Control Algorithm Research Articles

Redundancy resolution of a variable base frame of a 3-DoF Cable-driven serial chain by using an adaptive neuro-fuzzy controller

This paper presents a novel approach using adaptive neuro-fuzzy techniques to design controllers for planar cable-driven serial chain robots with variable configurations. The approach consists of two key components: (1) deriving dynamic models for cable-driven serial chain robots which are independent of their structure, and (2) adaptively determining the optimal cable connection points. Traditional methods face challenges in obtaining accurate dynamic equations for cable-driven serial chain robots with high degrees-of-freedom, hence neural networks are employed to estimate the model. In order to handle the variability in cable connection points, adaptive fuzzy methods are utilized. The proposed adaptive neuro-fuzzy controller algorithm introduces two new indices, namely cost-of-redundancy and degree-of-redundancy, to effectively address redundancy concerns. Additionally, the algorithm efficiently reduces the search space for finding the optimal configuration. Simulation results for a planar 3 degrees-of-freedom cable-driven serial chain robot using this algorithm showcase a noteworthy 42% reduction in cost-of-redundancy and an impressive 53.125% reduction in search space.

Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Abstract To solve the problem of excessive fluctuation of the V2G load of the electric vehicle charging pile, this paper proposes an automatic control algorithm for the V2G frequency response mode of the electric vehicle charging pile. The FPGA is used as the core processing unit to collect and process the V2G frequency data of the charging pile; the multiple linear regression model is used to predict the charging and discharging loads, and the automated two-layer control model is established; the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network to realize the control of the V2G frequency response mode. The experimental results show that the load prediction error of this method is small. After the automatic control, the load fluctuation of the charging pile is small, and the electric vehicle charging pile’s V2G frequency response control effect is better.

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

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

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

Analyzing the performance of metaheuristic algorithms in speed control of brushless DC motor: Implementation and statistical comparison.

A brushless DC (BLDC) motor is likewise called an electrically commutated motor; because of its long help life, high productivity, smaller size, and higher power output, it has numerous modern applications. These motors require precise rotor orientation for longevity, as they utilize a magnet at the shaft end, detected by sensors to maintain speed control for stability. In modern apparatuses, the corresponding, primary, and subsidiary (proportional-integral) regulator is broadly utilized in controlling the speed of modern machines; however, an ideal and effective controlling strategy is constantly invited. BLDC motor is a complex system having nonlinearity in its dynamic responses which makes primary controllers in efficient. Therefore, this paper implements metaheuristic optimization techniques such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Accelerated Particle Swarm Optimization (APSO), Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA); moreover, a chaotic map and weight factor are also being applied to modify LFWOA (i.e., CMLFWOA) for optimizing the PI controller to control the speed of BLDC motor. Model of the brushless DC motor using a sensorless control strategy incorporated metaheuristic algorithms is simulated on MATLAB (Matrix Laboratory)/Simulink. The Integral Square Error (ISE) criteria is used to determine the efficiency of the algorithms-based controller. In the latter part of this article after implementing these mentioned techniques a comparative analysis of their results is presented through statistical tests using SPSS (Statistical Package for Social Sciences) software. The results of statistical and analytical tests show the significant supremacy of WOA on others.

A fuzzy control based strategy for adjusting band blade feed rate in constant power sawing

Abstract The variable cross-sectional shape of the blank reduces the efficiency of the sawing machine. Additionally, the wear of the band saw blade causes a mismatch between the feed rate and the load, further exacerbating the wear. This study studied a feed rate control strategy considering sawtooth wear to balance the relationship between cutting efficiency and band life. Initially, speed parameters for the cut-in and cut-out areas, along with acceleration and deceleration control algorithms, were established to ensure the smooth transition of the saw blade during the machining process. Additionally, a constant power sawing feed rate model that considers the influence of wear was developed, enabling dynamic adjustments to the feed rate based on the saw blade's wear state. This study also proposes a fuzzy control-based target current correction method, which dynamically adjusts the target current according to the current wear state of the saw blade, ensuring that sawing requirements were met under varying levels of wear. The accuracy of the sawing model and control strategy was verified through experiments. The results demonstrated that this strategy improved efficiency by 44% compared to the traditional uniform feed rate method. This study introduces a novel feed rate control strategy for sawing and offers an effective solution to the problem of saw tooth wear.

The influence of neighbor selection on self-organized UAV swarm based on finite perception vision.

