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We present formulation and open-source tools to achieve in-material modelpredictive control of sensor/actuator systems using learned forward kinematicsand on-device computation. Microcontroller units (MCUs) that compute theprediction and control task while colocated with the sensors and actuatorsenable in-material untethered behaviors. In this approach, small parameter sizeneural network models learn forward kinematics offline. Our open-sourcecompiler, nn4mc, generates code to offload these predictions onto MCUs. ANewton-Raphson solver then computes the control input in real time. We firstbenchmark this nonlinear control approach against a PID controller on amass-spring-damper simulation. We then study experimental results on twoexperimental rigs with different sensing, actuation and computational hardware:a tendon-based platform with embedded LightLace sensors and a HASEL-basedplatform with magnetic sensors. Experimental results indicate effectivehigh-bandwidth tracking of reference paths (greater than or equal to 120 Hz)with a small memory footprint (less than or equal to 6.4% of flash memory). Themeasured path following error does not exceed 2mm in the tendon-based platform.The simulated path following error does not exceed 1mm in the HASEL-basedplatform. The mean power consumption of this approach in an ARM Cortex-M4fdevice is 45.4 mW. This control approach is also compatible with TensorflowLite models and equivalent on-device code. In-material intelligence enables anew class of composites that infuse autonomy into structures and systems withrefined artificial proprioception.

Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems