Peak-Seeking Technology to Optimize Flight, Manufacturing, and Business Operations

Organization: NASA Armstrong

Patent: US-8447443-B1

Reference:­ https://patents.google.com/patent/US8447443

Inventors: John J Ryan, Jason L Speyer

Background:

Inefficiencies in logistics lead to excessive expenditures, missed delivery deadlines, and damaged goods. Thus, optimizing operational efficiency and reducing logistics costs are essential to a company’s success. The performance of futuristic logistic technologies, including drones/UAVs, will be largely influenced by various environmental factors, such as wind, snow, and rain. This technology provides an algorithm that estimates various external parameters and produces a response function with the smallest amount of variation in the environmental conditions

Invention Overview:

Measuring multiple parameters in changing conditions, and responding to them appropriately, is difficult: the measurements are typically distorted by noise. This technology addresses that problem by employing a time-varying Kalman filter

Stage:

Technology Readiness Level 4-Validation in laboratory environment

Application/Differentiator:

This technology provides a fast response time as it adjusts control parameters that exist in response to real-time environmental changes. It takes into account, the impact of weak signals to optimize performance, resulting in significant cost savings. This algorithm could be integrated to various mobility platforms to reduce drag, thereby reducing fuel consumption. Moreover, the algorithm can be helpful in enhancing the performance of drones and electric cars in various environmental conditions, increasing range and reliability

Invention Disadvantages and Possible Mitigation:

The algorithm is as good as the quality of data input. Likewise, this technology is heavily dependent upon external sensors, which increase the likelihood of system malfunction. Hence, this technology should be implemented in conjunction with a setup to validate the quality of incoming data