Partial enumeration (PE) is presented as a method for treating large, linear model predictive control applications that are out of reach with available MPC methods. PE uses both a table storage method and online optimization to achieve this goal. Versions of PE are shown to be closed-loop stable. PE is applied to an industrial example with more than 250 states, 30 inputs, and a 25-sample contro...

In the past decade the field of nonlinear model predictive control (NMPC) has witnessed steadily increasing attention from control practitioners. Its popularity comes from the fact that today’s processes need to be operated under much tighter performance specifications while at the same time more and more constraints, stemming for example from environmental and safety considerations, need to be...

Ecological processes frequently occur at multiple spatial scales simultaneously. For example, fires imprint the landscape at a variety of spatial scales, from small areas of high burn intensity due to patchy surface fuels, to large stands within fires that escape conflagration entirely (Fig. 7.1). These types of complex disturbances can increase environmental heterogeneity and thus species dive...

The present sought to draw a comparison between Model Predictive Control performance and two other controllers named Simple PI and Selective PI in controlling large-scale natural gas transport networks. A nonlinear dynamic model of representative gas pipeline was derived from pipeline governing rules and simulated in SIMULINK® environment of MATLAB®. Control schemes were designed to provide a s...

Scientific explanation often requires inferring maximally predictive features from a given data set. Unfortunately, the collection of minimal maximally predictive features for most stochastic processes is uncountably infinite. In such cases, one compromises and instead seeks nearly maximally predictive features. Here, we derive upper bounds on the rates at which the number and the coding cost o...