Regras de sintonia para compensadores de tempo morto

Abstract

This work proposes a robust tuning rule of a controller based on Smith predictor for unstable delayed open-loop processes. Transport delay is widely found in industrial processes relied on the movement of materials or energy. However, if the controller is not designed correctly, it may cause undesirable behavior or may not be robust. As a solution to improve the problem is to use dead-time compensators. Nevertheless, despite many advances in the field of compensators for unstable processes with dead time, the adjustment, in general, is performed only for particular cases. The proposed strategy computes a robust dead-time compensator from specifications of dead time, maximum delay uncertainty and aimed closed-loop time constant. In order to define and validate the rule, several simulations were carried out within a desired region. In addition, since practical implementation is always in the discrete time domain, a method is presented for choosing the sampling time of unstable first-order processes with dead time. This method allows obtaining the maximum sampling time without significantly changing the robustness and performance of the system. Four examples from the literature are used in simulations to show the advantages of the proposed method. Through the proposed strategy, it was possible to find better or equivalent results compared to correlated works. Furthermore, it was possible to use sampling times up to ten times longer without compromising performance. At the end, a practical experiment is presented where the objective is to control the position of a pendulum driven by a motorized propeller. Process feedback is ensured through the image processing from a camera, through a microprocessor which communicates over a Wi-Fi network. The inherent delays in communication and computing time justify the insertion of the controller proposed here. As a result, the control algorithm maintained the expected robust performance, bypassing the uncertainties due to the model’s approximation.