Anytime Control Algorithm: Model Reduction Approach

Abstract

Recently, there has been considerable interest in anytime algorithms for real-time systems. Anytime algorithms are computational models that compromise quality of result for computational time. The tolerance to fluctuating CPU time makes anytime algorithms operationally optimal for real-time task scheduling. A methodology is presented that transforms linear control algorithms into anytime control algorithms. Implementation of a linear control algorithm involves matrix–vector multiplications that require a fixed computational time. Such algorithms fail to compute the controller output if the alloted CPU time is less than required and cannot make use of any excess CPU time that might be available. When implemented as a real-time system, the static nature of the required computational time makes it operationally suboptimal for task scheduling. Linear control algorithms are transformed to anytime control algorithms by switching between controllers of different order. Balanced truncation and residualization are considered as model reduction tools to generate a set of reduced-order controllers, and a switching algorithm is presented that smoothly switches between them to accommodate variation in available computational time.