Predictive Neuromorphic Control of a Scanning Mirror System: An Event-Centric Framework

Abstract

Organizing control around events can reduce sensing and actuation demands while directly linking control decisions to the occurrence and timing of desired system behaviors. This paper proposes a novel event-centric model predictive control (MPC) framework that unifies event sensing, event prediction, and event regulation. Events define the feedback information, prediction horizon, and event-timing objectives, placing them at the center of predictive control design. The framework is developed for a scanning mirror system equipped with a baseline neuromorphic controller composed of spiking neurons that supports sparse actuation. The control objective is to achieve prescribed zero-crossing times and a desired scanning range while minimizing the mechanical energy transferred between the actuator and the mirror. A sampling-based algorithm is presented to obtain suboptimal solutions of the event-centric MPC optimization problem without requiring an explicit event-to-event prediction model. By grounding predictive optimization in events, the proposed framework opens a new direction for MPC design and its integration with neuromorphic control.