Application of neural generalized predictive control to robotic manipulators with a cubic trajectory and random disturbances

In this study, a single-input single-output (SISO) neural generalized predictive control (NGPC) was applied to a three-joint robotic manipulator with a cubic trajectory and random disturbances. The SISO generalized predictive control (GPC) was also used for comparison. Modelling of the dynamics of the robotic manipulator was carried out by using the Lagrange–Euler equations. The frictional effects, random disturbance, carrying and falling load effects were added to the dynamics model. The cubic trajectory principle is used for position reference and velocity reference trajectories. A simulation program was prepared by using Delphi 5.0. All computations for the manipulator dynamics model, GPC_SISO, and NGPC_SISO were done on a PC with 733 MHz CPUs using this program. The parameter estimation algorithm used in the GPC_SISO is Recursive Least Squares. The minimization algorithm used in the NGPC_SISO is Newton–Raphson. According to the simulation outcome, the results from the NGPC_SISO algorithm were better than those from the GPC_SISO algorithm. And these results showed also that the NGPC_SISO reduced the influence of the load changes and disturbances. This means that the NGPC_SISO algorithm combines the advantages of predictive control and the neural network.