Control design using state observation and model reference adaptive systems for a dc motor

Abstract

This paper proposes an adaptive control structure involving the integration of a Luenberger observer with a model reference adaptive system. The Luenberger state observation algorithm describes a dynamic system that estimates the state vector of a system under observation. Model reference adaptive system is a technique providing an automatic adjustment of a controller in real-time. The integrated control structure was implemented in the simulation of a first-order system that describes the dynamics of a direct-current motor. The results obtained show the performance improvement to other classic control techniques for these direct-current systems. Simulation results also demonstrate the acceptable performance achieved by the proposed control for systems involving variables without direct measurement. For these cases, it is mandatory to have a good observation of the unmeasured variable and a suitable control structure developed for the overall process.