Research on intelligent control algorithms and applications for mechatronic systems of robots

This paper investigates the intelligent control algorithms and applications for mechatronic systems of robots. The fundamental principles and components of robot control in mechatronic systems are introduced, including perception, decision-making, and execution. Sensors, controllers, and actuators are highlighted as core components. Subsequently, intelligent control algorithms for robots are discussed in detail, incorporating intelligent algorithms such as reinforcement learning, deep learning, fuzzy control, and evolutionary algorithms to enhance the robot's perception, decision-making, and execution capabilities. These algorithms possess adaptability, learning capability, and optimization characteristics, allowing robots to make intelligent decisions and achieve precise execution in complex environments. Finally, the applications of intelligent control algorithms for robots in mechatronic systems are summarized. In automated production lines, they contribute to the intelligence and flexibility of the production process. In the field of service robots, they facilitate human-robot interaction and service delivery. In medical robots, they enable surgical assistance and rehabilitation training, among other functions. Through the research and application of intelligent control algorithms for robots, the performance of robots can be effectively improved, expanding their application areas, and driving the development of robot technology in mechatronic systems. This study carries significant theoretical and practical implications for robot control and application.