Lyapunov-Based Model Predictive Control
In Chap. 2, some basic results on Lyapunov-based control, model predictive control and Lyapunov-based model predictive control of nonlinear systems are first reviewed and then two Lyapunov-based model predictive control (LMPC) designs for systems subject to data losses and time-varying measurement delays are presented. In order to provide guaranteed closed-loop stability results, the constraints that define the LMPC optimization problems as well as the implementation procedures are carefully designed to account for data losses (or asynchronous measurements) and time-varying measurement delays. The presented LMPC designs possess an explicit characterization of the closed-loop system stability region. Using a nonlinear chemical reactor example, it is demonstrated that the presented LMPC approaches are robust to data losses and measurement delays.
KeywordsStability Region Data Loss Continuously Stir Tank Reactor Prediction Horizon Measurement Delay
- 1.Allgöwer, F., & Chen, H. (1998). Nonlinear model predictive control schemes with guaranteed stability. In R. Berber & C. Kravaris (Eds.), NATO ASI on nonlinear model based process control (pp. 465–494). Dordrecht: Kluwer Academic. Google Scholar
- 11.Christofides, P. D., & El-Farra, N. H. (2005). Control of nonlinear and hybrid process systems: Designs for uncertainty, constraints and time-delays. Berlin: Springer. Google Scholar
- 21.Fogler, H. S. (1999). Elements of chemical reaction engineering. Englewood Cliffs: Prentice Hall. Google Scholar
- 40.Khalil, H. K. (1996). Nonlinear systems (2nd ed.). New York: Prentice Hall. Google Scholar
- 59.Maeder, U., Cagienard, R., & Morari, M. (2007). Explicit model predictive control. In S. Tarbouriech, G. Garcia, & A. H. Glattfelder (Eds.), Lecture notes in control and information sciences: Vol. 346. Advanced strategies in control systems with input and output constraints (pp. 237–271). Berlin: Springer. CrossRefGoogle Scholar