AUT Journal of Modeling and Simulation
Year:2024 | Volume:56 | Issue:2|Papers Count:8

This paper presents a comprehensive investigation into the modeling and dynamic analysis of a XY mechanism, considering the potential occurrence of motor pulley slipping. The research commenced with the establishment of kinematic relationships among system components and the definition of virtual pulleys. By characterizing static and kinetic frictions for individual system elements, various rolling and slipping states were explored. The study unveiled that the system, excluding motor pulleys, experiences a stick-slip phenomenon due to friction between actuator components and the ground, resulting in deadzones during motion initiation. To address this, a novel friction model encompassing deadzones was introduced, and equations accounting for the stick-slip phenomenon were derived. Moreover, recognizing that each actuator motor can be in either a rolling or slipping state, it was established that the mechanism represents a hybrid-DOF dynamic system. The equations of motion switch between four modes, denoted as RR, SR, RS, and SS, contingent on input voltages, kinematic and dynamic variables, and friction coefficients between motor pulleys and belts. Calculating the required friction to maintain rolling was essential; insufficient friction between motor pulleys and belts leads to motor slipping. The determination of the system's subsequent dynamics considered different motor states, varying DOF, and the interplay of stick-slip phenomena with deadzones. Ultimately, dynamic analyses of the mechanism were conducted through simulations in three distinct scenarios. This study effectively highlights the nonlinear effects and complexities inherent in this mechanism, offering valuable insights into its behavior under different conditions.

Project management in software development is one of the most crucial activities as it encompasses the entire software development process from start to finish. Estimating the effort required for software projects is a significant challenge in project management. Managing software projects and consequently estimating their effort for more efficient and impactful management of such projects is necessary and unavoidable. Analogy-based estimation in software effort estimation involves comparing new projects to completed ones. However, this method can be ineffective due to variations in feature importance and dependencies. To address this, weights are assigned to features using optimization techniques like meta-heuristic algorithms. Yet, these algorithms may get stuck in local optima, yielding nonoptimal results. An approach to estimate software effort is proposed in this study. It aims to find global optimal feature weights by combining particle swarm and genetics metaheuristic algorithms. This hybrid approach leverages particle motion and composition to enhance solution generation, increasing the likelihood of finding the global optimum and overcoming local optima issues. The algorithm calculates feature weights for project estimation using analogy-based methods. The proposed approach was tested and assessed using two datasets, namely Maxwell and Desharnais. The experimental results indicated an enhancement in the evaluation criteria, including MMRE, MdMRE, and PRED, compared to similar research works.

Mimetic Coral Reefs Optimization (MCRO) has proven highly effective for feature selection due to its capacity to explore diverse solution spaces, enhancing model accuracy and robustness. However, integrating MCRO with local search techniques remains challenging, as it tends to be computationally intensive and prone to premature convergence. To address these issues, this paper introduces a Rank-based Adaptive Brooding (RAB) mechanism, designed to refine the local mimetic search strategy within MCRO. RAB adaptively adjusts the brooding operator based on the ranks of coral larvae, minimizing disruption to high-rank larvae and harnessing the exploratory potential of lower-rank larvae. This approach promotes a more balanced exploration-exploitation trade-off, leading to faster convergence and enhanced performance in complex problem spaces. The proposed method's efficacy is tested across eight UCI datasets using KNN, Decision Tree, and SVM classifiers, and the results are evaluated by precision, recall, and F1 score. Empirical results reveal that RAB outperforms existing adaptive strategies with fixed brooding, delivering superior feature selection performance, particularly in high-dimensional datasets. Additionally, the optimization capabilities of RAB were examined using 39 CEC benchmark functions, revealing consistent improvements in feature selection accuracy while demonstrating variable outcomes in broader optimization tasks. Notably, RAB showed significant enhancements in eight benchmark cases, highlighting its potential for broader applicability in optimization scenarios.

Biological evidence indicates that the actuation system in humans and legged animals is characterized by impulsiveness rather than continuity; i.e., control actions are concentrated within a specific phase of the motion cycle (the stance phase), while the rest of the cycle is passive. Based on this observation, we propose a simple event-based impulsive controller to generate walking cycles for legged robots. To improve optimization speed, we parametrize the controller-applied forces as a Gaussian function of time and employ a deep reinforcement learning method to optimize the controller parameters. To further enhance learning speed, an autoencoder is utilized to address the high dimensionality in the state space. Additionally, we employ a three-phase reward shaping approach to further improve learning speed and achieve better results. In phase one, the reward function focuses on stability and forward motion to learn stable locomotion. In phase two, the reward function is modified to achieve stable locomotion with lower control effort and desired forward velocity. In phase three, the reward function remains the same as in phase two but places more emphasis on forward velocity regulation. The proposed controller, state encoder, and learning process can be implemented on a group of legged robots with actuation at the leg contact point with the ground. In this paper, the proposed approach is tested on a simulated single-legged robot. Moreover, the controller robustness is analyzed considering different types of external disturbances. The simulation results indicate the efficacy of the proposed method as a bio-inspired control approach for legged locomotion.

