Loop Algorithm Research Articles

Energetically Optimal Trajectory for a Redundant Planar Robot by Means of a Nested Loop Algorithm

This work presents an optimal point-to-point trajectory planning method based on fourth degree polynomials to reduce the energy consumption of a redundant planar robot in the XY plane. The contribution of this study focuses on the development of a two-level nested optimization algorithm, which manages to optimize three elements: the Optimal Weighting Vector that establishes the influence of each joint on the total energy consumption of the robotic equipment; the optimal start and finish configurations of the trajectory; and the vector corresponding to the fifth coefficient of the trajectory generator polynomial. The tests are carried out with different optimization techniques and objective functions related to energy consumption to determine their best combination and achieve the minimum possible computation time and energy savings, according to the test conditions used. Furthermore, the energetic performance of the redundant manipulator is compared with its non-redundant version, limiting its operation to only two degrees of freedom in all its variants.

Low-Sidelobe Shaped-Beam Pattern Synthesis With Amplitude Constraints

The article deals with the low-sidelobe shaped-beam pattern synthesis (SBPS) problem under amplitude constraints, including the controllable dynamic range ratio (DRR) and the predetermined excitation amplitude. The formulated problem is nonconvex owning to the existing nonconvex constraints. To this end, a set of auxiliary variables are introduced to reformulate the original problem, and then a double-loop iterative algorithm based on the penalty dual decomposition method (PDD) framework is proposed. The proposed approach solves the reformulated problem by updating primal variables with block successive upper-bound maximization (BSUM) algorithm in the inner loop and updating the dual variables and penalty parameter with the constraint violation strategy in the outer loop. One important advantage of the proposed approach is that all of its subproblems have closed-form solutions, which guarantees that the reformulated problem can be effectively solved. Numerical simulations are carried out to validate the proposed approach’s superiority with both isotropic element pattern and active element pattern (AEP). The results show that the proposed approach can obtain a lower sidelobe level (SLL) and a smaller DRR when compared with the state-of-the-art approaches.

Sliding Mode Control of Grid-Connected Neutral-Point-Clamped Converters Via High-Gain Observer

In this article, a nonlinear high-gain observer (NHGO)-based second-order sliding mode (SOSM) control strategy is proposed for the three-phase three-level neutral-point-clamped (NPC) converter. This controller applies the advanced SOSM algorithm both in the voltage regulation loop and in the power tracking loop, which provides a fast dynamic for the dc-link voltage, and also assures a good steady-state behavior for the NPC converter. Additionally, an NHGO technique is implemented in the voltage regulator combining with the SOSM algorithm. The conventional observer-based controllers suffer from the destructive effects of measurement noise, and it can only be addressed by diminishing the observer gain, which sacrifices the observer property. The NHGO technique adopts a time varying gain, that is, high gain in transient while low gain in steady state, which minimizes the adverse influence of measurement noise. The tuning method of the proposed NHGO-based SOSM controller is given to simplify the implementation process. Finally, the simulation and experimental results of the proposed control scheme for the NPC converter are given and compared with the conventional PI controller as well as the well-known linear extended state observer-based control method, which validates the feasibility and superiority of the proposed controller.

Three-Phase PLL Based on Adaptive Clarke Transform Under Unbalanced Condition

This letter proposes a three-phase phase-locked loop (PLL) algorithm relying on an adaptive Clarke transform (CT) under amplitude and/or phase angle unbalanced condition. Analytical expressions for coefficients of matrix associated with the adaptive CT are derived and used to generate orthogonal signals. An auxiliary algorithm is also proposed to track the actual amplitudes and phase angle deviations. Unlike the conventional CT relying on constant coefficients, the adaptive CT can generate accurate orthogonal signals from the three-phase voltages including phase angle and/or amplitude imbalances. As a result, the proposed PLL is not affected by the unbalanced phase angles with/without amplitude imbalances. The advantages of the proposed adaptive CT-based PLL method is illustrated by both simulation and experimental results.

