Control Algorithm Research Articles

Research on Path-Following Technology of a Single-Outboard-Motor Unmanned Surface Vehicle Based on Deep Reinforcement Learning and Model Predictive Control Algorithm

Path following is one of the key technologies for unmanned surface vehicles (USVs). This paper proposes a path-tracking control method for a single-outboard-motor USV based on a Deep Deterministic Policy Gradient (DDPG) algorithm and model predictive control (MPC) algorithm. Initially, the motion model and outboard motor model of the USV are analyzed. Subsequently, simulation and real ship experiments provide a comprehensive performance comparison between the proposed DDPG-MPC method and the traditional ALOS-PID method. The results indicate that for straight path tracking, the DDPG-MPC algorithm achieves 37% and 21% reductions in the average cross error and heading angle error, respectively, compared to the ALOS-PID algorithm. The real ship experiments further validate the DDPG-MPC algorithm’s advantages in real-world environments. Specifically, under disturbances like wind, waves, and currents, the maximum cross error of the DDPG-MPC algorithm is one-third of the ALOS-PID algorithm. Additionally, the DDPG-MPC algorithm sustains a higher and more stable longitudinal velocity over extended periods, while the ALOS-PID algorithm shows greater instability and variability. Overall, the findings confirm the feasibility and effectiveness of the proposed approach, highlighting its potential for enhancing path-tracking control performance in single-outboard-motor USVs.

Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions

Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and ethics. There is an urgent need for action in cities, particularly to (i) enhance the health and wellbeing of urban residents while ensuring inclusivity in urban development (e.g., through the intelligent design of public spaces, mobility, and transportation) and (ii) improve resilience and sustainability (e.g., through better disaster management, planning of city logistics, and waste management). This paper aims to explore how neuroscientific and neurotechnological solutions can contribute to the development of smart cities, as experts in various fields underline that real-time sensing designs and control algorithms inspired by the brain could help build and plan urban systems that are healthy, safe, inclusive, and resilient. Motivated by the potential interplay between societal challenges and these emerging technologies, we provide an overview of state-of-the-art research through a bibliometric analysis of neurochallenges within the context of smart cities using terms and data extracted from the Scopus database between 2018 and 2022. The results indicate that smart city research remains fragmented and technology-driven, relying heavily on internet of things (IoT) and artificial intelligence (AI)-based technologies. Mostly, it also lacks careful integration and adoption tailored to societal goals and human-centric concerns. In this context, the article explores key research streams and discusses how to create new synergies and complementarities in the challenge-technology intersection. We conclude that realizing the vision of smart cities at the nexus of neuroscience, technology, urban space, and society requires more than just technological progress. Integrating the human dimension alongside various technological tools and systems is crucial. This necessitates better interdisciplinary collaboration and co-production of knowledge toward a hybrid intelligence, where synergies of education and research, technological innovation, and societal innovation are genuinely built. We hope the insights from this analysis will help orient neurotechnological interventions on urban living and ensure they are more responsive to societal and environmental challenges as well as to legal and ethical concerns.

基于改进 ACO-DWA 算法的轮式农业机器人仓库搬运避障路径研究

The existing obstacle avoidance control algorithms for wheeled agricultural warehouse handling robots are prone to the local optimal solution in the process of path optimization and collision can occur easily occur during multi-robot simultaneous operation. Given this, the obstacle avoidance of wheeled agricultural warehouse handling robots was explored in this study, and an obstacle avoidance path planning algorithm for wheeled agricultural handling robots in warehouse based on improved ACO-DWA algorithm was proposed. Then, the moving trajectory of agricultural warehouse handling robots during handling process was studied, and their spatial kinematics equation was given. Next, the real-time pose of agricultural warehouse handling robots was detected, and their motion path was planned considering the real-time position of obstacles and the target locations of handling. In addition, the obstacle avoidance controlling quantity of agricultural warehouse handling robots was calculated according to the deviation between the pose of robots and the planned path. Supported by a controller, the obstacle avoidance control work of agricultural warehouse handling robots was realized. It was concluded through the effect experiment that compared with the traditional method, the improved ACO-DWA algorithm designed in this study significantly reduced the number of collisions between agricultural robots, and through practical application, the proposed algorithm can meet the needs of improving warehouse logistics management efficiency.

Deployment of a Machine Learning Algorithm in a Real-World Cohort for Quality Control Monitoring of Human Epidermal Growth Factor-2-Stained Clinical Specimens in Breast Cancer.