The presence of bias in low-cost inertial sensors has always been a fundamental problem in the estimation of Euler angles. The transfer alignment technique involves using two sets of sensors: a set of low-cost sensors (which may have significant bias) and a set of high-accuracy sensors (which provide reliable data). By comparing the outputs of these two sets, the biases in the low-cost sensors can be estimated and corrected. To achieve this goal, it is necessary to obtain the difference between the master and slave frames. The “master frame” typically refers to the coordinate system defined by the high-accuracy sensors, while the “slave frame” refers to the coordinate system defined by the low-cost sensors. In this article, the method of small rotation angles is used to calculate this difference between the two frames. This article proposes a new alignment algorithm that is both fast and accurate. The biases of accelerometers and gyroscopes can be rapidly estimated and compensated for. The alignment discussed in this article is conducted during motion, which is more complex than stationary mode estimation. The desired maneuver in this study is a constant acceleration maneuver, where the biases of both the accelerometer and gyroscope are well estimated. For other maneuvers, gyroscope biases may not be accurately estimated, while accelerometer biases are generally well estimated. The estimated biases for accelerometers and gyroscopes are often noisy due to errors in the inertial sensors; therefore, preprocessing filtering is also performed to estimate the biases smoothly.

This research studies the model reference adaptive control strategy based on the fuzzy theory to control a wind turbine with a doubly-fed induction generator (DFIG). The model reference adaptive control method, incorporating Takagi-Sugeno (T-S) fuzzy logic, is proposed to control the turbine rotor speed using a pitch angle control. The aim of the proposed control system is to address the shortcomings of the traditional wind turbine controllers, such as unknown dynamics and system nonlinearities. The proposed hybrid adaptive-fuzzy structure provides an effective tool for controlling the wind turbine system, which exhibits complex nonlinear dynamics. The superiority of the proposed method over the traditional model reference adaptive control lies in modeling the nonlinear system with multiple fuzzy linear models instead of a single linear model. Additionally, the use of the fuzzy method enhances the adaptability of this control method, resulting in more accurate outcomes. Stability analysis of the closed-loop system with the proposed fuzzy model reference adaptive control (FMRAC) is conducted using the Lyapunov method. The proposed FMRAC method is simulated for a 0.2 Mw variable speed wind Turbine and compared with the traditional model reference adaptive control. The simulation results of the proposed method demonstrate higher performance and an accurate response despite the unknown dynamics and nonlinearities of the model.

This study examines the multifaceted challenges posed by Bitcoin’s rapid growth, focusing on economic, social, and regulatory issues, including illegal mining, high energy consumption, security vulnerabilities, and tax evasion. Through a System Dynamics (SD) modeling approach, the research explores the feedback loops and interactions within the cryptocurrency ecosystem. Key findings emphasize that strategic regulatory interventions are vital for balancing innovation with sustainability, mitigating environmental impacts, and ensuring power grid stability. The study underscores the role of evidence-based policymaking (EBPM) in formulating data-driven policies for the cryptocurrency sector. By integrating EBPM with SD modeling, the research provides actionable insights for policymakers to understand the broader implications of mining efficiency, network difficulty, and supply growth within the Bitcoin ecosystem. Four distinct scenarios were modeled to analyze varying outcomes influenced by technological advancements and miner participation. Scenario 1 depicts stable growth achieved through gradual increases in mining difficulty, resulting in limited yet sustainable expansion. Scenario 2 highlights the advantages of rapid technological adoption, driving short-term growth while raising concerns over energy consumption and market saturation. Scenario 3 focuses on long-term stability with a balanced growth strategy, ensuring sustainability but at the cost of slower expansion. Scenario 4 illustrates how rapid technological advancements can lead to increased mining activity, though accompanied by resource strain and diminishing returns. These findings stress the importance of adopting a balanced approach to managing Bitcoin mining. Policymakers, miners, and investors must align technological growth with sustainability to maintain the Bitcoin network’s long-term stability and profitability. Establishing a unified policymaking framework is critical for effective governance and coordination among stakeholders, including the Central Bank, Ministry of Energy, and Ministry of Industry. Additionally, public education campaigns on digital currencies and blockchain technology are imperative for fostering informed participation and mitigating misuse.

This paper aims to model and sliding mode control of a roll yaw seeker . roll yaw seeker is a tow axis gimbaled seeker . In the roll yaw seeker , a singularity occurs when the seeker is directed precisely to a target , and the seeker will lose the target . Thus, the controller design should include a tracking strategy to deal with singularity . In this paper , Newton Euler's method applies the dynamic model of a roll yaw seeker's roll and yaw gimbals . The dynamics of the roll yaw seeker are hardly nonlinear . Also , unmodified uncertainties and perturbations reduce the model's reliability . A two input , two output integral sliding mode controller is designed to control the nonlinear dynamics of the seeker and deal with uncertainties . The numerical simulation results show that all three stabilization, tracking , and guidance loops in both roll and yaw channels have acceptable performance . Also , it is shown that the controller has good robustness in uncertainty which is applied in every component of inertial momentum matrix. It shows the controller design and tracking strategy performed effectively in the roll yaw seeker and its singularity problem .