An Optimized Enhanced Phase Locked Loop Controller for a Hybrid System

The use of renewable energy sources is the need of the hour, but the highly intermittent nature of the wind and solar energies demands an efficient controller be connected with the system. This paper proposes an adept control algorithm for an isolated system connected with renewable energy sources. The system under consideration is a hybrid power system with a wind power harnessing unit associated with a solar energy module. A controller that works with enhanced phase locked loop (EPLL) algorithm is provided to maintain the quality of power at the load side and ensure that the source current is not affected during the load fluctuations. EPLL is very simple, precise, stable, and highly efficient in maintaining power quality. The double-frequency error which is the drawback of standard phase locked loop is eliminated in EPLL. Optimization techniques are used here to tune the values of the PI controller gains in the controlling algorithm. Tuning of the controller is an important process, as the gains of the controllers decide the quality of the output. The system is designed using MATLAB/SIMULINK. Codes are written in MATLAB for the optimization. Out of the three different optimization techniques applied, the salp swarm algorithm is found to give the most suitable gain values for the proposed system. Solar power generation is made more efficient by implementing maximum power point tracking. Perturb and observe is the method adopted for MPPT.

Soft multi-modal thermoelectric skin for dual functionality of underwater energy harvesting and thermoregulation

Sustaining a long-term but viable power source for underwater electronics has been an engineering conundrum due to a lack of feasible physical mechanisms to harvest energy in the underwater environment. In this regard, thermoelectricity, which converts heat into electricity, can suggest a potent solution, since the ocean and other water bodies maintain the constant water temperature and therefore serve as a permanent thermal differential. In addition to energy harvesting, the same thermoelectric device can be also utilized to both heat and cool, thereby providing an effective means of controlling the temperature of the arbitrary subject in the underwater environment. In this light, we present a soft and stretchable multi-modal thermoelectric skin (TES) that can both (i) generate electricity across the temperature differential between the ocean water and the human body and (ii) thermoregulate the body temperature in the underwater environment. The soft and elastic nature of TES enables an intimate and thorough contact with the deformable and irregular surfaces of the human skin, therefore maximizing the heat conduction at the human-device interface that the rigid or flexible thermoelectric devices can not fully attain. To the authors’ best knowledge, TES produces the highest electrical power density when compared to the stretchable thermoelectric devices reported so far, mainly owing to its optimum design factors. Along with the outstanding device performance, the underwater environment further boosts the thermoelectric efficiency of TES both in energy harvesting and thermoregulatory perspectives due to the much more favorable thermal properties of water than those of air. Furthermore, to verify the practical usage of TES and demonstrate its high wearability, we incorporated multiple TES units into the neoprene dry-suit. The TES units can self-power multiple embedded sensors that wirelessly monitor the physiological condition and further provide the spatial information of the tactical diver, such that the TES units and embedded system can function as a wearable underwater rescue platform. Lastly, the temperature feedback loop algorithm embedded in the thermoregulatory system allows the TES units to constantly regulate the temperature of the human body and thus prevent underwater hypo-/hyperthermia.

An integrated surrogate model constructing method: Annealing combinable Gaussian process

Surrogate models are widely used to mimic complex systems to reduce the high experimental cost. As the system becomes high-dimensional and complex, there is an increasing demand for building relatively simplified surrogate models that capture key variables and represent complex interactions. This study proposes the annealing combinable Gaussian process, an integrated solution for identifying key variables and constructing the high-precision surrogate model. Firstly, to identify redundant variables, this study optimises variables selection with a modified simulated annealing algorithm over the complete model space. This process is called the outer loop. Secondly, to improve the model accuracy and structure, this study constructs a non-parametric Gaussian process model with additive or multiplicative kernel, effectively extracting high-order interactions. Simultaneously, a Markov chain is proposed to sample the model space. The conditional entropy is used as the scoring rule for the simulated annealing algorithm of the outer loop. It is named the inner loop. This study also discusses the rationality of conditional entropy as a criterion. The annealing combinable Gaussian process performs well in various application scenarios, including regression and classification problems. Finally, our method is implemented with the particle-in-cell simulation to find out the key physics parameters.

