Robust Adaptive Model Predictive Control of Nonlinear Systems

When faced with making a decision, it is only natural that one would aim to select the course of action which results in the “best" possible outcome. However, the ability to arrive at a decision necessarily depends upon two things: a well-defined notion of what qualities make an outcome desirable, and a previous decision1 defining to what extent it is necessary to characterize the quality of individual candidates before making a selection (i.e., a notion of when a decision is “good enough"). Whereas the first property is required for the problem to be well defined, the later is necessary for it to be tractable. The process of searching for the “best" outcome has been mathematically formalized in the framework of optimization. The typical approach is to define a scalar-valued cost function, that accepts a decision candidate as its argument, and returns a quantified measure of its quality. The decision-making process then reduces to selecting a candidate with the lowest (or highest) such measure.