NEURAL NETWORK PREDICTIVE CONTROL FOR ROBOT DRIVE BASED ON TWO-MASSES MODEL WITH RESILIENCE
Robot drive control problem based on two-masses model with resilience is considered in the paper. Considered mathematical model in traditional one and describes, in particular, a manipulator robot drive with resilient kinematics transmissions. Most results on solution of control synthesis problem for such dynamical objects are derived from nonlinear and adaptive control theory. The paper considers the two-masses system direct current drive with resilience and viscous friction. In this paper features of predictive neural network controller are investigated in comparison with conventional PID-regulator. Block diagrams of the electric drive control systems implemented in Simulink Matlab. It was demonstrated that application of predictive neurocontroller can essentially improve transient processes quality. Results of simulations are given and discussed.