This paper presents a discrete-time inverse optimal control scheme using a neural network for a doubly fed induction generator (DFIG). The DFIG generation scheme has a voltage-source converter connected between the rotor and the electrical grid, which is composed by a two insulate-gate bipolar transistors (IGBT) power converters connected in back-to-back configuration. These converters are known as rotor side converter (RSC) and grid side converter (GSC), respectively. The RSC is used to control the electric torque and reactive power of the DFIG, and the GSC is used to control the DC Link voltage in the IGBT connection and the stator terminals reactive power. To take into account possible parameter variations, a recurrent high order neural network (RHONN) is used to approximate the DFIG model in an identification process; after that, based on the neural model obtained, a discrete-time inverse optimal control scheme for the RSC is developed. Using a similar approach, a DC Link neural identifier and a controller are proposed for the GSC. The proposed control scheme applicability is validated by means simulations, which includes a comparison with proportional-integral controllers; then this control scheme is implemented in real-time. The paper novelty consists on the synthesis and real-time implementation of a DFIG inverse optimal control based on a RHONN.

In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were compared against an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an intelligent technique. The data gathered involved solar radiation, outside temperature, wind speed, daylight hour and photovoltaic power; collected from on-site real-time measurements at Mexico City and Hermosillo City, Sonora State. According to our results, all three methods achieved satisfactory performances, since low values were obtained for the convergence error. The GDO improved the MLR results, minimizing the overall error percentage value from 7.2% to 6.9% for Sonora and from 2.0% to 1.9% for Mexico City; nonetheless, ANFIS overcomes both statistical methods, achieving a 5.8% error percentage value for Sonora and 1.6% for Mexico City. The results demonstrated an improvement by applying intelligent systems against statistical techniques achieving a lesser mean average error.

In this paper, a novel distributed secondary cooperative control based on neural inverse optimal control scheme is synthesized for microgrids considering island and grid-connected modes. This approach is developed for each distributed generator to track frequency and voltage predefined values for island mode, and to track active and reactive power references for grid-connected mode, and for smooth switching between these two modes. To achieve these goals, a secondary control layer is proposed to define the required currents trajectories, which then are controlled using the primary controller. Both control layers are synthesized using recurrent high order neural network on-line trained using an extended Kalman filter, which builds up neural models and is to be used to implement the respective inverse optimal controllers. In addition, a pinning technique is also used for achieving synchronization of all distributed generators only requiring neighborhood information. Thus, a large reduction in the communication network complexity is obtained and the control system reliability is improved. Simulations are performed to evaluate the effectiveness of the proposed scheme with a microgrid benchmark. The obtained results illustrate adequate performances of the proposed scheme to operate the microgrids in island mode, grid-connected mode, and to achieve seamless switching between these two modes.

This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture.

This paper presents the development of a neural inverse optimal control (NIOC) for a regenerative braking system installed in electric vehicles (EVs), which is composed of a main energy system (MES) including a storage system and an auxiliary energy system (AES). This last one is composed of a supercapacitor and a buck–boost converter. The AES aims to recover the energy generated during braking that the MES is incapable of saving and using later during the speed increase. To build up the NIOC, a neural identifier has been trained with an extended Kalman filter (EKF) to estimate the real dynamics of the buck–boost converter. The NIOC is implemented to regulate the voltage and current dynamics in the AES. For testing the drive system of the EV, a DC motor is considered where the speed is controlled using a PID controller to regulate the tracking source in the regenerative braking. Simulation results illustrate the efficiency of the proposed control scheme to track time-varying references of the AES voltage and current dynamics measured at the buck–boost converter and to guarantee the charging and discharging operation modes of the supercapacitor. In addition, it is demonstrated that the proposed control scheme enhances the EV storage system’s efficacy and performance when the regenerative braking system is working. Furthermore, the mean squared error is calculated to prove and compare the proposed control scheme with the mean squared error for a PID controller.

This work addresses one of the most common problems for mobile robotics (autonomous navigation) but is applied to the dynamical model of a catamaran of small dimensions for monitoring and data acquisition applications. In this work, we present the study of the dynamics of a USV (Unmanned Surface Vehicle) under the presence of two simulated environmental perturbations: marine induced waves and currents. The mathematical model of the vehicle is studied and the equations that describe the behavior of environmental perturbations are also described. A numerical simulation of the model considering the effects of these perturbations is carried out in three degrees of freedom. Also, a strategy of robust control‐based Sliding Mode Control (SMC) is developed for counteracting the effects of the perturbations over the trajectory of the USV.