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Considering that joint inertial effects of the 3-DOF parallel mechanism within a hybrid robot vary with system configurations, a fuzzy-logic strategy is proposed for feedback and feedforward controller parameters tuning. This approach features the generation of a group of specific configurations representing different inertial levels via clustering analysis, and the creation of a membership function that matches the joint inertial distributions across the entire task workspace. Merging these two threads allows the controller parameters at any arbitrary configuration to be estimated by taking the parameters off-line tuned at the specific configurations as the inputs of the membership function. Both simulation and experimental results on a prototype machine show that only eight specific configurations are required, where the controller parameters of three actuated joints of the 3-DOF parallel mechanism need to be tuned for implementing the fuzzy control strategy. It also concludes that it is significant to use the proposed strategy when the robot moves in the region where the gradient of joint inertial effects varies sharply. [ABSTRACT FROM AUTHOR]

This work introduces an algorithm integrated from two component signals called fuzzy sliding mode control (FSMC). This aims to ensure both road holding and ride comfort criteria rather than just one, as mentioned in previous articles. These mentioned criteria are guaranteed based on the design of membership functions and fuzzy rules, while the stability of the sliding mode framework is evaluated through the Lyapunov function. Simulations are performed in the MATLAB-Simulink interface, with four cases corresponding to different road types. According to the calculation results, the displacement and acceleration values of the sprung mass are the smallest once the FSMC method is used to control automotive suspension. In the last case, the wheel can be separated from the road if the automobile has only a passive suspension system or an active suspension system controlled by the proportional integral derivative (PID) algorithm. However, this does not happen when the FSMC algorithm is applied. As a result, the vehicle's road holding and ride comfort can be ensured in many conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Niche can reflect the changes in the quality of the ecological environment and the balance of ecological state. The more advanced the ecosystem, the more complex and higher-order nonlinearities and uncertainties that are presented. For such an uncertain parameter system with complex nonlinearity, backstepping fuzzy control is a good control method. When the backstepping control method is introduced into the Type-2 fuzzy T-S control principle, the equality index symmetry function composed of ecological factors is used as the backstepping control consequence, and the Lyapunov function is constructed to analyze the stability and find out the adaptive law of the ecological factors in the equality index symmetry function of the control consequence. This reflects that the individual organisms always develop in their own favorable direction, highlighting the bionic intelligent control of the method. Through simulation analysis, the Type-2 Backstepping control method is effective in stability and parameter tracking, which reflects the self-development ability and self-coordination ability of individual organisms, highlighting the physical background and symmetry of the bionic intelligent control of this method. [ABSTRACT FROM AUTHOR]

Energy conservation and emission reduction is a common concern in various industries. The construction process of electric wheel loaders has the advantages of being zero-emission and having a high energy efficiency, and has been widely recognized by the industry. The frequent shift in wheel loader working processes poses a serious challenge to the operator. Automatic shift is an effective way to improve the operator's comfort and safety. The driving intention is an important input judgment condition to achieve efficient automatic shift. However, the current methods of vehicle driving intention recognition mainly focus on passenger cars. The working condition of the wheel loader is significantly different from that of the passenger car, with a high shifting frequency and severe load fluctuation. The driving intention recognition method of passenger cars is difficult to transplant directly. In this paper, aiming at the characteristics of wheel loader working conditions, a fuzzy recognition method based on fuzzy control is applied to driving intention recognition for electric wheel loaders. The throttle, throttle change rate and braking signals are used as inputs for recognizing the driving intention at the current moment of the whole machine. Five types of driving intentions, namely, rapid acceleration, normal acceleration, acceleration maintenance, deceleration and braking, are defined and recognized. In order to verify the effectiveness of the proposed method, simulation and experimental research are carried out. The results show that the proposed driving intention recognition method can effectively identify the driver's intention and provide effective shift signal input for the wheel loader. [ABSTRACT FROM AUTHOR]

