## 4 Simulation Analysis
In the observation of \(x_{3}\), the initial maximum observation error of NDO reaches +6000\(\sim\)\(-\)2300 and gradually stabilizes to +30\(\sim\)-30, while the error of GSTDO is almost 0. In the observation of \(d\), the initial maximum observation error of NDO reaches \(-\)1, and the true value is observed after 0.3 s, but there is a delay of 0.1 s. The initial maximum observation error of GSTDO is +0.15\(\sim\)--1, but it converges to the true value at 0.15 s. In the observation of \(q\), the initial maximum observation error of NDO reaches \(-\)2, and the true value is observed after 0.3 s, but there is a delay of 0.1 s. The initial maximum observation error of GSTDO is +0.3\(\sim\)--2, but it converges to the true value in 0.3 s. In summary, NDO has an obvious observation error at the beginning, it takes some time to approach the true value and there will always  
Figure 4:0 Comparison of the tracking error between GSTDO-GSTBSMC and NDO-SMC in Experiment 2. Figure 4:1 Control input for GSTDO-GSTBSMC u in Experiment 2.  
be an observation delay. The GSTDO proposed in this paper can always maintain an observation error of almost zero, and the convergence time is lower. In summary, the reliability of the control method proposed in this paper can be proved. Third, in order to further verify the universal applicability of the control algorithm, the frequency of the control target signal and disturbances is changed. Electro-hydraulic servo systems are real. In the actual engineering background, there will be very large disturbances. Therefore, in this comparative experiment, disturbances are amplified to an order of magnitude with the control target, which can verify the anti-interference ability of the controller. The sinusoidal signal \(\sin 3t\) is selected as the control target, the mismatched disturbance \(d\) is \(\cos 2t\) and the matched disturbance \(q\) is \(\sin 2t+1\). The tracking performance of the two controllers under the same conditions is shown in Figure 15, and the tracking error curve is shown in Figure 4:2 It is obvious that GSTDO-GSTSMC has better tracking performance. Compared with NDO-SMC, the maximum initial tracking error is +0.15--0.32\({}^{\circ}\), and the maximum initial tracking error of GSTDO-GSTSMC is +0.01--0.01\({}^{\circ}\), which can approach the desired curve in a short time of 0.3 s. In the case of large disturbances, the error of NDO-SMC will increase significantly, and the controller proposed in this paper can still guarantee good tracking performance. The control input of GSTDO-GSTSMC is normalized as shown in Figure 17 below. Figure 4:3 Comparison of the observational effects of GSTDO and NDO in Experiment 2. Figure 4:4 Comparison of the observation error of GSTDO and NDO in Experiment 2. Figure 4:5 Comparison of the tracking results of GSTDO-GSTBSMC and NDO-SMC in Experiment 3. The observation performance of the observer in the above two control methods is shown in Figures 18 and 19. In the experiment, \(x_{2}\), \(x_{3}\), mismatched perturbation \(d\) and matched perturbation \(q\) were observed, respectively. It can be seen from the observation results that the GSTDO proposed in this paper has better observation performance. In the observation of \(x_{2}\), the initial maximum observation error of NDO reaches +7.5\(\cdots\)\(-\)6.7 and gradually stabilizes to +0.3\(\cdots\)\(-\)0.3, and the GSTDO error is almost 0. In the observation of \(x_{3}\), the initial maximum observation error of NDO reaches +2150\(\cdots\)\(-\)1100 and gradually stabilizes to +55\(\cdots\)\(-\)45, while the error of GSTDO is almost 0.