Control Algorithm Research Articles

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.

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

Real-time economic following velocity and gear planning for commercial vehicles based on the bi-level optimization

ABSTRACT Current Predictive Adaptive Cruise Control (PACC) systems pose significant challenges. These include poor collaborative optimisation between economic velocity and the powertrain system, conservative energy-saving strategies and underutilisation of map data due to real-time constraints. A computationally efficient collaborative optimisation method is proposed for economic following velocity and gear shifting based on Bi-level optimisation theory. Based on the Bi-level theory, economic following velocity and gear shifting are decoupled and hierarchically planned. The lower-level subproblem constructs the objective function based on a dynamic-weight safety distance model within the Model Predictive Control (MPC) framework, solving the economic following velocity by the Continuous Generalized Minimal Residual Method (C/GMRES). The upper-level subproblem solves the economic gear shifting by the optimal control law based on the economic following velocity. Finally, comparative experiments are conducted between the proposed PACC algorithm, a standard Adaptive Cruise Control (ACC) algorithm, and a benchmark Dynamic Programming-Model Predictive Control (DP-MPC) PACC algorithm. The results show that the proposed PACC algorithm reduces fuel consumption by approximately 5.76% compared to the ACC. Compared to the benchmark algorithm, the proposed PACC algorithm also demonstrates significantly improved computational efficiency while achieving comparable energy savings.

An Adaptive Sliding-Mode Observer-Based Fuzzy PI Control Method for Temperature Control of Laser Soldering Process

Abstract In the process of electronic packaging, laser soldering exhibits advantages such as non-contact, minimal thermal affected zone, and rapid soldering process. Additionally, the precision of temperature control in laser soldering process is crucial as it determines the quality of the solder joints. However, the uncertain time-variant parameters and the unknown time-varying bounded disturbances make it challenging for high-precision temperature control to achieve high-quality solder joints. To aimed at the parametric uncertainties and disturbances, an adaptive sliding-mode observer-based fuzzy PI (ASMFPI) control method is proposed. In ASMFPI, an adaptive sliding-mode algorithm-based observer is designed to estimate temperature variations caused by uncertainties and disturbances, aiming to improve the accuracy of the temperature model. An observer-based compensation is employed to enhance the temperature control precision. Then, a fuzzy PI control algorithm is adopted to guarantee the track performence and robustness of the closedloop system. Experiments and comparisons are conducted on a laser soldering machine to validate the effectiveness of the proposed method. The results demonstrate that the designed observer significantly improves the accuracy of the thermodynamic model. Furthermore, comparisons of ASMFPI, well-tuned PI, and feed-forward PI methods indicate that ASMFPI exhibits superior temperature track performance compared to the other two methods.

Model predictive control parameter optimization method for autonomous vehicles

Aiming at the problem of insufficient tracking accuracy and long computational time of model predictive control algorithm in trajectory tracking of autonomous vehicles, a model predictive control parameter optimization method based on a nondominated sorting whale optimization algorithm was proposed. In this method, the parameter optimization problem of the model predictive control algorithm was transformed into a multiobjective optimization problem, with the predictive horizon, control horizon, and sample time as optimized objectives and the sum of squared lateral trajectory errors and computational time as optimized variables. The multiobjective optimization problem was solved using the nondominated sort whale optimization algorithm, and then the Pareto optimal solution set was obtained. The best solution was determined using the expert scoring method, continuous order weighted average operator method, game theory combination weighting method, and technique for order preference by similarity to the ideal solution method. Finally, the phase plane method is used to analyze the vehicle handling stability. The experimental results show that compared with the initial method, the lateral trajectory errors of the proposed method are reduced by 66.09% on average, and the computational time is reduced by 54.68% on average. Therefore, the proposed method is considered to be an effective method for parameter optimization of model predictive control.

System to Assist the Driver During a Single Lane Change Maneuver, in the Conditions of Danger Arising from a Change in the Condition of the Road Surface

The article describes the application of a vehicle simulation model to assess the driving hazards that arise due to changes in the tyre–road adhesion during a lane change. An experimentally verified vehicle dynamics model was used. The typical single lane change maneuver, performed on dry concrete, wet concrete, and ice-covered roads, was simulated for three vehicle speeds. The disturbance was introduced as a changed tyre–road adhesion on the target lane. Typical sinusoidal (one-period) input was applied to the steering wheel. Adhesion change may lead to an accident when considering the driver’s reaction time. An original control algorithm has been built. It is the driver assistance system with a double PID controller of the steering wheel angle. The system parameters were determined based on the principles recommended in the automatic control engineering monographs. The controller fulfils its tasks for motion at very high speeds on a homogeneous road surface and for the case where the tyre–road adhesion is changed during a maneuver. Thanks to this, the threat can be avoided.

Optimization and design of electromechanical control automation based on dual motor control algorithm

IntroductionIn response to the high demand for dynamic characteristics and control in current electromechanical automatic control systems.MethodsThis study first analyzes the dual motor system. A novel electromechanical control automation model based on a dual motor control algorithm is proposed through the control strategy of dual motor backlash elimination and digital proportional integral derivative control algorithm.Results and DiscussionThe results indicated that the optimization of the model had a promoting effect on the control performance of the electromechanical automatic control system. Compared with other popular electromechanical control automation models of the same type, the performance of the research method was the best. During the no-load start-up phase, the maximum tracking error and synchronization error speed of the proposed new electromechanical control automation model showed a significant decreasing trend, with the maximum synchronization error between the two motors being only 0.02%. Under steady-state sudden load, the research model could reach a stable state within 3 s, with errors within ±5%.ConclusionIn summary, combining the dual motor control algorithm with the electromechanical control automation method can provide a theoretical basis and practical guidance for designing and implementing efficient dual motor electromechanical control systems.

Admission control algorithm under delay quality of service constraint in multiple packet reception-capable visible light communication system

Admission control algorithm under delay quality of service constraint in multiple packet reception-capable visible light communication system

Design and Simulation of Steering Control Strategy for Four-Wheel Steering Hillside Tractor

Steering control is vital for hillside tractors, and four-wheel-steering technology significantly enhances stability and flexibility. However, existing methods often overlook the slope–stability relationship. In order to address this issue, considering the impact of tractor wheels on the steering characteristics, a nonlinear three-degree-of-freedom dynamic model based on the basic requirements within a 15° range of slope angles was derived. Subsequently, a four-wheel steering control strategy for hillside tractors was designed based on this model, incorporating a fuzzy PID control algorithm. The dynamics model was validated on the high-fidelity Carsim/Simulink co-simulation platform, and relevant experiments were conducted in Matlab/Simulink. Results showed that fuzzy PID control reduced the yaw rate’s average settling time from 0.36 s to 0.1 s and the response value from 0.28 rad/s to 0.038 rad/s. In addition, the lateral velocity and sideslip angle responses closely matched ideal values. Thus, the tractor with four-wheel steering under fuzzy PID control exhibited improved wheel angle flexibility and higher tracking accuracy. Finally, hardware-in-the-loop-experiments were conducted, confirming the algorithm’s effectiveness in ensuring stability and meeting the requirements of semi-physical simulation scenarios. This research provides a foundation for potential applications in tractor manufacturing to improve control performance on hilly terrains.

Optimized Configuration and Operation Of Isolated Microgrid Systems For Rural Electrification: Baron Technopark

Microgrid systems represent a significant advancement in energy supply technologies, particularly for rural communities lacking access to electricity; however, these systems are predominantly reliant on diesel generators (DG). The formulation and selection of suitable configurations, alongside operational patterns, must be meticulously evaluated in the pursuit of economically viable and dependable microgrid systems. Consequently, this research sought to devise an optimal configuration and operational for microgrid systems situated in isolation, utilizing the Baron Techno Park (BTP) in Indonesia as a case study. The optimization process was executed utilizing HOMER software, integrated with an operating cost comparison, with particular emphasis placed on daily load fluctuations, the selection of control algorithms, the reconfiguration of the power supply system, and the regulation of diesel generator operational hours. The proposed microgrid system yielded a surplus energy production of 16.7%, a renewable fraction (RF) of 100%, a levelized cost of electricity (LCOE) of $5.6 per kWh, a net present cost (NPC) of $3,97M. In summary, the study shows that by slightly increasing the capital cost of PV system procurement, it can reduce the operating cost of $629 from the base system in the long term.

Comparison of Two Fuzzification Algorithms (EC-pH and EC-pH Error Error Difference Fuzzifications) of Nutrient Solution Control in Plant Factory

Comparison of Two Fuzzification Algorithms (EC-pH and EC-pH Error Error Difference Fuzzifications) of Nutrient Solution Control in Plant Factory

Hydrophobicity of periodic structure with taper angle under pressure impact

Biomimetic periodic structures have garnered attention due to their excellent water repellency. The normal-taper angle, which is aspects of the cross-sectional structure, is important factor in achieving water repellency and pressure resistance; however, the underlying physical phenomenon has not been fully explained. Moreover, once a surface becomes hydrophobic, it is difficult to measure the apparent contact angle. The purpose of this paper is to clarify the taper angle that provides high water repellency under pressure impact conditions by formulating the relationship between the taper angle and the height of a droplet bouncing, instead of traditional contact angles, using experimental results. We fabricated multiple samples with different taper angles and groove width/tooth width ratios, through micro-processing using a femtosecond-pulsed laser and a control algorithm, and investigated their effects on water repellency. By using height of a droplet bouncing as an evaluation parameter, we were able to effectively differentiate between taper angles in terms of water repellency. Additionally, we suggested that in the bouncing phenomenon, where droplets are given velocity by falling, the sidewall of the periodic structure and the taper angle affect liquid repellency. To explain this phenomenon, we proposed a pressured-taper angle model where a droplet is pressed against the taper angle. Based on both experimental findings and the pressured-taper angle model, a relationship between the equilibrium contact angle, the taper angle, and the lifting force angle was revealed. Moreover, using this pressured-taper angle model, the taper angle of the periodic structure to achieve maximum liquid repellency was estimated from the equilibrium contact angle of the base material.

A Scalable Convex Min–Max Optimization Algorithm for Robust Control of Polytopic Positive Systems

ABSTRACTIn this paper, we investigate the robust control of a class of bilinear positive systems. The main objective is to minimize the norm of the controlled system while dealing with polytopic uncertainties. In contrast to traditional Lyapunov‐based robust control problems with polytopic uncertainties, we establish that minimizing the objective function over the vertices of the polytopic set, rather than considering its infinite elements, is adequate for achieving the globally optimal solution. Following this, we present a customized algorithm that relies on partial cutting plane to address the min–max problem. The introduced robust control strategy exhibits global convergence to the optimal solution and demonstrates scalability with the number of states and the number of vertices in the uncertain family. Simulation results validate the effectiveness of the proposed strategy in handling uncertainties.

Optimal control of grid-connected overvoltage of distributed photovoltaic power generation based on cluster division

To address the overvoltage problem caused by the reverse flow of current when a high proportion of distributed photovoltaic (PV) is connected to the distribution network, this paper proposes a grid-connected voltage regulation control strategy based on the cluster division of the Distributed Model Predictive Control (DMPC) algorithm. Firstly, the overvoltage responsibility of each node is calculated using the Shapley value method. This is combined with k-means clustering to achieve effective cluster division, enabling dynamic adjustment of the active and reactive power of photovoltaic power generation units to stabilize regional voltage. Secondly, a group grid-connected voltage control strategy is introduced. This strategy controls the active and reactive power outputs by integrating real-time power output and voltage information from PV generating units in the region with the DMPC algorithm, ensuring overall voltage stability of the grid-connected system. Finally, actual overvoltage data from a 10 kV distribution line in the Dingxi power grid, Gansu Province, is used to verify that under the proposed control strategy, PV grid-connected overvoltage nodes are maintained within 1.06 p.u. The control effect is improved by a margin of 0.05 compared to traditional control methods. This demonstrates the effectiveness of the grouped grid-connected voltage regulation control strategy, achieving smoother voltage regulation performance in distributed PV grid-connected systems.

Teleoperation system for multiple robots with intuitive hand recognition interface

Robotic teleoperation is essential for hazardous environments where human safety is at risk. However, efficient and intuitive human–machine interaction for multi-robot systems remains challenging. This article aims to demonstrate a robotic teleoperation system, denominated AutoNav, centered around autonomous navigation and gesture commands interpreted through computer vision. The central focus is on recognizing the palm of the hand as a control interface to facilitate human–machine interaction in the context of multi-robots. The MediaPipe framework was integrated to implement gesture recognition from a USB camera. The system was developed using the Robot Operating System, employing a simulated environment that includes the Gazebo and RViz applications with multiple TurtleBot 3 robots. The main results show a reduction of approximately 50% in the execution time, coupled with an increase in free time during teleoperation, reaching up to 94% of the total execution time. Furthermore, there is a decrease in collisions. These results demonstrate the effectiveness and practicality of the robotic control algorithm, showcasing its promise in managing teleoperations across multi-robots. This study fills a knowledge gap by developing a hand gesture-based control interface for more efficient and safer multi-robot teleoperation. These findings enhance human–machine interaction in complex robotic operations. A video showing the system working is available at https://youtu.be/94S4nJ3IwUw.