Precise determination of biomarker status is necessary for clinical trial enrollment and endpoint analyses, as well as for optimal treatment determination in real-world practice. However, variabilities may be introduced into this process due to the processing of clinical specimens by different laboratories and assessment by distinct pathologists. Machine learning tools have the potential to minimize inconsistencies, although their use is not presently widespread. To assess the applicability of machine learning to the quality control process for biomarker scoring in oncology, we developed and validated an automated machine learning model to be applied as a quality control tool for monitoring the assessment of human epidermal growth factor-2 (HER2). The model was trained using whole slide images from multiple sources to quantify HER2 expression and measure immunohistochemistry stain intensity, tumor area, and the presence of artifacts or ductal carcinoma in situ across breast cancer phenotypes. The quality control tool was deployed in a real-world cohort of HER2-stained breast cancer sample images collected from routine diagnostic practice to evaluate trends in HER2 testing quality indicators and between pathology laboratories. Automated image analysis for HER2 scoring is consistent and reliable using this algorithm. Deployment of the HER2 quality control tool across 3 clinical laboratories revealed interlaboratory variability in HER2 scoring and inconsistencies in data reporting. These results support the future incorporation of quality control algorithms for real-time monitoring of clinical laboratories contributing to clinical trials in oncology and in the real-world setting of HER2 immunohistochemistry testing in local clinical laboratories and hospitals.

Low-Cost Hardware Analog and Digital Real-Time Circuit Simulators for Developing Power Electronics Control Circuits

The paper describes low-cost hardware-based analog and digital real-time circuit simulators for the development of power electronics control circuits. During the process of designing and developing digital control circuits for power electronics systems, preliminary verification of control algorithms is required. For this purpose, software simulators such as Pspice, Psim, Matlab-Simulink, and many others are commonly used. Afterward, the developed control algorithm is implemented in the digital control system. For further verification of the implemented control algorithms, a hardware-based analog or digital simulator can be utilized. The paper presents the author’s proposed analog simulators. In the digital version of the simulator, TMS320F28388D microcontroller with 200 MHz clock was used. These simulators have demonstrated their usefulness in the development of power electronics systems.

Improved Active Vector Optimization Strategy‐based Model Predictive Torque Control for Matrix Converter‐Fed PMSM With Reduced Torque and Flux Ripples

ABSTRACTThe Matrix Converter‐fed Finite Control Set‐Model Predictive Torque Control (FCS‐MPTC) technique has superior potentiality in the control of electrical drives due to its availability of increased number of voltage vectors with compact topology. On the other side, the FCS‐MPTC technique suffers a major drawback of high computational complexity and processing time. This major drawback has degraded the attention of the FCS‐MPTC technique in high‐power industrial applications because it needs expensive and high‐rating processors for the functioning of the control algorithm. In this regard, this paper proposes an efficient active vector optimization concept by formulating a switching table for the optimal voltage vector selection. This approach reduces the available 18 active vectors to three, thereby decreasing the execution time of the cost function (CF). In addition, to mitigate the high torque and flux ripple problems associated with the existing FCS‐MPTC method, a modified switching table is proposed. The effectiveness of the proposed approach is evaluated by conducting various test loadings using the MATLAB Simulink, and the results are further validated in real time using the OPAL‐RT laboratory setup.

Real-time proof of concept for a hybrid magnetic bearing with improved damping and reduced control complexity

This paper presents a novel Axial Hybrid Magnetic Bearing (AHMB) and its corresponding control approach to overcome the constraints of conventional Active Magnetic Bearings (AMB) and Passive Magnetic Bearings (PMB). The HMB architecture comprises an independent magnetic circuit for the Permanent Magnet (PM) and AMB, resulting in improved damping and reduced control complexity. A Permanent Magnet Actuator Mechanism (PMAM) and its control algorithm were developed to render the HMB system suitable for counteracting diverse loads. Real-time experiments validated the efficacy of the HMB in mitigating external disturbances. Experimental results showed that when the load was added to the AHMB system, the rotor could be regulated to its nominal position within 1.5 s by the AMB. Moreover, the control currents to the AMB coil can be effectively reduced by ≥50%. The proposed hybrid magnetic bearing has potential applications including, but not limited to, cleanroom equipment, high-speed air compressors, high-speed turbomachinery, and flywheel energy storage systems.

Artificial intelligent fuzzy control and LAPO algorithm for enhancement LVRT and power quality of grid connected PV/wind hybrid systems

Low Voltage Ride Through (LVRT) is considered one of the main and serious problems facing the electrical grid. It occurs due to three-phase symmetric faults and asymmetric faults such as a double line to ground fault that applies in this system. This paper applies Static Synchronous Compensators (STATCOM) to improve the LVRT capability and dynamic performance of an electrical grid linked to a Photovoltaic (PV)/Wind hybrid system through grid disturbances. A hybrid power system containing a PV station that produces 1 MW and a wind farm from type Doubly Fed Induction Generator (DFIG) that produces 9 MW is connected to STATCOM with 48 pulses at PCC bus and energized load. It compensates reactive power to improve LVRT that occurred due to fault. The applied STATCOM controller adjusts the voltage of the PCC bus during an occuring fault on the grid by compensating reactive power. STATCOM is controlled by a Proportional–Integral–Derivative (PID) and is compared with STATCOM controlled by Artificial Intelligence Control (AIC)-based on Proportional—Integral Fuzzy Logic Control (PI FLC). The Lightning Attachment Procedure Optimization Algorithm (LAPO) optimization method is used to adjust the parameters of the PI controller to reduce error signals. A simulation model of the suggested hybrid power system has been performed using Matlab/Simulink. The simulation results of STATCOM proved powerful and the effectiveness of STATCOM with PI FLC in reducing voltage dip, compensating active power of wind and PV farm, protecting DC-link voltage of PV and wind from overvoltage and oscillation that happens at three-phase fault and double line to ground fault as compared with PID STATCOM in enhancement LVRT capability, and power quality.

A Whole-Body Coordinated Motion Control Method for Highly Redundant Degrees of Freedom Mobile Humanoid Robots

Humanoid robots are becoming a global research focus. Due to the limitations of bipedal walking technology, mobile humanoid robots equipped with a wheeled chassis and dual arms have emerged as the most suitable configuration for performing complex tasks in factory or home environments. To address the high redundancy issue arising from the wheeled chassis and dual-arm design of mobile humanoid robots, this study proposes a whole-body coordinated motion control algorithm based on arm potential energy optimization. By constructing a gravity potential energy model for the arms and a virtual torsional spring elastic potential energy model with the shoulder-wrist line as the rotation axis, we establish an optimization index function for the arms. A neural network with variable stiffness is introduced to fit the virtual torsional spring, representing the stiffness variation trend of the human arm. Additionally, a posture mapping method is employed to map the human arm potential energy model to the robot, enabling realistic humanoid movements. Combining task-space and joint-space planning algorithms, we designed experiments for single-arm manipulation, independent object retrieval, and dual-arm carrying in a simulation of a 23-degree-of-freedom mobile humanoid robot. The results validate the effectiveness of this approach, demonstrating smooth motion, the ability to maintain a low potential energy state, and conformity to the operational characteristics of the human arm.

Directly Interconnected High-Energy and High-Power Battery Packs

Designing battery packs is a trade-off between power capability and capacity. Often, high power is only desired for short periods; otherwise, high capacities are preferred. To meet these requirements, hybrid packs comprising high-power and high-energy batteries can be used. However, a major drawback of these systems is the need for additional direct current to direct current converters, which increase the complexity, weight, and cost. By directly interconnecting high-power and high-energy battery strings in parallel, the current distribution is determined exclusively by Kirchhoff’s laws, which can lead to the overloading of individual batteries and thus to damage or dangerous failures. To overcome these problems, we developed a layout and control algorithm for directly interconnected packs that keeps them in a safe state by solely controlling the external power, which is governed by two additional requirements. These keep the discharge current of the high-energy element below the maximum charge current of the high-power element and the charge current of the high-energy element below the maximum discharge current of the high-power element while the pack is discharging or charging, respectively. As a proof of concept, a directly interconnected lithium-ion battery pack was successfully designed using the electrochemical simulation software Battery Simulation Studio 2021,which was tested and integrated into a Audi A3 cabriolet.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING MONTE CARLO OPTIMIZATION TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using three types of Monte Carlo simulation techniques. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing the Monte Carlo technique to extract maximum energy profit using the hybrid microgrid. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

INFLUENCE OF HYDROGEN ON COMBUSTION PROCESS AND ON EMISSION PARAMETERS IN THE RCCI ENGINE

Thanks to its carbon-free nature, it is possible to say that hydrogen is a potential fuel for future automotive systems. Its main favourable properties include high burning rate, wide range of flammability and low ignition energy. In combination with hybrid electric technology, the hydrogen combustion engine has the potential for further development with the goal of maximum efficiency in energy conversion. For more efficient hydrogen combustion, a dual fuel injection hybrid system is designed, where one injector is designed for direct hydrogen injection into the engine cylinder and the other injector is supply fuel to the engine's intake pipe. The greatest challenge is development of a control algorithm intended for management of the combustion process in hydrogen engine and optimization of fuel injection divided into individual doses.

Spindle Bearing Preload Control Using NI MyRIO

The article presents an application for controlling the preload of bearings in a model spindle system. The bearing preload will be implemented using piezoelectric elements, which allow for changing the preload of bearings during spindle operation, which results in a change in the stiffness of the entire system. Such a change in stiffness allows for effective reduction of the vibration amplitudes of the spindle tip. The use of the MyRIO 1900 device from National Instruments, which was programmed using LabVIEW 2021 SP1 (32 bit), is described in detail. The effectiveness of the control algorithm was confirmed on the basis of actual measurements of the vibration amplitude of the front spindle tip on which three different substitute masses (model grinding wheels) were placed. The values for the system without and with the operating control system were compared. The results were recorded five times for each state of the device and each substitute mass. The value of the maximum amplitude was taken into account after averaging it from all five tests without control and with control. The effectiveness of the control algorithm was the greater the heavier the substitute mass, which allowed for reducing the vibration amplitude by up to 20%.

Second-order sliding mode optimization control of an inverted pendulum system based on fuzzy adaptive technology

IntroductionThis paper aims at the control problem of nonlinear systems with uncertainty in general, and avoids the deviation of sliding mode controller from the preset constraint region during the convergence process.MethodsA sliding mode control method based on fuzzy adaptive technique was proposed by constructing obstacle Lyapunov function (BLF). Design problem of fuzzy adaptive second-order sliding mode controller considering output constraints. The Lyapunov function of the cutting barrier is designed, and the fuzzy adaptive second-order sliding mode controller is constructed by combining the Lyapunov function with the regression method. A second-order finite output sliding mode controller is designed for the inverted pendulum system. In the case of unknown external disturbance, the mathematical modeling and force analysis of the first order inverted pendulum system are carried out, and the design problem of the fuzzy adaptive second-order sliding mode controller with output restriction is studied.Results and DiscussionThe proposed fuzzy adaptive second-order sliding mode controller has a good control effect in the inverted pendulum system. The fuzzy adaptive second-order controller stabilizes the sliding mode at 0.1 in 1.25 s, while the fuzzy adaptive second-order controller makes the system state reach equilibrium in 15 s. The accuracy of fuzzy adaptive second-order sliding mode controller reaches 99.2%, which is superior to other methods in terms of balance accuracy and recall rate. The controller not only has a fast response speed, but also can effectively suppress system flutter and ensure the rapid stability of the system after constraints. This research method lays a foundation for the design of fuzzy adaptive sliding mode control algorithm.

Event‐Triggered Fault‐Tolerant Control for Nonlinear Multilateral Teleoperation System With Unknown Environmental Forces

ABSTRACTIn this paper, an event‐triggered fuzzy adaptive fault‐tolerant control scheme is investigated to achieve displacement synchronization and force tracking for nonlinear multilateral teleoperation systems subject to actuator faults and communication network constraints. The time‐varying delays and network communication bandwidth limitations are incorporated into the communication network constraints, and the considered nonlinear systems are modeled by using T‐S fuzzy system theory. Then, a novel event‐triggered fuzzy adaptive fault‐tolerant control algorithm is designed to simultaneously estimate and compensate for possible actuator faults during system operation. Next, unlike many existing works, the unknown environmental forces are estimated by introducing radial basis function neural networks, and the estimated results are taken into account in the design of the event‐triggered mechanisms. Finally, a numerical simulation example is presented to illustrate the effectiveness of the designed algorithm.

Implementation of an Improved 100 CMM Regenerative Thermal Oxidizer to Reduce VOCs Gas

In this paper, an improved 100 CMM regenerative thermal oxidizer (RTO) is implemented for low-emission combustion. The existing RTO system is a cylindrical drum structure that cyclically introduces and discharges VOC gas into and from the rotating disk, and which achieves excellent energy efficiency with a heat recovery rate of more than 95%. However, the drive shaft designed under the RTO combustion chamber increases wear around the rotating shaft due to the load of the combustion chamber and there is a problem that the untreated gas is simultaneously released through the outlet due to the channeling phenomenon of the combustion chamber and the drive shaft. In addition, the combustion chamber, used at a high temperature of 800 °C, may cause serious problems such as rotation stop or explosion due to pollutants, dust accumulation, and thermal expansion in the chamber. Particularly when treating VOCs harmful gasses, RTO performance may be degraded due to the burner’s non-uniform temperature control and unstable combustion function. To solve this problem, first, the design of the combustion chamber rotating plate driving device is improved. Second, when treating high concentration VOC gas, the design of combustion chamber considers a temperature increase of up to 920 °C or more. For this, the diameter of the gas burner is 125 mm and the outlet dimension is set to 650 mm × 650 mm to effectively discharge high-temperature waste heat. Third, the heat storage material in the combustion chamber is composed of a ceramic block with a thickness of 250 mm, and the outer diameter and height of the combustion chamber are set to, 2530 mm and 1875 mm, respectively, to optimize gas residence time and heat insulation thickness. Fourth, we supplement safe operation by applying the trip control algorithm of the programmable logic controller (PLC) panel for failure prediction of RTO and the Edge-IoT-based intelligent algorithm for this. Finally, we evaluate the economic performance of 100 CMM RTO by conducting empirical experiments to analyze changes in VOCs removal efficiency, nitrogen oxide emission concentration, and total hydrocarbon (THC) concentration through 10 CMM design and implementation.

Dynamic Inverse Control of Uncertain Pure Feedback Systems Based on Extended State Observer

A novel, precise disturbance rejection dynamic inversion control algorithm has been proposed. In the high-order dynamic surface control system, an innovative approach utilizes a monotonically increasing inverse hyperbolic sine function to construct an extended state observer, which estimates the uncertain functions at each step. The monotonicity of the inverse hyperbolic sine function simplifies the system stability analysis. Additionally, being a smooth function, it avoids the disturbances caused by piecewise functions at their breakpoints in conventional observer construction, thereby enhancing system stability. The accurate prediction capability of the new observer improves the system’s disturbance rejection performance. To address the inherent differential explosion phenomenon in traditional dynamic inversion control schemes, this paper ingeniously employs a tracking signal observer as a substitute for traditional filters, thus avoiding the differential explosion that may occur with first-order filters. Finally, comparative simulations were conducted to validate the effectiveness of the proposed method. The results show that both the observer and the controller possess high-gain characteristics, and the closed-loop system exhibits a fast convergence rate.

System identification-based tuning of PID algorithm parameters by using PSO and genetic algorithms

The PID control algorithm is the most used industrial control method. This is due to its simplicity, ease of use, and because it yields stable results. PID parameters vary for each controller; therefore, finding the optimal parameters is crucial for obtaining good results. However, tuning PID parameters for complex systems is not a trivial task and is also somewhat difficult as conventional techniques rely on time and effort manual tuning or rely on simplified statistical estimation techniques of the system’s model yielding sub- optimal results. In this project, we propose to use machine learning for PID parameter tuning on proprietary historical time series operating process control data. The data is processed with the help of computational and machine-learning techniques to better identify the process model and predict the optimal PID parameters. The research methodology consists of three main steps. First, process-model identification is done by using a Radial Basis Function (RBF) neural network. Secondly, Particle Swarm Optimization (PSO) hybrid with Genetic Algorithms (GA) is used for finding the optimal PID parameters. The PID values predicted by PSO, will be fed to GA optimization process as an initial starting point. The final step consists of integrating the identified process model with the PID optimization algorithm in a computer-based simulation environment (Simulink). The experimental simulations are done for various study cases. Results showed that the predicted PID parameters error rate and standard deviation for PID control and process are decreased, which enhances the process controlling and stability.

Series Active Power Filter for Power Quality Improvement

The paper discusses the theoretical framework and operating principles of the SAPF, including its control strategies based on instantaneous reactive power theory and synchronous reference frame theory. The design criteria for the SAPF components, such as the voltage source inverter, DC link capacitor, and coupling transformer, are also elaborated to ensure optimal performance. Simulation studies using MATLAB/Simulink are conducted to evaluate the effectiveness of the proposed SAPF under various power quality disturbance scenarios. The results demonstrate that the SAPF effectively compensates for voltage sags, swells, and harmonics, maintaining voltage stability and reducing total harmonic distortion (THD) to within acceptable standards. Additionally, a prototype of the SAPF is developed and tested in a laboratory environment, confirming the simulation results and showcasing the practical feasibility of the solution. The study concludes that the Series Active Power Filter is a robust and versatile solution for power quality improvement in modern electrical distribution systems. Future work may involve exploring hybrid configurations combining series and parallel active filters for comprehensive power quality management and the development of advanced control algorithms for enhanced performance under dynamic load conditions.

A novel controller algorithm to improve stability of power system based on a hybrid of fuzzy controller and Gray wolf optimization by coordinating PSS and TCSC with considering uncertainty

A novel controller algorithm to improve stability of power system based on a hybrid of fuzzy controller and Gray wolf optimization by coordinating PSS and TCSC with considering uncertainty