A Digital Frequency Locked Loop With Minimum Computation Overhead for Heavily Distorted Single-Phase Grid Systems

Grid-connected converters require grid frequency and phase information for controlling the power injections into the grid. This information is achieved by a phase-locked loop (PLL) or frequency-locked loop (FLL) algorithms. The accuracy, reliability, and ease of implementation of the PLL/FLL algorithms in all grid conditions dictate the performance of the grid-connected converters. This article proposes a digital FLL for single-phase grid-connected systems. The proposed FLL tracks the frequency accurately, even in heavily distorted grid conditions. It uses a second-order generalized integrator (SOGI) as a pre-filter and a digital counter, based on the zero-crossing detection (ZCD) logic for estimating the grid frequency and phase angle. It does not require complex filters and tuning of multiple parameters. The implementation of the proposed FLL is simple and has less computational overhead. The ability of the proposed FLL to track the frequency and phase under various steady-state (dc-offset and harmonics) and transient (voltage sag, frequency drift, and phase jump) grid disturbances is tested in simulation using MATLAB/SIMULINK, and the results are reported. The experimental validation is done using a field programmable gate array (FPGA) controller in the laboratory. The proposed algorithm is able to track the estimates within 1.2 cycles of the grid voltage when tested under a heavy-distorted grid having 26.4% total harmonic distortion (THD). The measurement uncertainties and results of single-phase grid-connected inverter (GCI) using the proposed FLL are also presented.

Application of C1DAE-ANIL in End-to-End Communication of IRS-Assisted UAV System

Due to the uninterrupted flight of the unmanned aerial vehicles (UAV) and the varying phase of the intelligent reflecting surface (IRS) in the IRS-assisted UAV communication system, it is very difficult to estimate the channel information of the system accurately. The traditional solutions try to model the whole communication system as an autoencoder including encoder, channel, and decoder. However, the autoencoder needs to be retrained when the environment changes and the generalization ability is lousy. To solve above problems, a novel C1DAE-ANIL algorithm is proposed to estimate the channel information of the IRS-assisted UAV communication system. On one hand, one-dimensional convolution is introduced to the traditional multi-perceptron autoencoder (MLPAE) for enhancing the encoding and decoding performances of the one-dimensional convolutional autoencoder (C1DAE). On the other hand, the meta-learning is applied to the C1DAE, and the almost no inner loop (ANIL) algorithm is used to find an optimal initial parameter vector of the C1DAE to realize the rapid training in dynamic environments. Compared with the existing contenders, the simulation results show that the end-to-end communication model using the C1DAE-ANIL algorithm not only improves the accuracy of channel estimation but also greatly accelerates the training process in dynamic environments.

Design and Experimentation of a Single-Phase PLL With Novel OSG Method

Different phase-locked loop algorithms applied to three-phase grid voltages implement a closed control loop based on the Park transform to obtain the grid voltage instantaneous phase and frequency. When a single-phase grid voltage must be processed, one of the inputs of the Park transform is generated by a block that, starting from the available voltage, computes an additional signal with the same frequency of the grid voltage and ideally orthogonal to it. This paper introduces a novel method for the orthogonal signal generation and gives a detailed analysis of its functioning. Then, after sizing the control loop of the phase-locked loop, the paper considers different aspects relevant to implementation of the presented orthogonal signal generation and of the phase-locked loop on a digital signal controller, such as the finite numerical resolution, the memory usage and the computation time. Finally, the paper checks the comprehensive performance of the orthogonal signal generation and phase-locked loop pair by experimental tests and compares the obtained results with those available in the literature.

