This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.

Due to the opening of the energy market and agreements for the reduction of pollution emissions, the use of microgrids attracts more attention in the scientific community, but the management of the distribution of electricity has new challenges. This paper considers different distributed generation systems as a main part to design a microgrid and the resources management is defined in a period through proposed dynamic economic dispatch approach. The inputs are obtained by the model predictive control algorithm considering variations of both pattern of consumption and generation systems capacity, including conventional and renewable energy sources. Furthermore, the proposed approach considers a benefits program to customers involving a demand restriction and the costs of regeneration of the pollutants produced by conventional generation systems. The dispatch strategy through a mathematical programming approach seeks to reduce to the minimum the fuel cost of conventional generators, the energy transactions, the regeneration of polluted emissions and, finally, includes the benefit in electricity demand reduction satisfying all restrictions through mathematical programming strategy. The model is implemented in LINGO 17.0 software (Lindo Systems, 1415 North Dayton Street, Chicago, IL, USA). The results exhibit the proposed approach effectiveness through a study case under different considerations.

This work proposes a novel controller in a stationary reference frame for permanent magnet synchronous generator (PMSGs) of grid-connected wind turbines. The wind energy conversion systems (WECS) adopt a Back-To-Back converter system with voltage source inverter (VSI) and a phase-locked loop to track continuously the fundamental frequency and its phase from voltage node that is connected to the system generation. One of the central themes of this paper is the development of a control scheme that uses a B-spline artificial neural network for controllers tuning when the system is subjected to disturbances. The B-spline neural network must be able to enhance the system performance and the online parameters updated can be possible. This work proposes the use of adaptive PI controllers to regulate the current, frequency and DC-link voltage. Results of the dynamic behavior of the whole WECS are presented along with different cases subject to various disturbances. The simulations show the feasibility and robustness of the proposed control schemes for PMSG based wind turbines.

Proportional integral (PI) control is still the most widely deployed controller in the industrial drives due to its simplicity and the fact that it is easy to understand and implement. Nevertheless, they are successes applied to systems with a complex behavior with a nonlinear representation, but a disadvantage is the procedure to find the optimal PI controller gains. The optimal values of PI parameters must be computed during the tuning process. However, traditional tuning techniques are based on model and do not provide optimal adjustment parameters for the PI controllers because the transient response could produce oscillations and a large overshoot. In this paper, six swarm intelligence-based algorithms (whale, moth-flame, flower pollination, dragonfly, cuckoo search, and modified flower pollination), are correctly conditioned and delimited to tune the PI controllers, the results are probed in a typical industry actuator. Also, a rigorous study is developed to evaluate the quality and reliability of these algorithms by a statistical analysis based on non-parametric test and post-hoc test. Finally, with the obtained results, some time simulations are carried out to corroborate that the nonlinear system performance is improved for high precision industrial applications subjected to endogenous and exogenous uncertainties in a wide range of operating conditions.

Resumen: Los sistemas de control automático día a día se han convertido en elementos importantes en la vida cotidiana, en tal sentido, se deben proponer nuevas y mejores formas de incorporar modelos matemáticos y algoritmos de control adaptativos para superar la gran cantidad de cambios técnicos y físicos a los que se enfrentan para su utilización. En este artículo se realiza el control de posición y seguimiento de trayectorias para un Quadrotor. Debido a la naturaleza no lineal de este sistema subactuado, se propone el empleo de un controlador adaptativo basado en redes neuronales B-spline que permita determinar las señales de control mediante un entrenamiento dividido en dos etapas: a) uno inicial fuera de línea y; b) uno continuo en línea. Esta forma de aprendizaje permite al Quadrotor extender un desempeño satisfactorio ante diferentes condiciones operativas y seguimiento de los valores de referencia. Los resultados de simulación verifican la aplicabilidad del controlador propuesto y el impacto que se tiene en el desempeño del sistema minimizando la necesidad de contar con un modelo matemático no lineal detallado, así como el conocimiento exacto de los valores de los parámetros del Quadrotor. Automatic control systems every day become more important in everyday life; therefore, it must find new and better ways to incorporate mathematical models and adaptive control algorithms to cope with a number of technical and physical challenges for exploitation. In this paper, the algorithm of the dynamic model of a Quadrotor applied to an angular position and trajectory control as a study case is detailed. Due to nonlinear nature of this type of systems, an adaptive on line neurocontroller algorithm based on B-spline neural networks is proposed, the learning procedure is divided in two stages: a) an initial off line training and; b) an on line continuous learning. This form of learning allows the Quadrotor extend its satisfactory performance at different operating conditions and trajectory tracking. The simulation results demonstrate the applicability of the developed model and the impact of dynamic control on the system performance, diminishing the exact model requirement and the possibility to incorporate the system non linearities. Palabras clave: Control Neuronal, Control Libre de Modelo, Aprendizaje Automático, Sistemas Subactuados, Keywords: Neural Network Control, Model-Free Control, Automatic Learning, Underactuated System