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 work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters $P_0$, $Q_0$, and $R_0$ are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.

Numerous academic works have addressed the identification and control problem for complex dynamic systems. In recent decades, the use of control algorithms based on neural networks (NNs) has been highlighted, which have shown satisfactory results in the trajectory tracking control for a class of discrete-time nonlinear systems. The present work proposes an efficient learning law for discrete-time recurrent high order neural networks (RHONNs), using a training algorithm based on an unscented Kalman filter (UKF). This learning law is applied through a decentralized neural block control using UKF (DNBC-UKF), for trajectory tracking control of a class of discrete-time nonlinear systems. The proposed controller is experimentally evaluated by means of real-time tests in a two degrees of freedom (DOF) vertical direct-drive robotic manipulator against a decentralized neural block control using EKF (DNBC-EKF). The Lyapunov stability theory is used to show that the identification errors of RHONNs are semi-globally uniformly ultimately bounded (SGUUB), the RHONN weights remains bounded, and the closed-loop tracking errors go to zero.

This work presents an alternative solution of the trajectory tracking problem for a two degrees of freedom (DOF) robot manipulator using a discrete-time decentralized control strategy. The local controller for each joint uses only local angular position and velocity measurements. A recurrent high order neural network (RHONN) structure is used to aproximate the manipulator dynamics, and based on this model, a discrete-time control law is computed, which combines block control and the sliding mode techniques. The neural network learning is performed on-line by Unscented Kalman filter (UKF) algorithm, a variation of nonlinear Kalman Filter.

El conocimiento es un tejido vivo que se fortalece en la intersección de disciplinas. Siner- gias multidisciplinarias: innovación en ciencia, tecnología y socioeconomía es muestra de ello: un compendio de investigaciones que analizan problemáticas contemporáneas desde enfoques tecnológicos, científicos, socioeconómicos y sociales, con el propósito de generar soluciones de alto impacto. Este volumen invita al lector a recorrer avances en robótica, manufactura inteligente, energías limpias y análisis computacional; explora la aplicación de inteligencia artificial en la salud y el diseño de dispositivos biomédicos; examina estrategias de economía circular, innovación empresarial y sustentabilidad productiva, y profundiza en estudios sociales que reflexionan sobre la equidad, la inclusión y la sororidad en nuestras comunidades. Más allá de su diversidad temática, la obra destaca por su vocación interdisciplinaria y su orientación hacia la transformación social. Cada capítulo combina rigurosidad científica con sensibilidad hacia los contextos en los que se inserta, demostrando que la investigación no solo explica fenómenos, sino que también propone caminos hacia un futuro más justo y sostenible. Sinergias multidisciplinarias: innovación en ciencia, tecnología y socioeconomía se consolida, así, como una obra de referencia para investigadores, estudiantes, docentes y profesionales que buscan comprender la complejidad del presente y contribuir, desde el conocimiento, a la construcción de sociedades resilientes e innovadoras.