Investigation of robust ship course control based on auxiliary loop method

A novel robust control law is investigated on the problem of ship stabilization on the trajectory, which allows one to compensate perturbations in the parameters of the mathematical model of ship dynamics in cases of their changes caused by external conditions, namely: sea waves, currents, wind gusts, etc. To implement the proposed control law, only measured adjustable values are required such as the yaw angle and the control action that is the angle of the rudder of the ship. The synthesized ship course control system was investigated in MATLAB. The law of controlling the ship’s course with unknown parameters and external disturbances in the power supply is proposed. The design of the control law is based on a robust auxiliary loop algorithm and Khalil observers. The simulations illustrate the efficiency of the proposed control law.

Optimization loop based on adjoint sensitivity analysis for flows through porous media with adsorption

AbstractIn the past few years, the development of inverse design and optimization methods has opened up new possibilities. The adjoint method is of great significance in that context, since it permits high fidelity to flow‐physics at comparatively low computational costs. The present work focuses on adsorbed gas storage systems. Its contribution is the development of an alternative approach to the adjoint contour problem, which ensures that the latter is as well‐posed as the physical problem, itself. This approach proves to be fully consistent with Cacuci's methodology for computing sensitivity derivatives, which enables us to extend their scope. It allows for the evaluation of sensitivities with respect to parameters other than those pertaining to geometry, that is, to those that control the system operation such as average mass flux, convective heat transfer, inlet and external temperature. The main goal of this work is to obtain and validate a basic structure of an optimization loop algorithm (OLA) based on adjoint method, applied to adsorption natural gas storage systems, with pressure and density of adsorption as objective functions.

A filter algorithm for receiver tracking loops assisted by inertial information

AbstractIn traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.

An Improved Empirical Mode Decomposition Algorithm Based on Frequency Rotation Factor and Vector Operation in Complex Plane

In order to overcome the phenomenon of aliasing mode in empirical mode decomposition (EMD), an improved EMD method based on frequency rotation factor and vector operation is proposed. First, fast Fourier transform (FFT) is used to determine the frequency components contained in the original signal. Second, Hilbert transform is applied to transform the real plane analysis into complex plane analysis, then the amplitude frequency product of the signal is satisfied with adaptive full decomposed by frequency inversion and vector operation. Third, the traditional EMD algorithm is used to extract the lowest frequency mode in the complex frequency domain while it is the highest frequency in the time domain (because the frequency inversion operation is performed in the previous step), and finally the original mode of the real plane is restored by forward frequency rotation and inverse vector operation, so as to complete all modal decomposition through this loop algorithm. The simulation results show that the method can effectively separate the aliasing problem and extract the useful signals with low time cost.

Optimal drivetrain design methodology for enhancing dynamic and energy performances of dual-motor electric vehicles

Electric vehicles (EVs) are a dominant transportation trend towards a low-carbon future. However, there is a lack of systematic studies for full-electric off-road vehicles in terms of drivetrain design and energy management issues. This paper proposes a novel methodology to develop a drivetrain system for a four-wheel-drive (4WD) dual-motor off-road EV. The obtained design delivers high performance in both driving performance and energy efficiency of a desired vehicle using the two electric motors with their multi-speed gearbox. The proposed approach is to minimize the power envelope (force-speed characteristic) difference between a targeted model and the system under study. As a result, the objective function finds an optimal solution for a proper set of gear ratios and constant power speed ratio (CPSR) through a two-layer hierarchical scheme. The upper layer sets the targeted power envelope based on the expected driving measures. The lower layer minimizes the gap of the power envelope characteristics between the targeted vehicle and the dual-motor vehicle. In parallel, two optimization loops are implemented at the lower layer. The outer loop determines the proper value of CPSR, meanwhile, the gear ratios corresponding to the selected CPSR are optimized by a particle swarm optimization (PSO) algorithm in the inner loop. The results verify that the dynamic and energy performances of the studied dual-motor vehicle are enhanced over critical testing scenarios. In a comparison with the single-motor single-speed gearbox configuration, the optimized design shows that the top speed is 55% higher; the acceleration time to 45 km/h is reduced by 55.83%; and the gradeability is improved by 162.5%. Moreover, the test under a typical off-road cycle reveals a 1.9% overall efficiency increase compared to the dual-motor drivetrain using the same non-optimized gear ratio. The proposed comprehensive design approach has the potential to be applied to a wide range of vehicle areas.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms.

