Model predictive control: the challenge of uncertainty

The importance of model predictive control derives primarily from its industrial success. Theory has contributed to the development of model predictive control mainly in its discovery of conditions that ensure closed-loop stability. Indeed, there is now a rich collection of of useful and interesting results on stability that we summarize because of their relevance to uncertainty, the main topic of this address. While stability is, not the only topic of concern to theoreticians and practitioners alike, progress on other fronts, such as robustness to model error, state estimation error and exogenous disturbances has been slow; we discuss the issues for interesting reasons. The main challenge facing model predictive control is the development of techniques for reducing the inherent complexities in feedback model predictive control