The synthesis of electric motor control systems, seeking optimal performance, is a well-known and studied field of automation to date. However, the solutions often use very elaborate mathematical foundations and sometimes require considerable algorithmic complexity. Another approach to the same problem, which offers very interesting results, is the use of artificial intelligence methods to generate controllers. Intelligent methods allow the use of bio-inspired approaches to solve complex problems. This article presents a method to adjust the parameters of a controller for DC motors based on two components in the objective function: High productivity and efficiency. This can be achieved using well-known and low algorithmic complexity PID controllers, and metaheuristic artificial intelligence techniques to adjust a controller to obtain optimal behavior. To validate the benefits of the methodological proposal, a simulator of a DC motor has been rigorously constructed, respecting fundamental physical principles. The adjustment system based on metaheuristics (genetics algorithms) has been designed to work on the simulator and constitutes the central contribution of the paper. This system has been designed to establish the parameters of a PID controller, optimizing its behavior in relation to two variables of interest, such as performance and energy efficiency (a non-trivial problem). The results obtained confirm the benefits of the approach.

Se presenta en este artículo la comparación de tres controladores de velocidad (regulador cuadrático lineal-LQR-, proporcional integral derivativo-PID-y borroso) con la intención de determinar cuál de ellos ofrece mejor fiabilidad desde una perspectiva software. Para realizar las pruebas necesarias se utilizaron versiones mutantes de controladores bien ajustados, en los que se inyectaron defectos que simulaban errores de programación. Los controladores fueron diseñados para operar un vehículo autónomo terrestre y fueron ajustados por medio de un algoritmo genético. Dado el elevado número de pruebas a efectuar se decidió construir un simulador multicomputador con el que se realizaron más de 90000 ensayos. En cada uno de los ensayos se sometió a cada controlador mutante a la realización de un recorrido, de unos 20 minutos de duración máxima, sobre un suelo ligeramente ondulado. Con los datos obtenidos se generaron las curvas de fiabilidad por el procedimiento de Kaplan-Meier, lo cual permitió la comparación de controladores objetivo del estudio. De las curvas de fiabilidad del software obtenidas se deduce que, en las condiciones experimentales planteadas, el controlador LQR ofrece el mejor comportamiento, el segundo lugar le corresponde al controlador PID y el tercero al controlador borroso.

Abstract-In this paper, in order to select a speed controller for a specific non-linear autonomous ground vehicle, proportionalintegral-derivative (PID), Fuzzy, and linear quadratic regulator (LQR) controllers were designed. Here, in order to carry out the tuning of the above controllers, a multicomputer genetic algorithm (MGA) was designed. Then, the results of the MGA were used to parameterize the PID, Fuzzy and LQR controllers and to test them under laboratory conditions. Finally, a comparative analysis of the performance of the three controllers was conducted.

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