Spaceborne optomechanical systems face the dual challenges of extreme thermal disturbances and millikelvin-level temperature control precision during orbital operations,demanding robust control strategies.To address t...Spaceborne optomechanical systems face the dual challenges of extreme thermal disturbances and millikelvin-level temperature control precision during orbital operations,demanding robust control strategies.To address the performance limitations of conventional fixed-parameter active disturbance rejection control(ADRC)under complex operating conditions,this work proposes a Qlearning-enhanced adaptive ADRC framework.A thermal-transfer model incorporating multisource disturbances(solar radiation,structural conduction,and contact thermal resistance)is established,coupled with a reinforcement learning-driven parameter optimization mechanism.The ε-greedy policy dynamically adjusts observer bandwidth(ω_(o)∈[0.01,0.2])and controller bandwidth(ω_(c)∈[0.01,0.1])to enable real-time estimation and compensation of total disturbances.Simulation results demonstrate significant improvements over fixed-parameter ADRC and a self-tuning internal model control proportional-integral(SIMC-PI)controller:31.3% and 15.4% reduction in settling time during setpoint responses,respectively;21.8% lower integral absolute error(IAE)than the fixed-parameter ADRC during setpoint step responses;12.7% and 52.5% enhancement in control precision over conventional fixed-parameter and SIMC-PI controllers,respectively,under±10 K periodic and step thermal disturbances.Monte Carlo robustness tests reveal smaller fluctuation ranges of IAE,settling time,and overshoot under±5% parameter perturbations.This methodology establishes a new paradigm for millikelvin-level thermal control in space optical payloads.展开更多
基金The National Key R&D Program of China(No.2022YFB3902902)the National Natural Science Foundation of China(No.52276003).
文摘Spaceborne optomechanical systems face the dual challenges of extreme thermal disturbances and millikelvin-level temperature control precision during orbital operations,demanding robust control strategies.To address the performance limitations of conventional fixed-parameter active disturbance rejection control(ADRC)under complex operating conditions,this work proposes a Qlearning-enhanced adaptive ADRC framework.A thermal-transfer model incorporating multisource disturbances(solar radiation,structural conduction,and contact thermal resistance)is established,coupled with a reinforcement learning-driven parameter optimization mechanism.The ε-greedy policy dynamically adjusts observer bandwidth(ω_(o)∈[0.01,0.2])and controller bandwidth(ω_(c)∈[0.01,0.1])to enable real-time estimation and compensation of total disturbances.Simulation results demonstrate significant improvements over fixed-parameter ADRC and a self-tuning internal model control proportional-integral(SIMC-PI)controller:31.3% and 15.4% reduction in settling time during setpoint responses,respectively;21.8% lower integral absolute error(IAE)than the fixed-parameter ADRC during setpoint step responses;12.7% and 52.5% enhancement in control precision over conventional fixed-parameter and SIMC-PI controllers,respectively,under±10 K periodic and step thermal disturbances.Monte Carlo robustness tests reveal smaller fluctuation ranges of IAE,settling time,and overshoot under±5% parameter perturbations.This methodology establishes a new paradigm for millikelvin-level thermal control in space optical payloads.
