PNNL

Abstract

This paper presents the embedded implementation of differentiable predict control (DPC) in a real-time control application with fast dynamics. DPC is a model-based policy optimization method that utilizes automatic differentiation to learn explicit control policies. The laboratory device features an aeropendulum mechatronic system equipped with a low-cost 32-bit microcontroller development board. We aimed to explore the possibilities of utilizing the DPC with linear and nonlinear dynamics models on lean hardware in a setpoint tracking application while satisfying the input and state constraints. The resulting control performance, memory footprint, and execution time are evaluated for different lengths of prediction horizon.