Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method.

Summary: Imagine a robot arm that is slightly flexible, like a fishing rod, rather than perfectly rigid. While this flexibility can be useful, it makes the arm wobble and difficult to control precisely, especially when external forces bump it. This paper introduces a "smart" control system to solve this problem. It uses a type of Artificial Intelligence called a Radial Basis Function Neural Network (RBFNN) to learn the arm's movements and correct for wobbles in real-time. It also adds a "damping" feature to absorb shocks from the outside world. To double-check its work, the system uses another AI tool (LSTM) to predict potential errors before they become problems. Computer simulations show that this new method keeps the robot arm steady and accurate, even when things get shaky.