Reinforcement Learning-Based Online Drive Control for MEMS Gyroscopes

MEMS gyroscopes are highly sensitive to environmental disturbances such as time-varying temperature, vibration, and random noise.To address these issues, this work proposes an online intelligent control method based on reinforcement learning, implemented using the proximal policy optimization (PPO) algorithm. A gyroscope dynamical model with error terms is first established, followed by the formulation of the control problem as a Markov decision process. An online control framework is then developed, in which control actions are adaptively adjusted according to the observed system states. A hardware-in-the-loop (HIL) experimental platform is constructed to enable real-time control of a physical gyroscope. Experimental results show that, during the startup phase, the proposed method reduces the drive-voltage overshoot by 90% compared with a PID controller. During long-term steady operation, the bias stability is improved by 26%. These results demonstrate that RL-based online control can enhance both the transient response and long-term stability of MEMS gyroscopes under real-world disturbances.