Intelligent iPD control estimation of Hardware in-the-Loop generated dynamics

We are concerned here by the study of explicit ultra-local model approximation in the context of model free intelligent Proportional Derivative (iPD) control, which to our knowledge has not been reported in the available control literature. The unmodeled dynamics estimation is approximated by integrals reducing real-time system measurements noise in the control loop and implemented using a Finite Impulse Response (FIR) digital filter. Hardware-in-the-Loop (HIL) perturbation load generated dynamics are used in an iPD control scheme exhibiting the unknown dynamics approximation. We use two DC servo motors interconnected by their shafts. The first DC servo motor is controlled by the proposed feedback-based iPD controller whereas the second DC servomotor is used as a programmable torque load to the controlled DC servo motor. Using HIL testing we can generate desired load torques directly showing how the iPD controller approximates HIL generated perturbations. For the proposed control iPD scheme, we present both computer-based simulation and experimental real-time control results.