Cardiac rhythms are related to heart electrical activity, being an essential aspect of the cardiovascular physiology. Usually, these rhythms are represented by electrocardiograms (ECGs) that are useful to detect cardiac pathologies. This paper investigates the control of cardiac rhythms in order to induce normal rhythms from pathological responses. The strategy is based on the electrocardiograms and considers different pathologies. An intelligent controller is proposed considering the ECG as the observable variable. In order to allow the assessment of the control performance, synthetic ECGs are produced from a reduced-order mathematical model that presents close agreement with experimental measurements. The adopted model comprises a network of oscillators formed by sinoatrial node, atrioventricular node and His-Purkinje complex. Three nonlinear oscillators are employed to represent each one of these nodes that are connected by delayed couplings. The controller considers the control variable at the His-Purkinje complex. To evaluate the ability of the control law to deal with both intra- and interpatient variability, the heart model is assumed to be not available to the controller designer, being used only in the simulator to assess the control performance. The incorporation of artificial neural networks into a Lyapunov-based control scheme, however, allows the presented intelligent approach to compensate for unknown cardiac dynamics. Results show that abnormal rhythms can be avoided by applying the proposed control scheme, turning the electrocardiogram closer to the expected normal behavior and preventing critical cardiac responses.

Omnidirectional mobile robots have gained a lot of attention in recent years due to their maneuverability capabilities. However, ensuring accurate trajectory tracking with this class of robots is still challenging control system designers. In this work, a novel intelligent controller is introduced for accurate trajectory tracking of omnidirectional robots subject to unstructured uncertainties. An adaptive neural network is adopted within a Lyapunov-based nonlinear control scheme to deal with frictional forces and other unmodeled dynamics or external disturbances that may occur. Online learning, rather than supervised offline training, is employed to allow the robot to learn on its own how to compensate for uncertainties and disturbances by interacting with the environment. The adoption of a combined error signal as the single input in the neural network significantly reduces the computational complexity of the disturbance compensation scheme and enables the resulting intelligent controller to be implemented in the embedded hardware of mobile robots. The boundedness and convergence properties of the proposed control scheme are proved by means of a Lyapunov-like stability analysis. The effectiveness of the proposed intelligent controller is numerically evaluated and experimentally validated using an omnidirectional mobile robot. The comparative analyses of the obtained results show that the adoption of an intelligent compensation scheme based on adaptive neural networks allows reductions of more than $$95\%$$95 % in the tracking error, thus guaranteeing an accurate tracking and confirming the great superiority of the proposed control strategy.

The development of accurate control systems for underwater robotic vehicles relies on the adequate compensation for hydrodynamic effects. In this work, a new robust control scheme is presented for remotely operated underwater vehicles. In order to meet both robustness and tracking requirements, sliding mode control is combined with Gaussian process regression. The convergence properties of the closed-loop signals are analytically proven. Numerical results confirm the stronger improved performance of the proposed control scheme.

Cardiac rhythms are related to heart electrical activity, being the essential aspect of the cardiovascular physiology. Usually, these rhythms are represented by electrocardiograms (ECGs) that are useful to detect cardiac pathologies. Essentially, the heart activity starts in the sinoatrial node (SA) node, the natural pacemaker, propagating to the atrioventricular node (AV), and finally reaching the His-Purkinje complex (HP). This paper investigates the control of cardiac rhythms in order to induce normal rhythms from pathological responses. A mathematical model that presents close agreement with experimental measurements is employed to represent the heart functioning. The adopted model comprises a network of three nonlinear oscillators that represent each one of the cardiac nodes, connected by delayed couplings. The pathological behavior is induced by an external stimulus in the SA node. An adaptive controller is proposed acting in the SA node considering an strategy based on the signal obtained by the natural pacemaker and its regularization. The incorporation of adaptive compensation in a Lyapunov-based control scheme allows the compensation for the unknown dynamics. The controller ability to deal with interpatient variability is evaluated by assuming that the heart model is not available to the controller design, being used only in the simulator to assess the control performance. Results show that the adaptive term can reduce the control effort by around 3% while reducing the tracking error by 20%, when compared to the conventional feedback approach. Additionally, the controller can avoid abnormal rhythms, turning the ECG closer to the expected normal behavior and preventing critical cardiac responses. Therefore, this work demonstrates that an adaptive controller can be used to regulate the ECG signal without prior information about the system and disregarding inter- and intrapatient variability.

This work introduces a new control scheme for uncertain underactuated mechanical systems. The proposed approach is mainly based on sliding mode control, but a Gaussian process regressor is also embedded in the control law for uncertainty estimation and compensation. The convergence properties of the closed-loop signals are analytically proved by means of the Lyapunov stability theory. Numerical simulations with an inverted pendulum on a cart are presented to confirm the improved performance of the proposed control scheme.