Abstract: A machine control system can store model weights determined via machine learning using a training dataset correlating preset hydraulic valve displacements to measured movement parameters of a machine component. The machine control system can receive an input command for the component and machine state data from machine sensors. A control mapping model can use the model weights to map a combination of the input command and the machine state data into a predicted displacement of the hydraulic valve that causes movement of the component in response to the input command.

Abstract: A machine control system can store model weights determined via machine learning using a training dataset correlating preset hydraulic valve displacements to measured movement parameters of a machine component. The machine control system can receive an input command for the component and machine state data from machine sensors. A control mapping model can use the model weights to map a combination of the input command and the machine state data into a predicted displacement of the hydraulic valve that causes movement of the component in response to the input command.

Abstract: A method of determining the real time state of a machine includes receiving acceleration and angular rate of motion measurements from IMU's mounted on components of a machine. Fusing signals received from the IMU's with separate Kalman filter modules by combining an acceleration measurement and an angular rate of motion measurement from each IMU to estimate an output joint angle for the component on which the IMU is mounted. Estimated and measured values of the output joint angle for each component are combined, a kinematic equation is solved to determine a real time value for at least one of position, velocity, and acceleration of the component at successive timesteps, and the determined real time values are applied to control movement of each component.

Abstract: A method of determining the real time state of a machine includes receiving, with at least one processor, from each of a plurality of Inertial Measurement Units (IMU's) mounted on different components of the machine, a time series of signals indicative of acceleration and angular rate of motion measurements for each of the components of the machine on which one or more of the plurality of IMU's are mounted. For each one of the IMU's on a separate component of the machine, the signals received from the IMU are fused with a separate Kalman filter module by combining an acceleration measurement and an angular rate of motion measurement from the IMU to estimate an output joint angle for the component of the machine on which the IMU is mounted.