In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision.