Intelligent Control Algorithm Research Articles

Intelligent control algorithms to ensure the flight safety of aerospace vehicles.

Intelligent control algorithms to ensure the flight safety of aerospace vehicles.

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Device-free crowd counting using multi-link WiFi CSI for occupant-driven energy management of HVAC systems

ABSTRACT Accurate estimation of the number of occupants in a building’s thermal zone is a crucial input for the dynamic estimation of the cooling load of a heating, ventilation, and air conditioning equipment. This data, in conjunction with an intelligent control algorithm, can be judiciously employed for enhancing occupant comfort and achieving substantial energy savings through demand-driven operation. In this work, we propose a WiFi channel state information (CSI) based passive people counting model that utilizes a wavelet-based signal denoising scheme and an artificial neural network (ANN) model for automatic feature extraction. The application of the proposed model on a public dataset shows more than 99% accuracy in detecting up to eight people in three rooms of varying sizes. The proposed model was also validated on CSI samples collected from a laboratory environment with five occupants, using ESP32, a low cost and low power IoT device. The results show that the proposed model can perform people counting with an accuracy of up to 98%, using single- and multi-link measurements.

Intelligent control of the Magnus anti-rolling device: A co-simulation approach

In this study, we present an artificial intelligence control method specifically designed for the Magnus anti-rolling device. The core of our approach is the development of a co-simulation framework that integrates an intelligent algorithm with a three-dimensional numerical computational programme within a complex hydrodynamic environment. This integration is achieved through the use of a deep reinforcement learning algorithm, which allows for intelligent adaptation of the anti-rolling device's rotating speed in real time. Our research focuses on evaluating the performance of the intelligent anti-rolling control algorithm under a variety of conditions, including different ship-model roll angles, column geometry models, and varying swinging or slewing speeds. Through numerical studies, we compare the effects of these variables on the effectiveness of the control method. The co-simulation technology provides a platform for testing the intelligent control method. It allows a detailed examination of how the intelligent algorithm interacts with the hydrodynamic responses of the Magnus anti-rolling device, ensuring that the control method can adapt to a wide range of operational scenarios. This adaptability is crucial for maintaining stability and improving the performance of the anti-rolling device in real maritime environments. This study highlights the potential for integrating artificial intelligence with traditional maritime engineering solutions.

Implementation of smart wheelchair with obstacle avoidance

The primary goal of this work is to develop a dependable and efficient solution for maintaining optimal mobility in wheelchair systems. The aim is to enhance functionality and reduce the user’s physical effort required to move the wheelchair wheels. The proposed system integrates advanced sensing technology, intelligent control algorithms, and robust hardware components to manage the wheelchair, detect obstacles, and eliminate the need for external assistance. This wheelchair employs joystick control and halts when the system senses an obstacle. The system enhances user autonomy by providing ease of control and ensures safety by detecting and avoiding obstacles. Motor speeds are adjusted based on joystick input, and the motor direction (forward, backward, left, right) is determined accordingly. An ultrasonic sensor measures obstacle distance, and if an obstacle is detected (distance <= 20cm), both motors stop, and a warning buzzer activates for 1 second. If no obstacle is detected, the warning buzzer turns off, and motor speeds are adjusted based on joystick input. The system effectively demonstrated obstacle avoidance by changing direction when a command was issued away from the obstacle. The outcomes of this study contribute valuable insights to the field of wheelchairs, specifically in the realm of obstacle avoidance.

Application of Artificial Intelligence and Fuzzy Control Algorithm in Green and Low-Carbon Highway Construction

Smart highways endorse green and low-carbon handling construction infrastructures for promoting pollution-free environments and driving spaces. With the incorporation of artificial intelligence and smart computing features, sophisticated decision systems improve the aim of such smart highway constructions. For promoting green and low-carbon highway construction infrastructure, this research article introduces a pollution control-facilitated recommendation system (PCFRS). The system aims to satisfy eco-friendly driving demands and low-carbon emission requirements. In this system, eco-friendly driving demands and low-carbon emissions are the prime requirements for designing such highways. The research employs fuzzy control algorithms to analyze the relationship between green demands and demand satisfaction factors across different infrastructures based on policies from environmental departments and governing agencies. The green demands as formulated by the environmental department/ governing agencies are used for verifying the demand and relationship factors across various infrastructures. The fuzzy algorithm provides recommendations for optimal highway infrastructure design by identifying the maximum possible combinations of satisfaction factors, enabling cost-effective construction while meeting green environment and low-carbon emission goals. This process is aided by a fuzzy control algorithm with the relationship and demand factor as crisp inputs. The decisions on the maximum possible combinations of the satisfaction factor are used for infrastructure recommendations. The need for optimal design and promoting green environments are optimal across various highway lanes.

Conductive hydrogels‐based self‐sensing soft robot state perception and trajectory tracking

AbstractSoft robots face significant challenges in proprioceptive sensing and precise control due to their highly deformable and compliant nature. This paper addresses these challenges by developing a conductive hydrogel sensor and integrating it into a soft robot for bending angle measurement and motion control. A quantitative mapping between the hydrogel resistance and the robot's bending gesture is formulated. Furthermore, a nonlinear differentiator is proposed to estimate the angular velocity for closed‐loop control, eliminating the reliance on conventional sensors. Meanwhile, a controller is designed to track both structural and nonstructural trajectories. The proposed approach integrates advanced soft sensing materials and intelligent control algorithms, significantly improving the proprioception and motion accuracy of soft robots. This work bridges the gap between novel material design and practical control applications, opening up new possibilities for soft robots to perform delicate tasks in various fields. The experimental results demonstrate the effectiveness of the proposed sensing and control approach in achieving precise and robust motion control of the soft robot.

Microneedles-Based Theranostic Platform: From the Past to the Future.

Fully integrated theranostic devices are highly esteemed in clinical applications, offering immense potential in real-time disease monitoring and personalized care. Microneedles (MNs), as innovative and wearable devices, boast important advantages in biosensing and therapy, thus holding significant promise in the advancement of diagnostic and therapeutic platforms. Encouragingly, advancements in electrochemical sensing technology, micronano fabrication, and biocompatible materials are propelling momentum for MNs-based closed-loop systems, enhancing detection capabilities, biocompatibility, and cost-effectiveness. Moreover, the notable progress in integrating MN chips with other biochips signifies a frontier for growth. Successful clinical trials in target molecule monitoring and drug delivery domains herald excellent clinical translational prospects for the aforementioned theranostic platform. Finally, we delineate both challenges and opportunities in the development of integrated diagnostic and therapeutic MN systems, including continuous monitoring, intelligent control algorithms, safety, and regulatory considerations.

Intelligent control algorithm for industrial robots when performing the assembly operation of cylindrical non-rigid parts

Intelligent control algorithm for industrial robots when performing the assembly operation of cylindrical non-rigid parts

Application of Local Search Particle Swarm Optimization Based on the Beetle Antennae Search Algorithm in Parameter Optimization

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

Crop Monitoring System with Water Moisture Levels Using Controller

This paper presents a Crop Monitoring System (CMS) designed to effectively manage crop water moisture levels through automated irrigation control. The system incorporates soil moisture sensors and environmental sensors for real-time data collection, which is then processed by a controller to determine optimal irrigation schedules and water application rates. By integrating predefined thresholds and algorithms, the controller ensures precise irrigation tailored to specific crop and soil conditions, thus preventing both under- watering and over-watering. Additionally, the CMS features wireless communication capabilities, enabling remote monitoring and adjustment of irrigation settings by farmers. This remote accessibility facilitates timely interventions and optimizations, even in the absence of farmers in the fields. Overall, the CMS enhances crop health, improves resource efficiency, and conserves water in agriculture. By leveraging advanced sensor technologies and intelligent control algorithms, this system offers a sustainable solution for precision agriculture, contributing to increased yields while minimizing water usage and environmental impact. This project intends to make farming as mechanized and labor-free as possible.