User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

The search for causality: A comparison of different techniques for causal inference graphs.

Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A neuroendoscopic navigation system based on dual-mode augmented reality for minimally invasive surgical treatment of hypertensive intracerebral hemorrhage

Background and objectiveHypertensive intracerebral hemorrhage is characterized by a high rate of morbidity, mortality, disability and recurrence. Neuroendoscopy has been utilized for treatment as an advanced technology. However, traditional neuroendoscopy allows professionals to see only tissue surfaces, and the field of vision is limited, which cannot provide spatial guidance. In this study, an AR-based neuroendoscopic navigation system is proposed to assist surgeons in locating and clearing hematoma. MethodsThe neuroendoscope can be registered through the vector closed loop algorithm. The single-shot method is designed to register medical images with patients precisely. Real-time AR is realized based on video stream fusion. Dual-mode AR navigation is proposed to provide comprehensive guidance from catheter implantation to hematoma removal. A series of experiments is designed to validate the accuracy and significance of this system. ResultsThe average root mean square error of the registration between medical images and patients is 0.784 mm, and the variance is 0.1426 mm. The pixel mismatching degrees are less than 1% in different AR modes. In catheter implantation experiments, the average error of distance is 1.28 mm, and the variance is 0.43 mm, while the average error of angles is 1.34°, and the variance is 0.45°. Comparative experiments are also conducted to evaluate the feasibility of this system. ConclusionThis system can provide stereo images with depth information fused with patients to guide surgeons to locate targets and remove hematoma. It has been validated to have high accuracy and feasibility.

Genetic algorithm-based strategy for the steam reformer optimization

The steam reforming reaction is widely used for obtaining hydrogen. The reforming reaction has a strong endothermic character, which means it requires a considerable and continuous heat supply to proceed. Due to the process character, a highly non-uniform temperature field develops inside the reactor. It has a consequence in large temperature gradients, leading to the catalyst degradation and a reduced lifetime of the reforming unit. The aim of the presented research is to unify the temperature field developing in the reactor, for easier control of the process and extension of the reformer's life expectancy. A conventional plug-flow reactor consists of a cylindrical pipe body filled with catalyst. The presented methodology included optimizing the catalyst distribution in the reactor to acquire the most uniform temperature field possible. A genetic algorithm is selected as an optimization technique for finding the most advantageous alignment of the catalyst. It is an example of evolutionary algorithms, basing on rules similar to natural selection. The algorithm generates a random, initial population of reactors and the reforming simulation is executed for each of them. The computation results are then evaluated and ranked using predefined fitness functions. The ranked reactors parameters' are further recombined with selection probability based on the fitness values, until a whole new population is created and the algorithm's loop restarts. The higher the fitness value of a specific reactor, the higher are the chances of passing its segments composition to the proceeding generation. The fitness computation leaves a vast space for improvements, as it may be computed based on many different process' parameters. This work focuses on distinguishing differences in the algorithm performance, depending on the formula for fitness calculation. The algorithm's converging speed, overall fitness values of specimens and optimization results were investigated and compared. The results show that the algorithm with an updated fitness calculation procedure performs considerably better. Only about 50% of computational time was required, to acquire results of the same quality for the presented numerical cases, when comparing with the previously prepared procedure.

Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing.

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.