A Long Short-Term Memory with Deep Q-Learning and Bayesian Optimization Control Framework for Robust Position Regulation of Uncertain Electro-Hydraulic Actuators

The existence of friction, flow–pressure coupling, load variations, internal leakage, and other fluidic nonlinearities makes it challenging to design classical model-based controllers for servo-valve-driven electro-hydraulic actuators (EHAs). To address these issues and achieve high-precision output tracking, this paper proposes a learning-based control framework that integrates Long Short-Term Memory with Deep Q-Learning and Bayesian Optimization (BO–LSTM–DQN) for high-precision position regulation of servo-valve-driven EHAs. In this framework, the LSTM augments Q-learning with temporal memory to first establish and infer hidden dynamics from measured sequences. Meanwhile, Bayesian Optimization is used to automatically optimize key hyperparameters to improve convergence and policy stability, without requiring manual trial-and-error. Additionally, a constraint-aware reward function is formulated to encode realistic servo-valve operational limits and satisfy motion stability requirements. The effectiveness of the proposed control strategy is verified through comparative simulations with PID– and BO–DQN-based controllers under different operating scenarios, subject to load disturbance and internal leakage. Furthermore, to evaluate the robustness of the proposed controller against parametric uncertainties, extensive Monte Carlo simulations are conducted with simultaneous variations of up to 50% in five key system parameters. The results demonstrate that the proposed BO–LSTM–DQN framework achieves a significant reduction in Root Mean Square Error (RMSE) by up to 51.79% compared with the conventional PID and maintains superior stability over the optimized DQN baselines, confirming its effectiveness for real-world EHA applications under extreme operating conditions.