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O rastreamento de referências polinomiais é muito útil em aplicações como em sistemas mecânicos, químicos e de navegação. Em geral, este problema de rastreamento é difícil de executar. Através de técnicas de controle preditivo generalizado (GPC) é possível desenhar controladores para o rastreamento de referências polinomiais. Porém, a rejeição de perturbação também é um aspecto importante no desenvolvimento de sistemas de controle. O presente artigo aplica e avalia o comportamento de um controlador preditivo generalizado para rastreamento de referência polinomial (Poly-GPC) ante perturbações. Simulações foram efetuadas considerando a presença de perturbações tipo degrau tanto na entrada como na saída da planta. Os resultados indicam que o Poly-GPC possui a capacidade de rastrear referências polinomiais enquanto as perturbações são rejeitadas, sendo que a rejeição depende também da sintonia do controlador preditivo.

Recalcitrant compounds found in pulp and paper mill effluents, which are usually generated at high temperatures, have shown the potential for thermophilic treatment. Using bleached kraft pulp mill effluents, this study aims to evaluate the acclimation conditions of mesophilic aerobic sludge to thermophilic conditions and determine the kinetic parameters. Two feeding strategies were used for this purpose: in the first approach, the first reactor R1 was fed with bleached kraft pulp effluent, while in the second strategy the reactor R2 was fed with easily biodegradable synthetic effluent and then the gradual substitution for bleached effluent was performed after sludge acclimation to a thermophilic environment. Both reactors were operated in batch at 55ºC. The second strategy has a slight advantage in COD (chemical oxygen demand) degradation and a considerable higher biomass production. The kinetic parameters (KS, µmax, Y, Kd) for both approaches were obtained using the Monod model. Those values indicated that the approach used for reactor R2 provided higher reaction speed.

Resolver is an angular position sensor widely used in applications such as electric/hybrid vehicles, CNCs, antennas and robotics. However, the estimation of the angular position from resolver outputs is more difficult than the analysis of encoder signals, and it is still an open question. Most algorithms proposed in literature are based on type-I or type-II angle tracking ob- servers. Some type-III observers were proposed, but they require a high sampling frequency. This paper explores the use of synchronous demodulation of the resolver outputs to simplify the implementation of a type-III angle tracking observer. The resolver outputs are sampled at the peaks and valleys of the excitation resolver signal, being easy to get sine and cosine of the angular position. The proposed approach reduces the computational cost and the required sampling frequency to implement the type-III observer. Simulation and experimental results prove the accuracy of the proposed approach.

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 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.

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.

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.

Frequency domain multiplexing (FDM) allows simplifying the data acquisition in vector control when the resolver is used as a position sensor. However, the crosstalk reduction in the demultiplexing process is still a difficult task. This article proposes an improvement of the FDM-based acquisition system through synchronous sampling and Differential Evolution (DE). The resolver signals and the PWM carrier are synchronized so that the resolver outputs are zero when the PWM carrier reaches its valleys. Thus, the fundamental component of the stator currents can be easily estimated from the signals sent to the acquisition system. These estimated current signals and an angle tracking observer (ATO) are used to estimate the angular position. DE and a discretized model of the ATO are applied in order to improve the angle estimation transient response and to reduce crosstalk. Conditions to apply the proposed multiplexing approach will be defined. Simulation and experimental results show that the proposed approach allows an accurate estimation of the current fundamental components and the angular position when FDM is used, with better performance respect to other approaches in the literature. This is the first time that a DE is used to tune an ATO.

Transportation electrification is gaining notoriety in recent years as to fulfill the aim of reducing the fossil fuels consumption and to increase energy efficiency. Accordingly, integrated starter alternator is a compelling method to convert conventional vehicles into hybrid ones. A deal of benefits can be reached by using ISA systems. In general, it provides vehicles performance enhancement and also reduces fuel consumption. This paper aims to present simulation results and preliminary experimental achievements with a power electronics-based prototype driving a permanent magnet synchronous machine (PMSM). The ISA operation is demonstrated throughout providing cranking torque and assistant torque for constant power and constant torque conditions and afterward the generation mode. Details of the hardware design and control strategies, which include an ADALINE neural network for properly find the mechanical position and the application of a metaheuristic technique to tune the controllers of the motor control, have been also addressed.

