We developed a computationally efficient nonlinear control strategy for constrained multivariable processes. The proposed method is a novel combination of feedback linearizing control and linear model predictive control. First the unconstrained nonlinear system is feedback linearized. Then a constraint mapping procedure is used to transform the actual input constraints into corresponding constraints on the feedback linearized system. The resulting constrained linear system is used to design a linear model predictive controller. Our initial work focused on basic controller design issues and simulation studies using a highly nonlinear chemical reactor model. We developed an analogous controller design method for discrete-time systems that is more amenable to rigorous mathematical analysis. The continuous-time formulation was extended to multivariable processes via application to a free-radical polymerization reactor model.

Funding: National Science Foundation (CTS-9501368) and ExxonMobil Chemical

Student: Michael J. Kurtz (Ph.D.)

Publications:

1. Kurtz, M. J. and M. A. Henson, "Input-Output Linearizing Control of Constrained Nonlinear Processes," Journal of Process Control, 7, 3-17 (1997). [PDF]
2. Kurtz, M. J. and M. A. Henson, "Feedback Linearization of Discrete-Time Nonlinear Systems with Input Constraints," International Journal of Control, 70, 603-616 (1998). [PDF]
3. Kurtz, M. J. and M. A. Henson, "State and Disturbance Estimation for Nonlinear Systems Affine in the Unmeasured Variables," Computers and Chemical Engineering, 22, 1441-1459 (1998). [PDF]
4. Kurtz, M. J., G.-Y. Zhu and M. A. Henson, "Constrained Output Feedback Control of a Multivariable Polymerization Reactor," IEEE Transactions on Control System Technology, 8, 87-97 (2000). [PDF]