## 4 Experiments
### Experimental Results
When it is greater than 4.5, the vibration reduction performance of the system starts to decline sharply, and the vibration amplitude of the platform is constant and amplified compared with the initial value. When the FBC parameter increases to above 6, the system is in an unsteady state. Conversely, when the parameter decreases monotonously from 6 to 4.5, the system is still unstable; when it is less than 3, the system gradually returns to the stable state. In Figure 13, the results of the multi-parameter self-learning experiment are shown when the initial proportional control parameters P of the FBC and FFC are both 0. From Figure 13a-f, the optimal parameter of FBC is in the range of [2.0, 3.0], and the FFC parameter is located in [\(-\)5.0, \(-\)4.0]. The results show that the parameters learned simultaneously have good consistency with the values learned separately. Figure 12: Scatter diagram of state reward and feedback proportional parameter. Figure 13: Experimental results of multi-parameter self-learning in DDPG: (**a–e**) are the learning curves of the 0/40/80/120/140 th episode, respectively; (**f**) are the curves of the whole self-learning process. Figure 14 is the 3D projected scatter diagram of state reward and the multiple parameters. The red circles in the figure indicate the area where the optimal values are located, and the identification results are consistent with the final convergence values of the learning curves. Due to the high-order, nonlinearity, and strong coupling characteristics of the actual system, its dynamic characteristics are very complex, and it is difficult to obtain accurate system control parameters. Nevertheless, the above experiments show that a small optimal value interval can be determined. Based on the DDPG self-learning results above, which take the integral area of the difference between the passive and active vibration transmissibility curves as the reward function, the vertical FBC parameter of the system is selected as 2.5 and the FFC parameter as -4.0. Figure 15 shows the comparison results of vibration transmissibility curves about internal geophones and external VSE-16D sensors under passive, FFC, FBC, and hybrid control. The curves in the figure show that the decoupling calculation value of the internal distributed sensors, which can correctly reflect the actual vibration of the system, has good consistency with the direct test value of the external sensors. The experimental results show that the application of the DDPG algorithm in active vibration control is feasible and effective. Through the DDPG control method, the influence of the control parameters on the system performance within the set range can be analyzed. When the system structure and  
Figure 14: The learning results: (**a**) 3D scatter diagram of state reward and multiple parameters; (**b**) the projection of (**a**) on the opposite of the “Y” direction; (**c**) the projection of (**a**) in the “X” direction. Figure 15: Contrasts between the transmissibility curves of internal geophones and external VSE-16D sensors under optimal learning parameters: Passivity, FFC, FBC, hybrid control (FBC + FFC). payload change, the agent can repeat the learning process until the new optimal control parameters are obtained. In addition, the final learned parameters can be easily solidified into the static flash to achieve high-performance offline operation. The theoretical results of the method proposed in this paper are in good agreement with the experimental measurements and have strong control robustness in the active vibration reduction field. This method has good application prospects in mature commercial products.