Development Of Control Algorithms Research Articles

Development and performance evaluation of intelligent algorithm for optimal control of a hybrid heat pump system during the cooling season

The purpose of this study is to develop a control method for a hybrid heat pump system based on an artificial neural network (ANN) to reduce energy use and create a more comfortable thermal environment. The proposed optimal control method uses an ANN-based predictive model to predict the heat storage tank and indoor temperature during the cooling period and controls the flow rate of the circulation pump on the heat source and load side of the system. The performance of the predictive model for the heat storage tank temperature (R2 = 0.9988; coefficient of variation of the root mean square error [CV(RMSE)] = 1.06 %; normalized mean bias error [NMBE] = 0.16 %; and mean absolute error [MAE] = 0.09℃) and the indoor temperature (R2 = 0.9893; CV(RMSE) = 1.66 %; NMBE = 0.16 %; and MAE = 0.15℃) was excellent. The temperature control of the heat storage tank using the optimal algorithm exhibited an improvement of 18.39 % for the CV(RMSE), 3.10 % for the NMBE, and 1.31 °C for MAE compared with rule-based. For the indoor temperature, the optimal algorithm improved the CV(RMSE) by 1.30 %, NMBE by 0.42 %, and MAE by 0.29℃ compared to rule-based. The energy use was reduced by 52.85 % for the entire system using the optimal control method compared with the existing control strategy under similar outdoor conditions. Using the proposed control method, it is thus possible to improve thermal comfort and reduce carbon emissions in the building sector by improving the control and energy performance of hybrid heat pump systems.

Fiberbots: Robotic fibers for high-precision minimally invasive surgery.

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model. High-precision motion of the fiber robot delivered endoscopically facilitates the effective use of cellular-level intraoperative tissue identification and ablation technologies, potentially enabling precise removal of cancer in challenging surgical sites.

Magnetorheological Fluid Dampers: A Close Look at Efficient Parametric Models

The reasonable efficiency, high reliability, and minimal maintenance requirements of the semiactive control algorithms make this form of structural control the most successful method from a practical point of view. In addition, significant progress has been made in structural control devices. Magnetorheological (MR) dampers have been the subject of extensive research. In order to develop and implement a successful structural control algorithm, a deep understanding of their operation is required. Therefore, many efforts have been made to classify MR dampers as efficient tools; however, their complicated behavior has also been noted. Various relations have been proposed to describe the highly nonlinear behavior of MR dampers. To develop an efficient control algorithm, one must first properly understand the seismic behavior of the structure and the control device as well as its handling. The handling of the device will be efficient if there is a description for its control, a so-called inverse model. This model is an answer to the question of whether the proposed device model can be written in a form that practically brings the generated damping force as close as possible to the desired force. In the identification process, the selection of a basic mathematical model is critical. In this review article, we have compiled successful parametric models that accurately replicate the behavior of MR dampers. Since the primary functionality of the device is crucial for the development of a competent control algorithm, we have endeavored to investigate and present the reversibility of the proposed models. We also delved at the characteristics of the parameters that enable precise communication between the central control unit and the device. These parameters, which enable the dialog between the processing unit and the control devices, will play an important role in the development of the inverse model in its simplicity and efficiency.

Lateral Evasive Maneuver with Shared Control Algorithm: A Simulator Study.

Shared control algorithms have emerged as a promising approach for enabling real-time driver automated system cooperation in automated vehicles. These algorithms allow human drivers to actively participate in the driving process while receiving continuous assistance from the automated system in specific scenarios. However, despite the theoretical benefits being analyzed in various works, further demonstrations of the effectiveness and user acceptance of these approaches in real-world scenarios are required due to the involvement of the human driver in the control loop. Given this perspective, this paper presents and analyzes the results of a simulator-based study conducted to evaluate a shared control algorithm for a critical lateral maneuver. The maneuver involves the automated system helping to avoid an oncoming motorcycle that enters the vehicle's lane. The study's goal is to assess the algorithm's performance, safety, and user acceptance within this specific scenario. For this purpose, objective measures, such as collision avoidance and lane departure prevention, as well as subjective measures related to the driver's sense of safety and comfort are studied. In addition, three levels of assistance (gentle, intermediate, and aggressive) are tested in two driver state conditions (focused and distracted). The findings have important implications for the development and execution of shared control algorithms, paving the way for their incorporation into actual vehicles.

Selection of power supply scheme for controlled excitation converters in traction electric motors of single-phase DC electric locomotives

The article focuses on the development of an effective design and algorithms for automatic control of singlephase DC electric locomotives according to the laws of constant traction force and power without switching electrical devices within power electric circuits. Methods of mathematical modelling for electromagnetic, electromechanical, and mechanical processes by MatLab, Simulink, and SimPowerSystems software were used to address this problem. The nonlinearities of the magnetisation curve were taken into account, along with the influence of eddy currents from the coils of the main and additional poles in traction motors. Structural and parametric synthesis of a power electrical circuit and control algorithms by controlled bridge IGBT converters were used in the simulation. The object of the research was an electrotechnical complex, including a 9840 kW three-stack electric locomotive 3ES5K “Ermak”. On the basis of the research results, it is recommended to use a power electrical circuit with two reversible converters for each of the three sections on the electric locomotive. These converters provide power to four 820 kW traction motors and a group power supply for controlled bridge IGBT converters shunting the field coils for axial traction control. The calculations confirmed the applicability of a scheme for individual control of traction electric motors and excitation currents, ensuring a smooth increase in the traction force of an electric locomotive. The developed algorithm of axial traction control ensures a smooth increase in this force and creates the optimal conditions for coupling the wheels of an electric locomotive with rails. These solutions can be used in the manufacture and modernisation of new and existing electric locomotives

Intelligent Control of Electric Vehicle Drives using Swarm Robotics

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

Development of high-precision control algorithms for autonomous electric vehicles

Development of high-precision control algorithms for autonomous electric vehicles

Adaptive fault-tolerant tracking control for MIMO nonlinear systems with time-varying full state constraints

In this article, an adaptive fault-tolerant tracking control algorithm is developed for the multi-input-multi-output (MIMO) pure-feedback nonlinear systems with full state constraints. The state constraints are time-varying, hence greatly improve the applicability of the result but significantly increase the difficulties and complexities of control system design. To overcome the feasibility conditions in barrier Lyapunov function for handling time-varying constraints, a novel nonlinear mapping method for MIMO nonlinear systems is developed. Adaptive techniques and Neural Networks (NNs) are applied to estimate unknown uncertainties and disturbances, which the tracking control accuracy is improved. It is proved that the designed control algorithm can not only achieve the control objectives but also guarantee the boundedness of all signals. Finally, the effectiveness of the developed control algorithm is demonstrated via a simulation example.

Toward the autonomous flushing of building plumbing: Characterizing oxidation-reduction potential and temperature sensor dynamics

Manual flushing of building plumbing is commonly used to address water quality issues that arise from water stagnation. Autonomous flushing informed by sensors has the potential to aid in the management of building plumbing, but a number of knowledge gaps hinder its application. This study evaluates autonomous flushing of building plumbing with online sensor and actuator nodes deployed under kitchen sinks in five residential houses. Online oxidation–reduction potential (ORP) and temperature data were collected for nine weeks during the winter and summer in houses with both free chlorine and chloramine. ORP levels in houses with free chlorine residuals decreased after overnight stagnation. The overnight decrease in ORP was not observed when tap water was automatically flushed for five minutes at 6:00 h every morning. ORP levels in houses with chloramine residuals did not decrease consistently after overnight stagnation, and daily automated flushes did not have an observable effect on the ORP signals. Additional laboratory experiments were carried out to evaluate ORP signals during chlorine decay and after incremental changes in chlorine, as would be expected in building plumbing conditions. Results from the lab and field deployments suggest on-line ORP sensors may be used to detect free chlorine decay due to stagnating water, but are not as effective in detecting chloramine decay. However, field results also suggest ORP may not respond as expected on a timely manner after free chlorine or chloramine have been restored, hindering their applicability in developing control algorithms. In this paper we tested twice-daily five-minute automatic flushing and found that it counteracts water quality degradation associated with overnight stagnation in free chlorine systems. An automatic sensor-based flushing is proposed using online temperature sensor data to determine when flushing has reached water from the main. The results suggest that flushing informed by temperature sensors can reduce the flushing time by 46 % compared to the preset five-minute static flush.

Analysis of Autonomous Vehicle Control Algorithm Based on Different Models

As a matter of fact, the rise of autonomous vehicles (AVs) has necessitated the development of advanced control algorithms to ensure safe and efficient operation in recent years. With this in mind, the aim of this paper is to provide a comprehensive analysis of control algorithms for self-driving vehicles and to identify research opportunities for innovation by comparing the strengths and weaknesses of existing algorithms. On this basis, through a careful dissection of existing literature as well as case studies, this study provides an in-depth understanding of different control algorithms and a comparative analysis of performance metrics from real-world tests and simulations. To be specific, three models will be applied. According to the analysis, the results of this study emphasize the critical role of accurate vehicle dynamics in the development of complex automated driving control algorithms at the same time, paving the way for safer, more efficient as well as sustainable transportation solutions.

Robotic-assisted burring in total hip replacement: A new surgical technique to optimise acetabular preparation.

In Total Hip replacement (THR) surgery, a critical step is to cut an accurate hemisphere into the acetabulum so that the component can be fitted accurately and obtain early stability. This study aims to determine whether burring rather than reaming the acetabulum can achieve greater accuracy in the creation of this hemisphere. A preliminary robotic system was developed to demonstrate the feasibility of burring the acetabulum using the Universal Robot (UR10). The study will describe mechanical design, robot trajectory optimisation, control algorithm development, and results from phantom experiments compared with both robotic reaming and conventional reaming. The system was also tested in a cadaver experiment. The proposed robotic burring system can produce a surface in 2min with an average error of 0.1 and 0.18mm, when cutting polyurethane bone block #15 and #30, respectively. The performance was better than robotic reaming and conventional hand reaming. The proposed robotic burring system outperformed robotic and conventional reaming methods to produce an accurate acetabular cavity. The findings show the potential usage of a robotic-assisted burring in THR for acetabular preparation.

Adversarial waypoint injection attacks on Maritime Autonomous Surface Ships (MASS) collision avoidance systems

ABSTRACT Autonomous navigation is currently subject to particular interest for naval and commercial vessels. In addition to the regulatory effort, several pieces of research tackled the development of new guidance laws, stable and robust control algorithms, methodologies to increase situational awareness, and collision avoidance algorithms. However, most of these systems blindly trust information from navigation sensors, which can malfunction or, even worse, have their data altered by cyber-attacks. This last scenario will be increasingly common shortly and represents a dangerous threat that future highly computerised ships must face. The proposed work shows an approach capable of hijacking a state-of-the-art collision avoidance algorithm route by feeding it with rogue data. The effects of such a cyber-attack are shown via an extensive simulation campaign, testing the methodology on three different COLREG scenarios: head-on, crossing, and overtaking. The study additionally highlights the susceptibility of forthcoming navigation systems to security breaches, which still needs to be addressed in the existing literature. According to the experimental findings, attackers can attract the autonomous ship guidance toward their objective at a mean distance of 133 m . Finally, suggestions on how to harden these systems against this family of attacks are provided in the conclusion.

Analysis of Battery Testing Protocols in the Transition from the Lab to the Field: A Test Case Using Advanced Zn-MnO2 Batteries in Off-Grid Solar Microgrids

