A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA

Fuzzy logic controller for UAV with gains optimized via genetic algorithm

A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.

SCA-Safe Implementation of Modified SaMAL2R Algorithm in FPGA

Cryptographic algorithms (RSA, DSA, and ECC) use modular exponentiation as part of the principal operation. However, Non-profiled Side Channel Attacks such as Simple Power Analysis and Differential Power Analysis compromise cryptographic algorithms that use such operation. In this work, we present a modification of a modular exponentiation algorithm implemented in programmable devices, such as the Field Programmable Gate Array, for which we use Virtex-6 and Artix-7 evaluation boards. It is shown that this proposal is not vulnerable to the attacks mentioned previously. Further, a comparison was made with other related works, which use the same family of FPGAs. These comparisons show that this proposal not only defeats physical attack but also reduces the number of resources. For instance, the present work reduces the Look-Up Tables by 3550 and the number of Flip-Flops was decreased by 62,583 compared with other works. Besides, the number of memory blocks used is zero in the present work, in contrast with others that use a large number of blocks. Finally, the clock cycles (latency) are compared in different programmable devices to perform operations.

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition

Emotion recognition is a key attribute for realizing advances in human-computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.

Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control

Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due to the system's imperfection (i.e., unpredictable duty cycle) and influences the closed-loop performance. The methodology is executed offline; thus, it highlights the applicability of collected BDC motor measurements in time series analysis. The proposed method updates the PID controller gains based on the Simulink ™ controller tuning toolbox. The contribution of this approach is shown in a comparative study that indicates an opportunity to use time series analysis to forecast DC motor parameters, to re-tune PID controller gains, and to obtain similar performance under the same perturbation conditions. The research demonstrates the practical applicability of the proposed method for fine-tuning/re-tuning controllers in real-time. The results show the inclusion of the time series analysis to recalculate controller gains as an alternative for adaptive control.

Pipeline Vibration Control Using Magnetorheological Damping Clamps under Fuzzy–PID Control Algorithm

Aiming at the problem of low-frequency vibration of the hydraulic pipeline, a new type of semi-active damping magnetorheological (MR) damping clamp structure is designed. The structure size and material of the MR damping clamp were determined. The control model of the vibration damping system was established, and the control method combining fuzzy control and Proportional-Integral-Derivative (PID) control was used to carry out the numerical simulation, which proved that the fuzzy–PID control algorithm is effective and stable. The results show that the MR damping clamp proposed in this paper can effectively suppress the axial displacement and acceleration of the hydraulic pipeline in the excitation frequency range of 1 Hz~10 Hz. This research provides a new technical approach for low-frequency vibration control of hydraulic pipelines.

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

The motion control of high-precision electromechanitcal systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improve the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1 μm error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.

A Review of Deep Learning Algorithms and Their Applications in Healthcare

Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data. It was found that deep learning approaches can be used for big data analysis successfully. Applications include virtual assistants such as Alexa and Siri, facial recognition, personalization, natural language processing, autonomous cars, automatic handwriting generation, news aggregation, the colorization of black and white images, the addition of sound to silent films, pixel restoration, and deep dreaming. As a review, this paper aims to categorically cover several widely used deep learning algorithms along with their architectures and their practical applications: backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing. In addition, challenges of deep learning are also presented in this paper, such as AutoML-Zero, neural architecture search, evolutionary deep learning, and others. The pros and cons of these algorithms and their applications in healthcare are explored, alongside the future direction of this domain. This paper presents a review and a checkpoint to systemize the popular algorithms and to encourage further innovation regarding their applications. For new researchers in the field of deep learning, this review can help them to obtain many details about the advantages, disadvantages, applications, and working mechanisms of a number of deep learning algorithms. In addition, we introduce detailed information on how to apply several deep learning algorithms in healthcare, such as in relation to the COVID-19 pandemic. By presenting many challenges of deep learning in one section, we hope to increase awareness of these challenges, and how they can be dealt with. This could also motivate researchers to find solutions for these challenges.

Design and Implementation of Scalable and Parametrizable Analog-to-Digital Converter on FPGA

The flexibility provided by FPGAs permits the implementation of several ADCs, each one configured with the required bit resolution and sampling frequency. The paper presents the design and implementation of scalable and parametrizable analog-to-digital converters (ADC), based on a successive approximation register (SAR), on FPGAs (field programmable gate arrays). Firstly, the work develops a systematic methodology for the implementation of a parametrizable SAR-based ADC from a set of building modules, such as the pulse-width modulator (PWM), external low-pass filter (LPF) and the analog comparator. The presented method allows choosing the LPF parameters for the required performance (resolution bits and sampling frequency) of a SAR-based ADC. Secondly, the paper also presents several optimizations on the PWM module to enhance the sampling frequency of implemented ADCs, and the method to choose the LPF parameters is adapted. The PWM and SAR logic are synthesizable and parametrizable, using a low number of resources, in order to be portable for low-cost FPGA families. The methodology and PWM optimizations are tested on a Zynq-7000 device from Xilinx; however, they can be adapted to any other FPGA.

Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms

The present research develops the parametric estimation of a second-order transfer function in its standard form, employing metaheuristic algorithms. For the estimation, the step response with a known amplitude is used. The main contribution of this research is a general method for obtaining a second-order transfer function for any order stable systems via metaheuristic algorithms. Additionally, the Final Value Theorem is used as a restriction to improve the velocity search. The tests show three advantages in using the method proposed in this work concerning similar research and the exact estimation method. The first advantage is that using the Final Value Theorem accelerates the convergence of the metaheuristic algorithms, reducing the error by up to 10 times in the first iterations. The second advantage is that, unlike the analytical method, it is unnecessary to estimate the type of damping that the system has. Finally, the proposed method is adapted to systems of different orders, managing to calculate second-order transfer functions equivalent to higher and lower orders. Response signals to the step of systems of an electrical, mechanical and electromechanical nature were used. In addition, tests were carried out with simulated signals and real signals to observe the behavior of the proposed method. In all cases, transfer functions were obtained to estimate the behavior of the system in a precise way before changes in the input. In all tests, it was shown that the use of the Final Value Theorem presents advantages compared to the use of algorithms without restrictions. Finally, it was revealed that the Gray Wolf Algorithm has a better performance for parametric estimation compared to the Jaya algorithm with an error up to 50% lower.

An Open-Source Test Environment for Effective Development of MARG-Based Algorithms

This paper presents an open-source environment for development, tuning, and performance evaluation of magnetic, angular rate, and gravity-based (MARG-based) filters, such as pose estimators and classification algorithms. The environment is available in both ROS/Gazebo and MATLAB/Simulink, and it contains a six-degrees of freedom (6 DOF) test bench, which simultaneously moves and rotates an MARG unit in the three-dimensional (3D) space. As the quality of MARG-based estimation becomes crucial in dynamic situations, the proposed test platform intends to simulate different accelerating and vibrating circumstances, along with realistic magnetic perturbation events. Moreover, the simultaneous acquisition of both the real pose states (ground truth) and raw sensor data is supported during these simulated system behaviors. As a result, the test environment executes the desired mixture of static and dynamic system conditions, and the provided database fosters the effective analysis of sensor fusion algorithms. The paper systematically describes the structure of the proposed test platform, from mechanical properties, over mathematical modeling and joint controller synthesis, to implementation results. Additionally, a case study is presented of the tuning of popular attitude estimation algorithms to highlight the advantages of the developed open-source environment.