Learning enhanced model reference adaptive control algorithms via a cost-effective educational plant

The enhanced model reference adaptive control (EMRAC) strategy is an effective extension of the model reference adaptive control (MRAC) algorithm, designed to steer the trajectories of plants towards those of a reference model despite uncertain plant parameters, unknown nonlinear dynamics, and disturbances by adapting the control gains in operation. However, MRAC algorithms are usually taught only in advanced control engineering courses, as they are more complex compared to linear control methods. To demonstrate the potential of EMRAC solutions for controlling systems with limited knowledge of plant dynamics and to facilitate students’ learning, this article presents the control formulation, design, and experimental validation of EMRAC strategies for an educational plant (i.e., the speed control of a DC motor through Arduino). Despite being modelled as a first order system, this plant is characterised by system nonlinearities and parameter uncertainties which can jeopardise closed-loop tracking performance of a controller with fixed gains. An incremental approach is used to introduce the EMRAC algorithm, with only knowledge in linear control design, e.g., pole-placement methods, assumed as a prerequisite. Specifically, the need for MRAC strategies is first introduced as a possible solution to overcome limitations of pole-placement techniques by adapting the control gains. Then, the classical MRAC strategy, along with methods for limiting the growth of the gains, is reviewed, and finally, two EMRAC strategies are presented. The improved closed-loop tracking performance given by the EMRAC strategies are also assessed quantitatively and compared to those obtained with two linear control solutions and two classical MRAC methods.