针对定深电液伺服系统内部参数的难确定性与时变性问题, 设计了一种神经网络与滑模变结构相结合的复合控制方法。该方法充分利用滑模变结构控制的强鲁棒性优点对定深电液伺服系统进行控制, 使其能够在外部干扰复杂的环境下进行工作, 并且结合神经网络来减弱滑模变结构控制自身所存在的抖振问题。此外,还利用神经网络对电液伺服系统的内部参数进行辨识并进行在线自适应学习, 能够很好地抑制内部参数摄动, 消除定深电液伺服系统工作时的时变性问题, 保证了系统的控制精度与鲁棒性。

Considering that the internal parameters of the given-depth electro-hydraulic servo system are uncertain and time-varying, a composite control strategy based on Neural Network (NN) and Sliding Mode Control (SMC) is designed to solve the problem. The strategy makes full use of the strong robustness of the SMC for the control of the given-depth electro-hydraulic servo system, enables it to work under complicated external interference environment, and uses the neural network to weaken the buffeting shortcoming of the SMC itself. In addition, the neural network is also used for identification of the internal parameters of the electro-hydraulic servo system and online adjustment, which can well suppress the internal parameter perturbation and eliminate the time-varying problem of the electro-hydraulic servo system, and thus can ensure the control precision and stability of the system.