a nonlinear experimental model of the process which is applied to recursive prediction in MPC. A robust genetic-algorithm-based optimizer is used for the optimization of control trajectories. A neural-network-based controller is included in the control scheme for enhanced optimizer initialization and for autonomous control after the learning period. The nonlinear MPC is applied to control the simulated chaotic cutting process. The dynamics of a cutting process are very complex due to the nonlinear e ects of high order involved. The control objective is to construct an on-line control system capable of improving the quality of the manufactured surface by preventing tool oscillati...

• Type: Journal
• Year: 2002
• Where: IJON
• Authors: Primoz Potocnik, Igor Grabec
• Keywords: Chaotic Cutting Process, Cutting Process, IJON 2002, Nonlinear