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the Acute Angle Test (AAT) is proposed,
which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighborselection mechanism can reduce the redundant communication burden between large scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance.

Smart Zone-Based Vehicle Speed Retarding System Utilizing Radio Frequency (RF) in Electronic Vehicles

The proposed system aims in designing an efficient automatic wireless vehicle speed control in low-speed zones using RF communication. The advent of new high-speed technology and the growing computer capacity provided realistic opportunity for new robot controls and realization of new methods of control theory. This technical improvement together with the need for high performance robots created faster, more accurate and more intelligent robots using new robots control devices, new drivers and advanced control algorithms. The proposed method uses RF transmitter and receiver modules for establishing wireless communication, DC motors for the vehicle movement controller. The status of the project will display on LCD display. The controlling device of the whole system is a Microcontroller to which RF receiver module, DC motors are interfaced through a motor driver. Whenever the vehicle enters into a low-speed zone, the data from RF transmitter of low-speed zone will be received by the vehicle system. The microcontroller in the vehicle system automatically slows down the speed to the speed limit of that zone. The microcontroller checks the data with the program embedded in it and performs appropriate actions on the electric motors. The microcontroller is programmed using Embedded C language.

New insights into factors affecting the severity of autonomous vehicle crashes from two sources of AV incident records

Superior safety is the main banner value of promoting autonomous vehicle (AV) technology, but it is difficult to responsibly claim it. The potential for AVs to reduce crash and injury risks would be overshadowed by technological limitations, regardless of their ability to mitigate or eliminate human error. This study aims to identify the key factors affecting crash severity by analyzing real-world AV crash data from the U.S. between 2015 and 2022. We integrated two open data sources from the California DMV and NHTSA. Mixed multinomial logit models incorporating the interaction effects were estimated using the crash severity level, addressing the observed and unobserved heterogeneities. Our results show that Advanced Driver Assistance System (ADAS) engagement is associated with a higher likelihood of slight injury crashes, whereas Automated Driving System (ADS) engagements show the opposite effect. In addition, we found that various environmental and road factors, including lighting conditions, weather, road type, and road surface conditions, significantly affect the severity of AV crashes. For instance, daylight conditions contribute to a lower likelihood of slight-injury crashes. On the other hand, driving under unfavorable weather conditions (cloudy, foggy, rainy, or snowy), on the highway, and on non-dry road surfaces are associated with an increase in the likelihood of severe injury crashes. Furthermore, several significant interaction effects were revealed. First, the mitigating effect of ADS engagement on the likelihood of slight injury crashes is reduced by the rear-end collision type. Second, the likelihood of slight injury crashes increases when AV interacts with heavy trucks on highways. Third, the likelihood of severe injuries increases when AVs collide with Vulnerable Road Users (VRUs) on urban streets. Overall, this research is expected to provide policymakers and AV manufacturers with valuable insights into AV safety, stressing that addressing the identified factors will lead to improved AV design and control algorithms.

Organizing the automated system of dispatch control over pump units at water pumping stations

The design and operation of modern dispatch control systems for pumping units involves comprehensive solution to separate engineering, technical, and scientific problems. The object of research is information processes enabling the operational modes of electrically driven pumping units at water pumping stations. This paper solves the scientific and technical task related to developing topology, hardware, and software tools, control algorithms, dispatch interface, and researching the modes of operation of pumping units in dispatch control systems at water pumping stations. Algorithms for controlling pumping units have been implemented, the advantage of which is the possibility of automated calculation of parameters for technological PID-controllers, taking into account the current electrical parameters of the frequency-controlled electric drive of pumping units and operating conditions. The WEB-oriented dispatch interface of the SCADA-based pumping unit control system was designed and tested, which enables control process in real time. Features of the developed dispatch control system are improved topology, expanded functionality, energy-saving control modes, and the possibility of further modernization based on the principles of standardization and unification of hardware and software tools and design procedures. The developed dispatch control system for pumping units at water pumping stations has been implemented and is successfully operated at an industrial water supply enterprise. The results provided for an increase in technical and economic indicators during the operation of technological equipment due to the efficiency of control and management procedures, energy-saving operational modes of the frequency-controlled electric drive of pumping units.

Fractional‐order fast terminal sliding mode trajectory tracking control of quadrotor UAV based on finite time disturbance observer

AbstractIn this paper, a novel trajectory tracking control problem of the quadrotor UAV is addressed, which considers the system uncertainties and unknown external disturbances meanwhile. To ensure a rapid convergence rate of the states, fast terminal sliding mode control (FTSMC) algorithm is improved combining with a fractional order method. In addition, finite‐time observer is designed to handle disturbances and enhance the robustness of the system. The closed‐loop finite‐time stability of the quadrotor UAV system is established using Lyapunov theory in accordance with the novel control law. To evaluate the effectiveness of our proposed strategy, various simulations under different scenarios are conducted respectively. These simulation results clearly demonstrate the superiority of our control scheme, which we refer to as fractional‐order fast terminal sliding mode control (FOFTSMC).

Wi-Fi sensing gesture control algorithm based on semi-supervised generative adversarial network

A Wi-Fi-sensing gesture control system for smart homes has been developed based on a theoretical investigation of the Fresnel region sensing model, addressing the need for non-contact gesture control in household environments. The system collects channel state information (CSI) related to gestures from Wi-Fi signals transmitted and received by network cards within a specific area. The collected data undergoes preprocessing to eliminate environmental interference, allowing for the extraction of complete gesture sets. Dynamic feature extraction is then performed, followed by the identification of unknown gestures using pattern recognition techniques. An improved dynamic double threshold gesture interception algorithm is introduced, achieving a gesture interception accuracy of 98.20%. Furthermore, dynamic feature extraction is enhanced using the Gramian Angular Summation Field (GASF) transform, which converts CSI data into GASF graphs for more effective gesture recognition. An enhanced generative adversarial network (GAN) algorithm with an embedded classifier is employed to classify unknown gestures, enabling the simultaneous recognition of multiple gestures. A semi-supervised learning algorithm designed to perform well even with limited labeled data demonstrates high performance in cross-scene gesture recognition. Compared to traditional fully-supervised algorithms like linear discriminant analysis (LDA), Light Gradient Boosting Machine (LightGBM), and support vector machine (SVM), the semi-supervised GAN algorithm achieves an average accuracy of 95.67%, significantly outperforming LDA (58.20%), LightGBM (78.20%), and SVM (75.67%). In conclusion, this novel algorithm maintains an accuracy of over 94% across various scenarios, offering both faster training times and superior accuracy, even with minimal labeled data.

Demand Response Potential of an Educational Building Heated by a Hybrid Ground Source Heat Pump System

Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time.

Pulse current stabilizer with digital control for the power supply system of the plasmatron

Purpose. Solution of theoretical and practical problems on providing digital control of a pulse converter using high-speed microprocessor tools in the output current stabilization mode with provision of a specified duration of transient processes caused by an increase in load voltage and output current astaticity, which allows to obtain significant advantages over analog versions. Methodology. Review of literary and patent sources on the subject, theory of pulse automatic control systems, mathematical modeling of processes in pulse current stabilizers in the MATLAB / Simulink software environment and physical prototyping. Findings. A simulation model of an autonomous power supply system based on a converter using soft switching technology of transistors and an arc load is presented. A control law is synthesized and a model of a pulse current stabilizer is developed. A method is proposed and ways are found to control a pulse current stabilizer that provide a given duration of transients and astatism of the output current. A model of a pulse stabilizer with digital control based on a single-crystal computing module is developed and manufactured. The results of the study confirm the achievement of a finite duration of transient processes caused by a step change in the load voltage, close to 3-4 periods of conversion and output current astaticism. It is shown that the use of a pulse stabilizer using a fully digital control circuit has undeniable advantages over analog systems. Originality. The problem of synthesizing a digital controller for a given control time by the method of desired transfer functions for a soft switching operating converter on an arc load is solved. In addition to the given control time, additional quality requirements in the steady state are provided. Practical value.The use of microprocessor technology makes it possible not only to implement complex and new highly efficient control algorithms for a converter operating in the pulse current stabilizer mode, but also to perform additional functions for overload protection, self-diagnostics and telemetry of pulse converters. The use of this same digital device simultaneously for the purpose of controlling a pulse converter will allow to abandon analog PWM controllers and thereby reduce its own energy consumption and weight and size characteristics, increase the reliability of the functioning of pulse converters in power supply systems as a whole.