Precise and robust sensing of motor stator currents is essential for the closed-loop control of three-phase electrical motors. Usually, vector control techniques require representing the stator currents in an orthogonal αs reference system through Clarke transformation. However, problems such as aging, high temperatures and errors in the conditioning circuits distort the current signals received by the data acquisition system (DAQ) of the motor drive. Those distortions produce current measurement errors that affect the motor drive performance. This paper analyses the application of an auto-regressive neural network (AANN) to compensate for the measurement errors of the three-phase stator currents. The three stator currents of a three-phase motor are sensed to have data redundancy, as the sum of these currents is zero. AANN estimates the actual values of the stationary αs stator currents based on the distorted three-phase current signals. The compensation performance considering different AANN topologies is analyzed. Simulation results show that AANN allows compensating for the current measurement distortion. Low number of artificial neurons in the AANN hidden layers limits the compensation accuracy. However, a high number of neurons in the hidden layer also reduces the compensation performance due to over-fitting.

LCL filters are widely used to connect a renewable electrical power source to the electrical grid. The optimal power transfer to the grid depends on the tracking of a sinusoidal current reference synchronized to the grid voltage. However, the tracking of that reference is a difficult task. On the other hand, the sinusoidal reference can be considered as composed of a set of polynomials of different degrees. Thus, this paper explores the application of polynomial reference tracking generalized predictive controller (Poly-GPC) to control the output current of the LCL filter connected to an AC grid. Different tests using the Poly-GPC with different polynomial reference degrees were performed to verify the relationship between the polynomial degree used to approximate the sinusoidal reference with the control accuracy. Simulation results show that the Poly-GPC can be used to control an LCL filter. An analysis of the effect of the polynomial order used to approximate the sinusoidal reference in the current reference tracking is done. The Poly-GPC adds integrators to the control system to reduce the tracking error. It was observed that adding too many integrators may create instability. However, a low number of integrators reduces the tracking performance.

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.

Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the uncertainty in the pixel-labeling process are not completely addressed. As such, we present a new approach that calculates a weight for each pixel considering its class and uncertainty during the labeling process. The pixel-wise weights are used during training to increase or decrease the importance of the pixels. Experimental results using different datasets like aerial, terrestrial, and ultrasound images show that the proposed approach leads to significant improvements in three challenging segmentation tasks in comparison to baseline methods. It was also proved to be more invariant to noise. The approach presented here may be used within a wide range of semantic segmentation methods to improve their robustness.

High-performance motor drives that operate in harsh conditions require an accurate and robust angular position measurement to properly estimate the speed and reduce the torque ripple produced by angular estimation error. For that reason, a resolver is used in motor drives as a position sensor due to its robustness. A resolver-to-digital converter (RDC) is an observer used to get the angular position from the resolver signals. Most RDCs are based on angle tracking observers (ATOs). On the other hand, generalized predictive control (GPC) has become a powerful tool in the development of controllers and observers for industrial applications. However, no GPC-based RDC with zero steady-state error during constant speed operation was proposed. This paper proposes an RDC based on a second-order difference GPC (SOD-GPC). In SOD-GPC, the second order difference operator is applied to design a GPC model with two embedded integrators. Thus, the SOD-GPC is used to design a type-II ATO whose steady-state angle estimation error tends to zero during constant speed operation. Simulation and experimental results prove that the proposed RDC system has better performance than other approaches in literature.

Resolver is an angular position sensor widely used in motor drives when robustness is critical. This sensor generates two high-frequency amplitude-modulated signals which give information about the angular position. A resolver-to-digital converter (RDC) is an equipment used to get the angular position from the resolver outputs. Many RDCs are based on angle tracking observer (ATO) which reduce the angle estimation error. The transient behavior of the angle estimation depends on the ATO parameters. This paper proposes the tuning of an ATO with two embedded integrators (a type-II system) through root-locus technique. It was used a lead compensator to improve the transient response of the ATO, while the embedded integrators make the steady-state error tend to zero for constant position and speed operation. Simulation results show that the ATO was properly tuned through root locus technique, allowing a good angle estimation.

Frequency-domain multiplexing (FDM) allows simplifying the acquisition hardware applied in the vector control of three-phase motors, when a resolver sensor is used to get the angular position. Conventionally, an analog switch is used together with an analog-to-digital converter to digitize the signals. This approach is based on time-domain multiplexing (TDM). However, the application of FDM in data acquisition has many advantages respect to the conventional approach based on TDM. Signals from the stator current sensors and from the resolver can be multiplexed in frequency by combining these signals through analog adders. Nevertheless, the demultiplexing process in order to recover the original data is still a open question. This paper does an analysis of the demultiplexed signals, in order to give more theoretical background for the development of improved data acquisition systems based on FDM. Simulation results show that using a high-pass filter improves the demultiplexing of the resolver outputs.

Resolver is a sensor used in applications that require a reliable angle measurement. However, getting the angular position from the resolver outputs is a difficult task. Observers called resolver-to-digital converters (RDCs) are used to get the angular position from resolver. In many researches, RDCs are estimation algorithms (software-based RDCs). The transient response and robustness against noise of the angle estimation depends on the parameters of the RDC. This paper proposes the use of a genetic algorithm (GA) to set the parameters of a type-III software-based RDC. GA is a heuristic algorithm that search a solution that minimize a fitness function. In this paper, the solution is composed by the gains of the angular tracking observer (ATO) that composes the RDC. On the other hand, the cost function is a linear combination of the error peak, the settling time and the effect of noise in the angle estimation. Simulations shows that the angle estimation using the proposed approach has accuracy, good transient response and robustness against noise. Besides, GA allows customizing the ATO in order for the RDC to give an estimation where the noise rejection or the settling time is priority.

Stirling cycle is the most similar to the Carnot cycle, which theoretically is the most efficient thermodynamic cycle. For that reason, Stirling engine is a powerful alternative in cogeneration, i.e., to produce electricity from different heat sources. The Stirling cycle is complicated to model, due to it has many equations. This paper shows the model of a Stirling engine developed in the free and open source SCILAB. The objective of this paper is to be a didactic tutorial to develop the model of the Stirling engine to be used as primary machine for the simulation of electrical generators used in cogeneration. The modeling using free software allows many students and researches to use and edit the model. The developed model gives information about pressure, volume, generated power and efficiency. Simulation results shows that the operative model done in SCILAB can be used in the analysis of Stirling engine.

The Stiling engine converts heat in mechanical energy based on the Stirling cycle, which is the most similar to the Carnot cycle. For that reason, Stirling engine is used in cogeneration process to get electricity from different heat sources. However, few studies were done that integrates the thermodynamic model of the Stirling engine together with the required power electronic systems to connect an electrical generator with a power grid. This paper shows the modeling of the Stirling engine, a DC generator coupled to this engine, the DC-DC converter and the inverter required for connecting the cogeneration system based on Stirling engine to the grid. The synchronization algorithm to connect the inverter to the grid is also explained. The proposed model allows calculating the grid current and voltage, the active and reactive power generated using a Stirling engine as cogeneration system, according to its mechanical and thermodynamic parameters. The Stirling-generator system operates with practically unitary power factor. The aim of this work is to motivate the analysis and use of the Stirling engine in cogeneration.

Frequency-domain multiplexing was proposed to reduce the number of signals to be digitized in vector control using a resolver as position sensor. Signals from resolver and current sensors can be multiplexed through analog adders. This approach has many advantages over using analog switches. On the other hand, the conventional demultiplexing system involves filtering and delay compensation, while the rotor angle is obtained after the signal demultiplexing. However, this conventional approach is difficult to implement because the required filters must have good stopband rejection, without adding a significant phase delay. This paper proposes an improved demultiplexing system whose structure is simpler than the conventional approach. A type-III angle tracking observer estimates the rotor angle and the resolver outputs directly from the multiplexed signals. The estimated resolver outputs and stopband filters are used to get the current signals. Thus, the demultiplexing of resolver signals and the delay compensators used in the conventional approach were eliminated. Experimental results prove that the proposed approach gives accurate estimations of the rotor angle and current signals. Besides, conditions for the resolver excitation frequency, the inverter switching frequency, and for current synchronous sampling were defined for the application of frequency-domain multiplexing.

The present work deals with the simulation of a photovoltaic distributed generation system at the micro generation level, using a DC-AC power converter responsible for injecting active power into the grid and acting as a shunt active power filter, mitigating the harmonics of local nonlinear loads. For this, the system has a reference current generator that employs an algorithm based on the synchronous reference frame. The current reference has a distorted portion, corresponding to the distortion power of the nonlinear load added to the active portion to be injected to the grid. A PI plus multi-resonant controller are inserted to reproduce correctly the current reference. In order to enhance stability and waveform quality a genetic algorithm is adopted to find the best gains for the controllers. Simulations throught MatLab/Simulink® are presented to confirm the feasibility of the proposal.

