Study on Control Approaches for Servo Systems Exhibiting Uncertain Time Delays

In response to the uncertainty of delay parameters within the servo control system, an adaptive estimation framework grounded in Bayes–Monte Carlo Markov chain fusion (Bayes-MCMC) is proposed. Subsequently, an uncertain delay estimation model was constructed, and a gain optimization method is put forward. An optimal gain state observer tailored to uncertain delays is derived, and a compound control strategy is established to counteract the delay. Experimental findings demonstrate that the observation error of the optimized observer is effectively mitigated. Compared with Smith and the unoptimized gain compensation system, the phase margin, delay margin and gain margin of the system after the gain optimization are increased by 22.8%, 1 order of magnitude, 23.6% and 13.07%, 1 order of magnitude, 1.12%, respectively. Under the condition of delay uncertainty, the system’s output can closely track the given input. The overshoot is effectively reduced, the system’s output response is expedited, the steady-state error is substantially decreased, and the time to reach the steady-state is shortened by around 12.5%. The system’s performance in both the time domain and the frequency domain is remarkably improved, thereby validating the effectiveness and superiority of the proposed method.