A Study on Ways to Expand the Speed Operation Range of Dental Laboratory Handpiece Motors Using Low-Cost Hall Sensors

In the dental prosthetics field, BLDC micro-motors are primarily used for implant procedures. As dental materials become increasingly harder, there is a growing demand for higher performance motors to precisely process these materials. However, difficulties in motor development arise due to price competitiveness. Dental motors require capabilities such as low-speed, high-torque for drilling operations and high-speed rotation for precise machining of high-strength dental materials. Additionally, there is a requirement to address thermal issues to prevent user burns due to motor-generated heat. Typically, motors requiring precision control utilize high-resolution position sensors like encoders or resolvers to acquire and control the rotor’s position. However, the high cost of these sensors and constraints such as increased device size pose challenges in maintaining price competitiveness. In this paper, we propose a control algorithm that satisfies the requirements for low-speed, high-torque and high-speed operation requirements for precision machining without hardware modifications using a BLDC micro-motor equipped with an inexpensive Hall sensor commonly used in the dental field. Furthermore, the algorithm is designed to ensure stable operation even in the event of Hall sensor failure or malfunction, and its effectiveness is validated through experiments.

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Addressing the Cost Optimization Issue for IOTA Based on Lyapunov Optimization Theory

IOTA is an emerging decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications. It has the advantages of zero transaction fees, incremental scalability, and high-performance transaction rates. Despite its well-understood benefits, IOTA nodes need to withstand considerable resource costs to generate the distributed ledger. The main reason for this is that IOTA abandons the original blockchain reward mechanism and does not charge transaction fees. Therefore, in this paper we address the cost optimization issue for IOTA based on Lyapunov optimization theory. We take the first step in investigating the cost optimization problem of IOTA and exploring a new optimization scheme using Lyapunov optimization theory. Our proposed scheme enables IOTA to minimize the total cost of IOTA nodes through a computational optimization algorithm. Then, an optimized transaction rate control algorithm can be designed based on the large deviation theory to reduce orphan tangles that waste computational costs. In addition, we define and deduce the effective width of the tangle to monitor the total throughput and reduce the time spent on cost optimization to avoid unnecessary waste of resources. Lastly, a comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is both efficient and practical.

Joint Optimization Control Algorithm for Passive Multi-Sensors on Drones for Multi-Target Tracking

Joint Optimization Control Algorithm for Passive Multi-Sensors on Drones for Multi-Target Tracking

Development of a PID-Controlled Refrigeration System for Reduced Power Consumption

Refrigerator systems are used for food preservation as well as other applications. However, the cost of running the system is high due to rising fuel and electricity prices. Traditionally, these systems are controlled by On/Off controllers. This study proposes the use of a proportional-integral-derivative (PID) controller algorithm to reduce costs for domestic and industrial refrigeration without negatively affecting the system performance. To accomplish this, a physical model was developed, comprising a domestic refrigerator, microcontroller, and MATLAB computer software for analysis. A mathematical model of second-order lead and second-order lag transfer function was also developed for a typical refrigerator system. The physical model was connected, and open-loop temperature-time response data was collected for system modeling. In addition, Data were collected from industries namely Fan Milk Industry and Benue State University Teaching Hospital Mortuary for a robust system analysis. All data sets were imported into MATLAB's system identification toolbox to estimate model parameters. The ultimate gains, frequency, and period were determined for each feedback closed-loop model, allowing the application of Ziegler-Nichols and Tyreus-Luyben PID tuning settings. The closed-loop models were then simulated in MATLAB to evaluate system performance. Simulation results showed that the Tyreus-Luyben model performed better, and offered better temperature response, less undershoot, and faster settling time than the Zeigler-Nichols method. Both PID models outperformed the traditional On/Off controller, with energy consumption reduced to less than one-third of the conventional method. The study concludes that PID controllers are a better alternative to On/Off systems when properly tuned.

Distribution Grid Hosting Capacity Improvement using Reactive Power Control Optimization Algorithm of Inverter-based Photovoltaic Generation System

This paper uses a reactive power control optimization algorithm to present an inverter-based photovoltaic generation system for increasing the distribution grid host capacity. The main objective of this study is to regulate bus voltages by providing sufficient minimal reactive power consumption, which is obtained by a Particle Swarm Optimization-based optimization algorithm. A modeling study of the system network is developed using DIgSILENT PowerFactory, and the control algorithm is implemented in MATLAB. The proposed approach is efficient for various scenarios of the IEEE 13-bus test system and practical distribution network. The analysis results are compared with outcomes from other control methods, including technical and economic assessments. The performance of the proposed optimization-based power system has shown that the proposed algorithm method yielded significantly superior mitigated voltage rises and increased hosting capacity compared to those achieved by existing control methods of a specific distribution network.