Direct torque control (DTC) allows having a speed and torque control with good dynamic response. However, DTC uses a square root function to get the motor stator flux. This function is difficult to implement in microcontrollers or FPGAs. Hence, this paper proposes the use of SPLINE interpolation to implement the square root function with low computational cost, in order to get the magnitude of the stator flux. In SPLINE, the function is divided in intervals, and each interval is approximated using polynomial function. Besides, different discretization techniques to implement the integrator used in flux estimation will be tested in order to show their effects in flux and torque estimation. Simulations show that the proposed approach gives good estimations of torque and flux.

This paper presents comparative analysis among digital current controllers for single-phase grid connected photovoltaic inverters with L-filter. The considered controllers are proportional and integral controller (PI), proportional and integral plus resonant controller (PI+Res), deadbeat control (DBC) and model predictive control (MPC). The considered parameters for analysis are the total harmonic distortion (THD) for the injected grid current, phase and amplitude error in steady-state and during power reference step change. Simulations were made through Matlab/Simulink® platform to guide novel projects.

Photovoltaic generation systems under partial shading conditions are difficult to optimize using conventional maximum power point tracking (MPPT) algorithms. Most of the techniques developed for these conditions fail to track dynamically the MPP and leads to energy losses during normal operation. This paper presents hybrid MPPT algorithms combining global and local MPPTs to extract the most available energy from the system under any condition. The methods are compared through simulations using Matlab/Simulink®, where the tracking factor (TF) and power characteristics during time are evaluated.

Simplification in the acquisition system allows increasing the robustness and the sampling frequency of a motor drive system. Frequency domain multiplexing (FDM) allows reducing the number of signals to be digitized in vector control, simplifying the analog-to-digital system in a motor drive. Nonetheless, the required demultiplexing process is still an open question. This paper proposes a simple demultiplexing technique based on synchronous current sampling. The triangular carrier used in modulation and the resolver excitation signal are synchronized, so that the valley of the carrier happens when the resolver outputs are equal to zero. Thus, the demultiplexed current signals are equal to the samples obtained through synchronous current sampling, eliminating the harmonics in the current signals. The estimated current signals and an angle tracking observer are used to estimate the angular position without the previous demultiplexing of the resolver output signals. Simulation and experimental results show that the demultiplexing process has high accuracy, rejecting the current harmonics and gives a good estimation of the angular position.

This work presents the use of radial basis function artificial neural network to estimate the sensors readings, exploring the analytical redundancy via auto association. However, in order to guarantee good performance of the network the training and optimization process was modified. In the conventional training algorithm, although the stop criteria, 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 matrix of the neural network may not be satisfactory. The paper describes the proposed algorithm including all the mathematical foundation. A dataset of a petroleum refinery is used to train a RBF network using the conventional and the modified algorithm and the performance of both will be evaluated. Furthermore, AAKR model is used to the same dataset. Finally, a comparison study of the developed models will be done for each of the performance metrics, as well as for the overall effectiveness in order to demonstrate the superiority of the proposed approach.

This paper proposes a tracking angular position controller for Permanent Magnet Synchronous Motor (PMSM). Instead of linearizing the dq model of the motor, the proposed approach compensates the non-linear terms of the model in order to use robust linear control techniques. Thus, the control laws are composed by two components: the first term compensate the nonlinearities of the dq model, while the second component is defined by linear controllers to get the desired position and d-axis stator current. A type-II internal model controller is used to control the angular position on the motor. Hence, the motor can track even sinusoidal position references. Simulation and experimental results prove the accuracy of the proposed tracking controller.

This paper proposes a simple nonlinear technique to control the angular position and d-axis stator current of a permanent magnet synchronous motor. Instead of making a linearization according to an operation point, the control laws, i.e., the dq-axis stator voltages are composed by two terms. The first one is to compensate the nonlinearities of the PMSM. The second component of the control law is determined by a linear controller to get the angular position. Thus, the design of the controller becomes easier. Simulation results prove the accuracy and robustness of the proposed angular position controller.

Resolver is an angular position sensor widely used in electric/hybrid vehicles and robotics. However, estimating the angular position and speed from resolver outputs is as difficult task. Most algorithms in literature use trigonometric functions to estimate the angular position. However, these functions are difficult to implement, especially in FPGAs. On the other hand, in speed vector control of three-phase motors, the angular position is only used to estimate the mechanical speed and Park transformation matrix. This paper presents a simple technique to get the mechanical speed and Park matrix without calculating trigonometric functions and without estimating directly the angular position. Filtering and synchronous demodulation allows obtaining the sine and cosine of the angular position, while the compensation of the delay produced by filtering and the estimation of the Park matrix are done using simple trigonometric properties and taking advantage that the number of pole pairs is an integer number. Experimental results show the accuracy of the proposed approach.