The rapid proliferation of EVs has ushered in a transformative era for the power industry, characterized by increased demand volatility and grid frequency instability. In response to these challenges, this paper introduces a novel approach that combines fuzzy logic with adaptive inertia control to improve the frequency stability of grids amidst large-scale electric vehicle (EV) integration. The proposed methodology not only adapts to varying charging scenarios but also strikes a balance between steady-state and dynamic performance considerations. This research establishes a solid theoretical foundation for the inertia-adaptive virtual synchronous generator (VSG) concept and introduces a pioneering fuzzy inertia-based VSG methodology. Additionally, it incorporates adaptive output scaling factors to enhance the robustness and adaptability of the control strategy. These contributions offer valuable insights into the evolving landscape of adaptive VSG strategies and provide a pragmatic solution to the pressing challenges arising from the integration of large-scale EVs, ultimately fostering the resilience and sustainability of contemporary power systems. Finally, simulation results illustrate that the new proposed fuzzy adaptive inertia-based VSG method is effective and has superior advantages over the traditional VSG and droop control strategies. Specifically, the proposed method reduces the maximum frequency change by 25% during load transitions, with a peak variation of 0.15 Hz compared to 0.2 Hz for the traditional VSG. [ABSTRACT FROM AUTHOR]

In the context of the integration of hybrid energy storage systems (HESSs) and electric vehicles (EVs), this paper investigates the load frequency control (LFC) issue of the power system. Weighting coefficients are set for the generators, HESSs and EVs, respectively, to show their different abilities to regulate the power system. A fuzzy logic-based sliding mode control approach is designed to ensure the stable performance of the LFC power system integrated with HESSs and EVs. The improvement of the proposed method is the application of the linear matrix inequality (LMI) toolbox in fuzzy controller design, which solves the limitations and uncertainties caused by trial-error or experience in common fuzzy controllers. There is no general form for the membership function of the fuzzy control. This paper presents a design approach for the membership function based on the calculation results of LMI. Simulations are tested on an IEEE 39-bus system integrated with HESSs and EVs. The simulation results prove that the proposed method reduces the time required for the power system frequency to reach stability by approximately 8.8 % , demonstrating the superiority and usability of the proposed approach. [ABSTRACT FROM AUTHOR]

The South Manchuria Railway, being the earliest constructed railway in Northeast China, has preserved a vast array of modern architectural heritage along its route, which holds significant research value. This study takes the urban agglomerations along the Shenyang–Yingkou section of the South Manchuria Railway as the research object, convening scholars from various fields to construct a hierarchy analysis model for heritage value and using fuzzy control tools to mitigate the impact of subjective cognition on the experimental results, thereby determining the weight values of the influencing factors of modern architectural heritage along the South Manchuria Railway. We invited professional scholars and stakeholders to score the modern architectural heritage, and after combining the weight values derived from the hierarchy analysis model and calculating the weighted average, the heritage value scores were determined for each piece of modern architectural heritage. This study utilizes heritage value scores to measure the degree of danger and the extent of protection required for these architectural heritages, identifies the current shortcomings and insufficiencies in the protection and renewal of these heritages, and compares the effectiveness of heritage conservation efforts in various cities and towns. [ABSTRACT FROM AUTHOR]

Purpose: Bistable laminates (BL) have garnered significant attention due to their broad application potential in morphing structures, particularly for their stable configurations, inherent vibration characteristics, snap-through behavior, and nonlinear dynamic responses under various loads. However, limited research has been conducted on the intelligent active nonlinear vibration control of BL. This study aims to investigate, for the first time, the intelligent active vibration control of four-corner simply supported bistable laminates using a fuzzy controller. Methods: A 14-parameter polynomial configuration function is employed, and within the framework of the first-order shear deformation theory (FSDT) and the Rayleigh–Ritz method, the stable configuration of cross-ply BL with Macro Fiber Composite (MFC) is determined. The electromechanical coupling nonlinear dynamic system under various impact loads is modeled using the Lagrange equation. For active intelligent control, the principal curvatures and their derivatives are used as inputs, while the control voltage determined by the fuzzy inference system serves as the output. An intelligent self-adjustable fuzzy vibration control system is implemented in MATLAB. Results: Simulation results confirm that the proposed fuzzy controller demonstrates superior efficiency and stability in controlling the vibration of bistable laminates. The vibrations of the BL are rapidly attenuated under the influence of the fuzzy controller. Additionally, an in-depth investigation into the vibration control performance of square bistable laminates with varying geometrical parameters under different load types is conducted, further validating the robustness and adaptability of the proposed control approach. [ABSTRACT FROM AUTHOR]

