With the rapid development of intelligent manufacturing and Internet technology, the industrial system has entered a new stage of development. As an indispensable carrier for intelligent manufacturing and industrial development, robots are expanding their applications. Among them, the flexible mechanical arm has the advantages of light weight, low energy consumption and low inertia compared with the bulky rigid mechanical arm, and has been increasingly valued. The flexible manipulator is a very complex dynamic system whose dynamic equations are characterized by nonlinearity, strong coupling and time-varying. Therefore, this paper uses the most common and effective method to establish the dynamic model of the flexible manipulator using the Lagrange equation. Due to the uncertain system parameters, lack of control of the trajectory and the influence of load changes and external disturbances, the flexible manipulator has great uncertainty in its control process, and the traditional control methods have not very good control effect. Based on this, this paper proposes a combination of dynamic pattern recognition theory and flexible joint manipulator intelligent control method for the two-link flexible manipulator, and uses the new GA-RBF neural network closed-loop adaptive control method to achieve high precision. Trajectory tracking ensures stability in a shorter time. The simulation results show that the intelligent joint control method based on dynamic pattern recognition has better trajectory tracking and autonomous fast recognition dynamic mode.
Intelligent control of a single‐link flexible manipulator using sliding modes and artificial neural networks