Peak-Seeking Control Method

Peak-seeking algorithms optimize physical processes in real time and are widely used throughout a variety of industries. However, measuring associated parameters in changing conditions, and responding to them appropriately, is difficult because the measurements are typically distorted by noise. This technology addresses that problem by employing a time-varying Kalman filter. The filter is an algorithm previously developed to estimate unknown parameters within systems that are intrinsically random and uncertain. The Kalman filter is excellent at finding estimates when it encounters noisy signals. As a minimal-variant filter, it inherently produces the best estimates of a function with the smallest amount of variation from the true value. Thus, the filter can help accurately determine the optimal coordinates of the peak- seeking function as conditions in the environment change.