## 4 Simulation Results  
In the operational life cycle of a large flotation cell, the electric motor frequently undergoes wear and tear, necessitating periodic replacements to ensure sustained efficiency in production. The site space is limited and the environment is harsh, which greatly improves the safety risks caused by the replacement of the motor. Conventionally, motor replacement procedures involve manual labor for handling, disassembling, and installing the unit. This process is not only labor-intensive but also fraught with significant safety risks. Due to the substantial size and weight of the electric motor, lifting equipment is essential for correct installation. However, adjusting these lifting devices during the installation process is highly challenging and can significantly decrease installation efficiency. To enhance safety and efficiency, a more systematic approach to motor installation and maintenance is imperative. To tackle these challenges, we propose a six-joint manipulator specifically designed for the replacement of electric motors in large flotation cells. A comprehensive virtual prototype model of the manipulator was developed to facilitate in-depth analysis and control strategy design. Trajectory tracking control simulations were subsequently performed to evaluate its performance under various operational conditions. The numerical simulation results validate the effectiveness of the SSCN-based OSMC method, highlighting its potential for achieving precise and reliable operation in complex industrial environments. The model designed using the software is illustrated in Figure 4.  
In the simulation process, the expected trajectory is defined as \(q_{d}=0.1\sin(t)\). To analyze the control effectiveness throughout the entire motion process, we conducted  
Figure 4: Simulation model picture. simulation analyses starting from a position relatively far from the target. By comparing this with cases where the manipulator begins closer to the desired position, the impact of the control strategy can be clearly demonstrated. Therefore, the initial position of the manipulator is set to \(\mathbf{q}=[0.032\ 0.032\ 0.032\ 0.032\ 0.032\ 0.032\ 0.032\ 0.032]^{\mathsf{T}}\). To increase the model's fidelity to real industrial production conditions, the unknown dynamics of the manipulator can be expressed as  
\[\mathbf{D}=\frac{\sin\Bigl{(}\sum_{i=1}^{6}\Bigl{(}\mathbf{q}_{i}^{3}+\ \hat{\mathbf{q}}_{i}^{3} \Bigr{)}\Bigr{)}+\sum_{i=1}^{6}\bigl{(}\mathbf{q}_{i}+\ \hat{\mathbf{q}}_{i}\bigr{)}}{1+\sum_{i=1}^{6}\Bigl{(}\mathbf{q}_{i}^{4}+\ \hat{\mathbf{q}}_{i}^{4}\Bigr{)}}\mathbf{E}+\mathbf{\tau}_{d} \tag{44}\]  
Given the wide variation in the dynamics and motion characteristics of the manipulator within its working range, disturbances were introduced during the control process to better approximate real-world operating conditions. The specific disturbances added are represented as \(\mathbf{\tau}_{d}=0.01\sin(t)\mathbf{E}\). Based on the principle of maximizing realism and the adjustments observed during the simulation experiments, the control parameters of the OSMC are set to \(a=-0.2\), \(m=2\), \(n=1.4\), \(\alpha=100\), \(\beta=11\), \(b_{1}=2\), \(b_{2}=4\), \(b_{3}=4\), \(c_{1}=-0.4\), \(c_{2}=0.55\), \(c_{3}=500\), \(\delta_{1}=0.005\mathbf{E}\), and \(\delta_{2}=14\mathbf{E}\). \(\mathbf{E}\) represents the identity matrix. The SSCN's operational efficiency involves two key phases: an initial training phase and a real-time adjustment phase. In the initial phase, the model is calibrated using historical data to optimize hidden layer nodes and input parameters, thereby minimizing computational complexity. During the real-time phase, an adaptive law adjusts the output weights, enhancing the model's compensatory abilities. The SSCN features a three-layer architecture with an external feedback loop integrated into the hidden layer.