Design and implementation of an Autonomous Systems Training Environment framework for control algorithm evaluation in autonomous plant operation

The shortage of trained plant operators who can control complex systems in the process and energy industry is leading to an increasing need for more autonomy of such plants. In future, such systems will be controlled by autonomous agents executing intelligent control algorithms. Due to the critical nature of such plants and processes, new control strategies should not be introduced untested. In order to test suitable algorithms, robust and comprehensive simulation environments are required. In this paper, a framework, called Autonomous Systems Training Environment, is proposed for the evaluation of control algorithms in autonomous plant operations. Furthermore, an exemplary use case for a process engineering system is implemented in Matlab/Simulink, taking into account different levels of control (e.g., regulatory control, operator control, safety control). Faults, which represent a major challenge in the autonomous control, are also considered.

ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement

In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).

Case Study of Solar Integration in HVAC Systems: Efficiency and Sustainability Outcomes

This study looks into the environmental advantages, economic feasibility, and technical viability of using solar energy to charge HVAC (Heating, Ventilation, and Air Conditioning) batteries. It also covers issues like the unpredictable nature of solar energy and the requirement for energy-storage technology. This study adds to the ongoing efforts to develop a more resilient and sustainable energy infrastructure by encouraging the incorporation of renewable energy sources into HVAC systems. PV (Photovoltaic) panels collect solar energy, a charge controller manages energy effectively, and a battery storage unit is adapted to meet HVAC needs make up the system. The purpose of solar energy integration into HVAC systems is to decrease dependency on the traditional grid, reduce environmental effects, and improve overall energy efficiency. Intelligent charge control algorithms, real-time monitoring, and predictive maintenance capabilities are some of the solar-powered charging system's key characteristics. These characteristics ensure that the HVAC system runs smoothly and leave as little of an environmental impact as possible while optimizing energy utilization. The system's scalability and adaptability to a wide range of customer needs enable it to serve HVAC applications in both residential and commercial settings.

Study on indoor temperature optimal control of air-conditioning based on Twin Delayed Deep Deterministic policy gradient algorithm

The optimization of HVAC system control affects the thermal comfort of building environment and air-conditioning energy consumption. However, most existing studies concentrate on human thermal comfort and neglects to incorporate air-conditioning energy consumption required to establish a controlled indoor thermal environment within the policy, which results in air-conditioning system high energy consumption. This paper suggests an indoor temperature intelligent control method based on thermal comfort and energy consumption of air-conditioning, and the TD3 algorithm is employed as an intelligent control algorithm to obtain the optimal setpoint that maximizes the joint benefit of the occupant’s thermal comfort and the energy saving rate of air-conditioning system. The air-conditioning indoor temperature intelligent control model based on TD3 algorithm is trained by using the experimental data, and the method is simulated in TRNSYS simulation software to corroborate the efficacy and energy saving rate of the method. The results indicate that the indoor temperature intelligent control model based on thermal comfort and energy consumption of air-conditioning has less optimal adjustment of indoor temperature setpoints, which is more stable for the air-conditioning system and can satisfy the thermal comfort demand at the initial control stage to prevent significant fluctuations in human thermal sensation compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation. Besides, the average daily energy consumption can be reduced by 6.54% and 3.37% respectively, when compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation.

Spacecraft attitude testbed

Spacecraft attitude determination and control systems (ADCS) stand as a pivotal factor for ensuring the success of missions. As satellite sizes continue to decrease, the need for more intelligent and efficient control algorithms becomes increasingly pronounced. In response to this imperative, we propose the development of a scaled-down testbed specifically designed for evaluating satellites (ACDS). This testbed will demonstrate effective control of a CubeSat through the use of Helmholtz coils, magnetic torquer solenoids, reaction wheels and inexpensive student-made components and available components off the shelf (COTS) in the San Diego State University SPACE Lab. Referred to as the Spacecraft Attitude Testbed (SAT), this custom-designed 3-DoF experimental attitude platform replicates the conditions experienced by a weightless satellite in space. The objective of this research paper is to improve the understanding of how to manufacture a testbed and components like those within this paper, expose some of the issues encountered and improve the outcomes for future academic testbeds. Within this study, the testbed employs torque application to magnetic torquers and reaction wheels to regulate attitude. In summary, this research endeavors to establish a comprehensive and cost-effective testbed platform dedicated to the assessment of spacecraft attitude control and determination systems. It not only seeks to enhance our understanding of advanced control algorithms but also aims to contribute valuable insights into fault detection and adaptive control strategies, ultimately bolstering the efficiency and robustness of satellite operations.

