Rudolf Kalman
Rudolf Kalman
main contribution
develops Kalman filtering and modern control theory to provide basic methods for navigation, state estimation and process control.
Biographical or institutional source material ↗’s relevance in technology cases
The following cases cite their theories, methods or industry contributions; the historical basis does not mean that I directly participated in or invented modern products alone.
ABB IRB 6700 industrial robot ↗
Link geometry, joint drive and rigid body dynamics determine the reachable space and load; repeated positioning and trajectory smoothing also depend on the control system, structural flexibility and calibration.
Emerson DeltaV distributed control system ↗
feedback control operates in a closed loop based on measurements, dynamic models and actuators. System stability and disturbance suppression need to consider time delays, couplings and constraints; functional safety is an independent design issue.
Priva Connext greenhouse environment control ↗
Temperature and humidity, radiation, ventilation and crop transpiration interact with each other. The sensor position and time delay determine the feedback quality; the overall number of air changes cannot represent all local microclimates.
John Deere See & Spray precise spraying ↗
image recognition, vehicle motion estimation and nozzle timing need to be closely coordinated, and the recognition performance is affected by lighting, crop and weed appearance; usage savings need to be evaluated according to specific scenarios.