Abstract
<title>Abstract</title> <p>Dynamic matrix equations underpin real-time estimation, optimization and control in autonomous mobile robot (AMR) systems, where coefficient matrices must be repeatedly updated in response to changing sensory inputs and operating conditions. Memristor-based analogue computing-in-memory offers an energy-efficient approach to matrix operations, but its use in dynamic workloads is limited by analogue-computing errors and the high overhead of accurate matrix reprogramming. Here we report a memristor-based analogue iterative computing system (mAIC) for efficient and accurate dynamic matrix-equation solving. Using characterized memristor predictive models, we develop an analogue-compensated predictive one-step programming (AC-PoP) scheme that maintains accurate matrix updates without iterative write–verify operations, reducing update energy and latency by 101-fold and 345-fold, respectively. Integrated with a continuous-time analogue feedback solver and implemented using foundry-fabricated memristor macros, mAIC achieves software-comparable accuracy in representative SLAM and real-world robotic path-planning workloads. Compared with an NVIDIA Jetson platform, it provides 4.4-fold lower processing latency and 627.9-fold lower energy consumption. These results advance memristor-based computing from predominantly static matrix acceleration towards accurate and dynamically reconfigurable analogue computing.</p>