Understanding cycling performance of batteries under varying conditions is crucial for continued development of emerging chemistries. Currently much of this work is focused on cycling single cells in well-controlled lab experiments. There are few module cycling studies in the open literature. Studies of full-sized fielded systems are even rarer. As a result of this data gap, there is limited understanding of how single cell cycling can be used to predict performance of fielded systems prior to their deployment.Sandia National Laboratories (SNL) recently deployed an advanced Zn-MnO2 secondary battery energy storage system in an off-grid solar plus storage system. Using this deployment as a test case, we will detail how performance and degradation for batteries can vary between the lab and the field. We will also discuss ways that single cell and module lab cycling can be improved to better predict operation in fielded systems.In preparation for this deployment, SNL conducted single cell cycling experiments in the lab to simulate operation in the field and determine the expected life of the systems. The first test program utilized elements of the IEC 61427-1 standard for photovoltaic off-grid application to simulate the temperature and predicted state of charge (SOC) range the cells will be cycled during field operation. The simulated field cycling targeted SOC ranges of 10 to 40% and 80-100% over 150 cycles with temperature varied between -4 and 35oC to match the ambient conditions in the field during each season. The second type of cycling test was a temperature dependence study to determine how temperature impacted cell degradation. Cells were cycled at C/10 at their full SOC range and at a single temperature until they reached 40% capacity retention. Both cycling studies predicted that low temperature operation would increase the rate of degradation.The system was deployed in May of 2022, and since that time the SNL team has collected a significant amount of performance data to help analyze system level behaviors as compared to observations from cell level testing. These include battery interactions with the solar charge controller, limitations on power inverter settings, and variance between the average system level state of charge and the test protocol. Additionally, the team is analyzing conditions found in the field, with regards to ambient temperature, solar insolation and battery charging rates, and customer usage patterns for comparison with cell-level testing protocol.Observations of the deployed system’s performance indicate there is a need for more nuanced testing protocols for lab testing that can better approximate field conditions. These may include dynamic temperature ranges that vary throughout a charge/discharge cycle, and solar charging assumptions that account for reduced insolation due to cloud cover. This can help further inform the development of charge control algorithms, system hardware and software that can help improve the overall performance of solar microgrid energy storage systems.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2023-02502A

Maximum Power Extraction in Photovoltaic Systems Using High-Performance Adaptive Control Approach

The nonlinearities present in photovoltaic (PV) generator models can significantly impact the performance of PV systems, leading to decreased system efficiency and reduced profitability. This paper is aimed at addressing these challenges by developing a novel control algorithm based on a high-performance adaptive control method for photovoltaic systems. The proposed algorithm is designed to effectively track set points and optimize power extraction even in the presence of disturbances. The key contribution of this work lies in the application of this control strategy specifically to PV systems to achieve optimal performance. When compared to traditional control methods, the proposed approach demonstrates significant improvements, notably in terms of power extraction efficiency and system loss reduction. Moreover, the control algorithm effectively ensures accurate tracking of set points. These outcomes underscore the notable performance enhancements facilitated by the proposed algorithm. In conclusion, the developed control algorithm offers superior performance in optimizing power extraction and maintaining precise set point tracking for PV systems, leading to improved system efficiency and increased profitability.

Physically-based control-oriented modeling for turbocharged stoichiometric spark-ignited engine with cooled EGR and flexible VVT systems

Accurate estimation and prediction of engine gas exchange system and in-cylinder conditions are critical for spark-ignited engine control and diagnostic algorithm development. In this paper, a physically-based, control-oriented model for a 2.8 l turbocharged, variable valve timing (VVT) and low pressure (LP) exhaust gas recirculation (EGR)-utilizing SI engine was developed. The model includes the impact of modulation to any combination of 10 actuators, including the throttle valve, compressor bypass valve, fueling rate, waste-gate, LP EGR valve, number of deactivated cylinders, intake valve open (IVO) timing, intake valve close (IVC) timing, exhaust valve open (EVO) timing and exhaust valve close (EVC) timing. The accuracy of the model in capturing engine dynamics was demonstrated by validating it against high-fidelity engine GT-Power simulation results for various drive cycles, particularly emphasizing elevated loads. In comparison to the open literature, novel contributions of the effort described in this paper includes in-cylinder gas composition modeling and turbine-out pressure estimation.

Development of an Optimal Control Algorithm for the Course Movement of an Unmanned Vehicle

The article presents an algorithm for controlling a ground unmanned vehicle. The main task is to obtain a solution to control problems that allows you to transfer an unmanned vehicle from the initial position to a given final position at a certain time. Currently, autonomous vehicles are being actively introduced in all areas. Including in Russia, you can find unmanned trucks on the federal highway. Given the development of microprocessor technology and the significant economic benefits of using unmanned vehicles, this task is relevant. A lot of research in the field of control of unmanned vehicles concerns the issue of their use in urban environments and on rough terrain. In this paper, we present a solution to the problem of optimal control of an unmanned vehicle using the maximum principle. The problem of optimal control is solved in a deterministic setting with an integro-terminal criterion. The solution of the two-point boundary value problem arising from the maximum principle was carried out using Newton’s method. The ranges of initial values of conjugate variables are obtained, which ensure the convergence of calculations. For the chosen mathematical model of the course movement of the car, solutions to the problem were obtained. The results of numerical simulation are presented, showing the possibility of using the proposed algorithm to control an unmanned vehicle under various initial and final conditions. The developed algorithm has been successfully applied in the presence of a penalty zone. The algorithm can be used when applying the concept of " flexible trajectories" in the tasks of controlling moving objects.

Development of data power control algorithms for downlink transmission in cooperative Wireless Body Area Networks

Development of data power control algorithms for downlink transmission in cooperative Wireless Body Area Networks

Development of a Heuristic Control Algorithm for Improving Energy Efficiency in Commercial Refrigerated Systems without Indoor Information

Despite the serious threat of global warming caused by carbon emissions, commercial refrigeration systems that generate cooling are unable to adjust their usage time. Therefore, it is essential to enhance the energy efficiency of refrigeration systems themselves. Recently developed refrigeration systems offer improved energy consumption efficiency as they utilize inverter-type outdoor units that can communicate with indoor units. However, traditional legacy refrigeration systems still suffer from poor energy efficiency because the existing indoor units cannot communicate with inverter-type outdoor units. Hence, this paper introduces a heuristic energy efficiency improvement algorithm for commercial refrigeration systems using legacy indoor units when an inverter-type outdoor unit is installed. In particular, to reduce the computational complexity of the process of selecting the optimal temperature difference and target low-pressure variables that can enhance energy efficiency, the algorithm employs the Monte Carlo method. Finally, the performance of the proposed heuristic energy efficiency improvement algorithm was evaluated in a laboratory environment to confirm its applicability.

High-density EMG, IMU, kinetic, and kinematic open-source data for comprehensive locomotion activities

Novel sensor technology enables new insights in the neuromechanics of human locomotion that were previously not possible. Here, we provide a dataset of high-density surface electromyography (HDsEMG) and high-resolution inertial measurement unit (IMU) signals, along with motion capture and force data for the lower limb of 10 healthy adults during multiple locomotion modes. The participants performed level-ground and slope walking, as well as stairs ascent/descent, side stepping gait, and stand-to-walk and sit-to-stand-to-walk, at multiple walking speeds. These data can be used for the development and validation of locomotion mode recognition and control algorithms for prosthetics, exoskeletons, and bipedal robots, and for motor control investigations.

Design and Evaluation of Control Algorithms for MEMS Devices in MATLAB/Simulink

The field of Micro-Electro-Mechanical Systems (MEMS) Has witnessed significant advancements in recent years, enabling the development of miniaturized devices with diverse applications. Efficient control algorithms play a crucial role in optimizing the performance of MEMS devices, ensuring accurate sensing and actuation capabilities. This paper presents the design and evaluation of control algorithms for MEMS devices using MATLAB/Simulink, a versatile simulation environment. The proposed research focuses on the development of control algorithms tailored specifically for MEMS devices. The design process involves modeling the MEMS system in MATLAB/Simulink and incorporating real-world constraints and dynamics. To evaluate the performance of the control algorithms, extensive simulations are conducted. The outcomes of this research provide valuable insights into the design and evaluation of control algorithms for MEMS devices in MATLAB/Simulink. The results demonstrate the effectiveness and efficiency of the developed algorithms in enhancing the performance of MEMS systems. Furthermore, the research contributes to expanding the knowledge base of MEMS control design, paving the way for advanced applications and innovations in this rapidly evolving field.