The electrocardiogram (ECG) is basic equipment used in the diagnosis of cardiac illness. However, in non-developed countries, most of the population does not have access to medical tests, and many hospitals do not even have these ECGs. On the other hand, the electrical signals generated by the heart and acquired by the ECG have low power and are affected by electromagnetic interference (EMI), mainly produced by the electrical system. Filtering EMI when frequency varies is a challenging task. Within this context, this work aims to produce an easy-to-use low-cost ECG with good electromagnetic disturbances rejection. The proposed noise rejection system is composed of two moving average filters and a phase-locked-loop, namely 2MAV-PLL. The system operates with a low sampling frequency and attenuates the EMI noise present in the ECG signal regardless of the amplitude, obtaining a filtered signal with a 44-dB signal–noise ratio (SNR) between the frequencies of± 10 Hz of the fundamental frequency. Simulation and experimental results prove that the ECG system can attenuate the EMI using relatively low sampling frequency, giving adequate information for health professionals to properly evaluate an electrocardiogram.

This brief proposes an adaptation of the Generalized Predictive Control (GPC) for ramp-reference tracking. The second-order difference operation and the plant model are used to get an augmented model with two embedded integrators and whose output is the tracking error. Differently from other GPC-based tracking algorithms, the proposed approach does not require information about the reference parameters, and the GPC prediction horizon is composed of the predicted errors instead of the expected plant outputs. Thus, the optimization function and the receding horizon strategy used in conventional GPC can be applied to get the control law. Simulation and experimental results prove that the proposed approach can successfully track constant and ramp references. The proposed method is applicable for single-input single-outputs plants. However, the mathematical background presented in this brief can be used in the development of new GPC strategies.

Some important applications require control systems capable of tracking high-degree polynomial references. However, polynomial reference tracking is difficult because the controller must have a fast response to track variable references. Generalized Predictive Control (GPC), used in industrial applications, has a fast response. However, up to now, GPC approaches in the literature are not designed for high-degree polynomial reference tracking. For that reason, this brief proposes a GPC system for the tracking of polynomial references of any degree. The high-order backward difference operator was used to develop an augmented prediction model designed to have an arbitrary number of embedded integrators. Thus, according to the internal model principle, the proposed predictive controller can track polynomial references of any degree. An optimization technique is used to define future control actions, while the control law is defined through the receding horizon principle. Simulation and experimental results prove that the proposed controller performs better than other approaches described in the literature.

Sinusoidal reference tracking is required in important practical applications. Resonant controllers are suitable for that task. However, stable implementation of these controllers in a digital processor is difficult to achieve. On the other hand, quasi-resonant controllers are easier to implement, but they do not guarantee asymptotic reference tracking. This paper proposes a new resonant controller based on Generalized Predictive Control (GPC) and Internal Model Principle (IMP). The first-order and second-order difference operators are applied to create a GPC system whose structure embeds the Z-transform of the sinusoidal reference. Thus, according to IMP, the proposed controller, named Resonant Generalized Predictive Control (RGPC), asymptotically tracks sinusoidal references. Besides, the proposed RGPC has the advantages of GPC, such as fast response and easy implementation in digital processors. Experimental tests prove that the proposed RGPC has better performance than other controllers for sinusoidal reference tracking.

This paper presents a simplified algorithm of space vector pulse width modulation (SVPWM) for a two-level three-phase inverter, which can operate in undermodulation and overmodulation modes. In other simplifications founded in literature, the reference voltages are modified and compared with a triangular carrier to estimate the switching states of the inverter. However, this paper proposes the modification of the carrier signal instead of the references. This procedure reduces the number of mathematical operations and increases the execution speed of SVPWM algorithm in DSPs or FPGAs. The reference voltages are sinusoidal, even for overmodulation mode. Simulation and experimental results proves that the proposed simplification produces the same switching patterns than conventional SVPWM, is simpler and is faster than other simplifications.

This paper presents the use of a radial basis function artificial neural network to estimate sensor readings exploring the analytical redundancy via auto-association. However, in order to guarantee optimal performance of the network, the training and optimization processes have been modified. In the conventional training algorithm, even if a stop criterion, such as summed squared error, is reached, one or more of the individual performance metrics, including: i) accuracy; ii) robustness; iii) spillover and iv) filtering of the neural network, may not be satisfactory while validating sensor measurements. Essentially, the proposed modification in the training algorithm is based on seeking to ensure that one or more of the metrics are met. This paper describes the proposed algorithm including all of its mathematical foundation. Afterward, a data set of a water injection pump for an oil and gas processing unit was used to train the RBF network using the conventional and the modified algorithm, and the performance of each was evaluated. Furthermore, the AAKR model is applied to the same dataset as a quality reference parameter. Finally, a comparison analysis of the developed models is presented for each of the performance metrics, as well as for overall effectiveness, demonstrating that the main advantage of the proposed approach is to obtain the estimation results equivalent or superior to the AAKR with shorter runtime and the disadvantage of having higher complexity during the model training.