This paper proposes an event-triggered parallel optimization model predictive control (MPC) strategy integrated with an extended state observer (ESO) for mobile robots operating under unknown disturbances. In the proposed framework, a conventional MPC formulation is employed to minimize the tracking error and control increments, thereby generating real-time control outputs. To improve control performance and computational efficiency, a parallel optimization strategy is introduced to optimize MPC parameters by jointly considering the accumulated tracking error, state convergence time, and computational cost. An improved Cuckoo Catfish Optimizer (ICCO) is adopted to perform the parallel parameter optimization in real time, enabling adaptive tuning of the MPC parameters. Furthermore, to address unknown external disturbances and model uncertainties, a nonlinear ESO is designed to estimate and compensate disturbances, thereby enhancing the robustness and tracking accuracy of the system. The effectiveness of the proposed control strategy is validated through experimental studies conducted on the MR500 mobile robot platform. The comparative experimental results demonstrate that the proposed method achieves an approximately 40% reduction in tracking error compared with the conventional MPC, while also exhibiting faster response speed, higher tracking accuracy, and stronger robustness against both internal and external disturbances.

With the rapid development of the Internet and information technology, the availability of music resources has experienced significant growth. However, along with this abundance, users often find it difficult to navigate through such an extensive collection and locate the specific songs they are looking for, leading to a considerable investment of time and effort. Thankfully, the emergence of music recommendation systems has addressed this problem effectively. These systems leverage advanced algorithms to swiftly help users discover music that aligns with their preferences, thereby saving their valuable time and energy, while also contributing to the economic success of the music platforms. This research centers around the recommendation of music using Transformer-based frameworks. To achieve this, we harness the power of the PyTorch framework to build a comprehensive network model that takes into account essential factors, such as music information, user profiles, contextual details, and historical user behavior data. An efficient transformer module is derived and serves as the backbone of the network model, facilitating the generation of a top-k recommendation list tailored to each user’s preferences. The module combines the attention free transformer and the convolutional layers. In evaluating our approach, we rely on established metrics, such as accuracy and recall, to assess the level of user interest. Experiments affirm the superiority of our approach, surpassing the performance of existing methods in the field.