# Advanced Servo Control and AI Integration in 3-DoF Platforms for Enhanced Simulation Interactivity
## 2 Control System
### Proposed Servo Control  
Servo motors often require the use of gearboxes, racks, or lead screws to generate actual mechanical motion. Due to the need for precise positioning or speed control in simulators, servo motors may experience discontinuities in rotation speed during instantaneous  
Figure 3: Closed-loop angle control system block diagram. Figure 2: Closed-loop speed control system block diagram. reversals due to frictional effects, leading to performance degradation. Past research [18] indicates that friction is often nonlinear and varies with changes in rotation speed, making it difficult to establish an accurate model through experimental means. Therefore, in scenarios involving reciprocating motion, it is proposed to design an ILC to compensate for nonlinear factors in the mechanism, aiming to improve the performance of reciprocating motion in the gearbox. The internal speed or position bandwidth of the driver is set to 1 kHz, and the frequency of packet transmission from the visual effects computer to the digital signal processor network is set to 50 Hz. As motors engage in reciprocating motion, nonlinear frictional forces periodically arise. ILC theory can adaptively adjust to control objectives and conditions, providing an effective and practical solution to repetitive tracking control and repetitive interference problems. Therefore, in this section, ILC is employed to compensate for frictional interference. Typically, ILC is used to improve transient system performance, such as eliminating periodic nonlinear disturbances. The compensation amount of the ILC adjusts using the system's error amount, without needing to know the various parameters of complex systems beforehand. It is suitable for complex systems that are time-varying, nonlinear, and difficult to model [19]. As shown in Figure 4, the control force from the previous learning iteration, stored in the internal memory of the digital signal processor, is compared with the desired trajectory to calculate the error value for updating the control force. Hence, the concept of ILC involves using control information from the previous iteration to generate the control input for the current iteration, which is then transmitted to the controlled system for execution. D-type iterative learning control (DILC) is represented as follows:  
\[u_{i}(n)=u_{i-1}(n)+L[(y_{d}(n+1)-y_{d}(n))-(y_{i-1}(n+1)-y_{i-1}(n))] \tag{18}\]  
where \(n\) is the sampling time and \(n\) = 0, 1,...N. \(N\) is the total number of iterations to be learned and \(0<\alpha<1\) is the forgetting factor, \(u_{i}(n)\) is the control input for this learning iteration, \(u_{i-1}(n)\) is the control input from the previous learning iteration, \(L\) is the learning gain constant, which is usually set as a fixed constant, \(y_{d}\) is the desired ideal output, and \(y_{i-1}\) is the controlled system output from the previous learning iteration. Since the transmission components of this system consist of servo motors and RV reducers, and considering the need for simultaneous three-axis operation in the simulator, RV reducers with high rigidity, smooth transmission, and high load capacity have been selected. During servo driving in forward and reverse rotation or slow movement processes, frictional forces can cause uneven rotation speeds. Traditional PID controller parameters, being fixed, cannot provide the system with appropriate gains. Therefore, this paper proposes an online calculation of the pulse frequency that needs to be input to the driver using iterative learning control (ILC). By adjusting the pulse frequency, the transient, steady-state, and loading characteristics of the system can be improved. Additionally, the weight of the previous learning control force \(\alpha\) in Equation (18) is increased. The D-type ILC is modified to incorporate a proportional term to enhance transient responses and handle uncertainties in external variations. Thus, the PD-type iterative learning control (PDILC) equation is formulated as follows:  
Figure 4: Iterative learning control diagram. \[