A system may be subject to multiple factors that influence the system, causing control of the system to be difficult. It is desirable that the multiple factors be taken into consideration when calculating control parameters such that system operation is stable and meets performance requirements. However, the computational resources allocated to the system may be incapable of considering enough of the influencing factors necessary to meet stability and performance requirements during operation.
One option for overcoming the issue of limited computational resources is to allocate additional resources. However, this option may be prohibitively expensive in terms of space, weight, or price, for example. Additionally, computational resources may not exist that are fast enough to react for systems in which the conditions or state of the system change rapidly, or for systems in which it is necessary to have the ability to rapidly change control parameters. Thus, simply adding more computational resources to the system may be insufficient.
Many systems with limited computational resources address the issue by designing the system for a certain set of expected conditions. However, if actual conditions are not as expected, system stability and/or performance may suffer. Thus it is preferable to have the capability to optimize control for stability and performance during system operation.
A solution for optimizing control during system operation without overburdening system computational resources reduces the number of calculations required during system operation.