Experimental investigation and intelligent control of the magnus anti-rolling device for ship stability at zero speed

Under zero-speed conditions, ships are particularly susceptible to the effects of waves, which directly impact the safety of the vessel. A ship anti-rolling device based on the Magnus effect is designed to mitigate rolling motions across a full range of speeds, thereby enhancing the vessel's stability. This study presents an experimental investigation and intelligent control of Magnus anti-rolling devices aimed at enhancing ship stability at zero speed. The test setup, intelligent control algorithm, and experimental procedures specifically tailored for evaluating the Magnus anti-rolling device were designed. Following this, a comprehensive analysis was conducted to assess the effects of different cylinder geometries, swinging speeds, initial roll angles, and control methods on the anti-rolling characteristics of the device. Results demonstrate that the intelligent control method achieves an average anti-rolling efficiency of 89%. Additionally, the optimised geometric model of the Magnus anti-rolling device exhibits improved anti-rolling efficiency relative to the original model. The study confirms the stability and robustness of the intelligent Magnus anti-rolling device and suggests future research directions for practical applications aboard full-scale vessels in complex marine environments.

Intelligent control algorithm for dynamic positioning control system

Introduction: The dynamic positioning system resists the environmental forces such as wind, wave and current acting on the ship through the thruster, so that the ship can remain in the position required by the sea level as much as possible, and the operation is very convenient. But its current dynamic positioning ability can not meet people's needs.Methods: A Kalman filter based on untracked optimization was designed for dynamic positioning control system. Then the intelligent control algorithm is designed for the dynamic positioning top-level controller and thrust optimal distribution controller, which occupy an important position in the system, namely the adaptive weight variation particle swarm optimization algorithm and thrust optimal distribution algorithm.Results and Discussion: The average position error of three degrees of freedom after filter processing is 1.53 m. Compared with other mainstream controllers, the mean root error of controllers based on adaptive weight variation particle swarm optimization in environment A and B is 2.295 and 1.8 m, respectively. In environment C, the controller based on thrust optimization allocation algorithm can get the optimal solution when the full rotary thruster reaches the 7 s and the channel thruster reaches the 4 s. The thrust exclusion zone is crossed at 46 s in environment D. In the dynamic positioning capability curve of the system, the experimental hull can balance the different environmental loads at all angles. In summary, the intelligent control algorithm proposed in this paper can effectively improve the positioning ability of the dynamic positioning control system and meet the needs of people for ship navigation today.

Enhancing Hydraulic System Performance through Intelligent Control and Energy Efficiency

In addressing the operational constraints of conventional hydraulic systems, significant advancements have been achieved through the development of an energy-efficient hydraulic system for press applications. This research identifies key inefficiencies, such as pressure losses and suboptimal flow rates, while proposing a novel architecture that utilizes advanced components, including energy-saving valves and variable displacement pumps. By automating critical processes, the integration of intelligent control algorithms - enabled by a programmable logic controller (PLC) - enhances fluid distribution and timing accuracy. The prototype notably employs gravity to propel fluid, significantly reducing energy consumption by minimizing the need for continuous pumping. Testing results confirm substantial energy savings, demonstrating the system’s potential to improve operational reliability and promote more environmentally sustainable industrial practices. Overall, this project exemplifies a holistic approach to hydraulic system design, addressing both performance enhancement and energy efficiency in a rapidly evolving industrial landscape.

Modular Intelligent Control System in the Pre-Assembly Stage

This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability.