Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm by V. Rajinikanth, K. Latha

“…An enhanced bacteria foraging optimization (EBFO) algorithm-based Proportional + integral + derivative (PID) controller tuning is proposed for a class of nonlinear process models. …”
Get full text

Optimization of Effluent Phosphorus Control for Al-Aziziah (Wasit) Wastewater Treatment Plant by Mohammed Siwan Shamkhi

“… The combination between the enhance biological phosphorus removal EBPR process and chemical phosphorous precipitation process removal from wastewater to avoid the instability of the biological phosphorus removal process due to temperature variation has been simulated and optimized by implementing three different strategies in the GPS-X 5.0 modeling and simulation software (Hydro mantis). …”
Get full text

Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. by Bastien Berret, Dorian Verdel, Etienne Burdet, Frédéric Jean

“…In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. …”
Get full text

Joint beam hopping and coverage control optimization algorithm for multibeam satellite system by Guoliang XU, Feng TAN, Yongyi RAN, Feng CHEN

“…To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.…”
Get full text

Control optimization for permanent magnet synchronous generators in multi-source power systems by LI Lingyun, LIU Weizhi, WANG Yongxiang, LIU Yang

“…This paper presents the design optimization for the control of PMSGs from both strategy and controller perspectives. …”
Get full text

An optimization similarity fuzzy inference method for traffic signal control at an isolated intersection by Mahin Esmaeili, Ali Anjomshoae, Nasser Shahsavari-Pour, Punyaanek Srisurin, Ruth Banomyong

“…This study introduces the Optimization Similarity Fuzzy Inference (OSFI) method, which improves traffic signal control at isolated intersections by continuously adjusting fuzzy rules based on the similarity between actual and desired outcomes. …”
Get full text

A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems by Kaifeng Xie, Jinjing Wan, Lifeng Chen, Yi Wang

“…This paper proposes a test case generation method based on large language models (LLMs), which utilizes prompt templates and optimization algorithms to generate a code relevant to specific optimization passes, especially for real-time control logic and safety-critical modules unique to the industrial control field. …”
Get full text

A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle by Chien-Hsun Wu, Wei-Zhe Gao, Jie-Ming Yang

“…This platform also consists of the driving cycle, driver, lithium-ion battery, vehicle dynamics, and energy management system models. Two rapid-prototyping controllers integrated with the required circuit to process analog-to-digital signal conversion for input and output are utilized to carry out a hardware-in-the-loop (HIL) simulation. …”
Get full text

Advanced Optimization Methods for Nonlinear Backstepping Controllers for Quadrotor-Slung Load Systems by Muhammad Maaruf, Sulaiman S. Ahmad, Waleed M. Hamanah, Abdullah M. Baraean, Md Shafiul Alam, Mohammad A. Abido, Md Shafiullah

“…Using Lyapunov theory and the backstepping technique, it designs the controller optimally with precise thrust and angular velocity control laws to ensure the closed-loop system remains asymptotically stable. …”
Get full text

Bistable dynamics of TAN-NK cells in tumor growth and control of radiotherapy-induced neutropenia in lung cancer treatment by Donggu Lee, Sunju Oh, Sean Lawler, Yangjin Kim

Subjects: Get full text

Optimization and evaluation of mouse model construction method for severe periodontitis by WANG Jiaxuan, ZHANG Qianqian, SUI Baiyan, LIU Xin

“…Objective·To investigate an optimal severe periodontitis mouse model by comparing two induction methods: simple ligature and ligature combined with injection of Porphyromonas gingivalis lipopolysaccharide (P.g. …”
Get full text

Design and Practical Evaluation of Robust Model Predictive Wind Turbine Control by Andreas Klein, Thorben Wintermeyer‐Kallen, János Zierath, Kevin Kluge, Dirk Abel, Heike Vallery, Maximilian Basler

“…Due to ever more and ever larger wind turbines (WT), the requirements for WT operation become more complex. Model predictive control (MPC) for WTs shows the potential to handle these requirements and conflicting control objectives in a single optimization‐based controller. …”
Get full text

Optimal dispatch of distributed renewable energy and energy storage systems via optimal configuration of mobile edge computing by He Jiang, Yan Zhao, Hang Zhou

Get full text

Analytical forecasting of the optimal trajectory of mobile robot by A. A. Lobaty, D. V. Kapskiy, A. K. Ibrahim, I. A. Shishkovets

“…To achieve this goal, the trajectory of motion is considered to consist of separate intervals, at each of which the control optimization problem is solved. The optimization criterion in general form and its representation in the form of a minimized quadratic quality functional, convenient for analytical synthesis of control, are substantiated. …”
Get full text

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model by Surjeet Dalal, Umesh Kumar Lilhore, Neetu Faujdar, Sarita Samiya, Vivek Jaglan, Roobaea Alroobaea, Momina Shaheen, Faizan Ahmad

Get full text

Cost optimization model for multi-cloud network based on Kubernetes by Ming GAO, Ming LIU, Yangting CHEN, Weiming WANG

“…The cloud-native scheduling system, represented by Kubernetes, is widely used by cloud tenants in a multi-cloud environment.The problem of network observation becomes more and more serious, especially the cost of network traffic across cloud and region.In Kubernetes, the eBPF technology was introduced to collect the network data features of kernel state of operating system to solve the network observation problem, and then the network data features were modeled as QAP, a combination of heuristic and stochastic optimization was used to obtain the best near optimal solution in a real-time computing scenario.This model is superior to the Kubernetes native scheduler in the cost optimization of network resources, which is based on the scheduling strategy of computing resources only, and increases the complexity of scheduling links in a controllable range, effectively reduces the cost of network resources in a multi-cloud area deployment environment.…”
Get full text

Optimization model of mining operation scheduling for underground metal mines by LI Guo-qing, LI Bao, HU Nai-lian, HOU Jie, XIU Guo-lin

“…To realize the accurate and scientific optimization of mining production scheduling for underground metal mines, a mathematic model based on 0-1 integer programming was constructed in view of the characteristics of mining process, such as decentralized production sites, complex production organization and difficulty in controlling the quality of the ore. …”
Get full text

Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory by vahideh mousavi kakhki, Sanaz Khatabi

“…Investors' decision-making plays a pivotal role in portfolio construction, prompting researchers to explore factors that influence the selection of portfolios with high returns and controlled risk. Numerous models have addressed the optimization problem of stock portfolio management, each tailored to specific conditions and constraints. …”
Get full text

AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

“…This makes the model a promising candidate for integration into AI-driven device controllers as a precise and energy-efficient solution for memristive device programming.…”
Get full text

Application of Optimized Convolution Neural Network Model in Mural Segmentation by Zhiqiang Chen, Leelavathi Rajamanickam, Xiaodong Tian, Jianfang Cao

Get full text