The new energy power generation is becoming increasingly important in the power system. Such as photovoltaic power generation has become a research hotspot, however, due to the characteristics of light radiation changes, photovoltaic power generation is unstable and random, resulting in a low utilization rate and directly affecting the stability of the power grid. To solve this problem, this paper proposes a coordinated control strategy for a new energy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit. Firstly, the variational mode decomposition algorithm is used to separate the high and low frequencies of the power signal, which is conducive to the rapid and accurate suppression of the power fluctuation of the energy storage system. Secondly, the fuzzy control algorithm is introduced to balance the power between energy storage. In this paper, the actual data is used for simulation, and the simulation results show that the strategy realizes the effective suppression of the bus voltage fluctuation and the accurate control of the internal state of the energy storage unit, effectively avoiding problems such as overshoot and over-discharge, and can significantly improve the stability of the photovoltaic power generation system and the stability of the Direct Current bus. It is of great significance to promote the development of collaborative control technology for photovoltaic hybrid energy storage units. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration. More specifically, this research paper establishes a power system model with three DC-DC converters, which includes a hierarchical energy management framework employed in a two-layer control strategy. Three loop control strategies for hybrid electric vehicles based on reinforcement learning are designed in the low-level layer control strategy. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) is used with a network. Three DRL controllers are designed using the hierarchical energy optimization control architecture. The comparative results between the two strategies, Deep Reinforcement Learning and Fuzzy logic supervisory control (DRL-F) and Super-Twisting algorithm and Fuzzy logic supervisory control (STW-F) under the EUDC driving cycle indicate that the proposed model DRL-F can ensure the Root Mean Square Error (RMSE) reduction for 21.05% compared to the STW-F and the Mean Error reduction for 8.31% compared to the STW-F method. The results demonstrate a more robust, accurate and precise system alongside uncertainties and disturbances in the Energy Management System (EMS) of FCHEV based on an advanced learning method. [ABSTRACT FROM AUTHOR]

Unmanned driving technology for agricultural vehicles is pivotal in advancing modern agriculture towards precision, intelligence, and sustainability. Among agricultural machinery, autonomous driving technology for agricultural tractor-trailer vehicles (ATTVs) has garnered significant attention in recent years. ATTVs comprise large implements connected to tractors through hitch points and are extensively utilized in agricultural production. The primary objective of current research focus on autonomous driving technology for tractor-trailers is to enable the tractor to follow a reference path while adhering to constraints imposed by the trailer, which may not always align with agronomic requirements. To address the challenge of path tracking for ATTVs, this paper proposes a fuzzy back-stepping path tracking controller based on the kinematic model of ATTVs. Initially, the path tracking kinematic error model was established with the trailer as the positioning center in the Frenet coordinate system using the velocity decomposition method. Then, the path tracking controller was designed using the back-stepping algorithm to calculate the target front wheel steering angle of the tractor. The gain coefficient was adaptively adjusted through a fuzzy algorithm. Co-simulation and experiments were conducted using MATLAB/Simulink/CarSim and a physical platform, respectively. Simulation results indicated that the proposed controller reduced the trailer's online time by 36.33%. When following a curved path, the trailer's tracking error was significantly lower than that of the Stanley controller designed for a single tractor. In actual experiments, while tracking a U-turn path, the proposed controller reduced the average absolute value of the trailer's path tracking lateral error by 65.27% and the maximum lateral error by 87.54%. The mean absolute error (MAE) values for lateral error and heading error were 0.010 and 0.016, respectively, while the integral of absolute error (IAE) values were 1.989 and 2.916, respectively. The proposed fuzzy back-stepping path tracking controller effectively addresses the practical challenges of ATTV path tracking. By prioritizing the path tracking performance of the trailer, the quality and efficiency of ATTVs during field operations are enhanced. The significant reduction in tracking errors and online time demonstrates the effectiveness of the proposed controller in improving the accuracy and efficiency of ATTVs. [ABSTRACT FROM AUTHOR]

ABSTRACT This article investigates the issue of finite‐time tracking control for uncertain robotic manipulator systems with unknown actuator faults and shifting loads based upon a fuzzy sliding mode control strategy. A novel fuzzy adaptive sliding mode fault‐tolerant control law is synthesized, where a fuzzy approximation algorithm is utilized to fit the unknown plant parameters, potential disturbances, and relational actuator faults, and the corresponding adaptive robust term is designed to estimate the unidentified boundary of the estimated errors, and the boundedness of all the relevant design parameters of the manipulator systems is ensured combining with the average dwell time mechanism of switched system control theory. Furthermore, the reachability of the pre‐devised sliding surface and the finite‐time convergence of tracking error are achieved under the presented controller design. Ultimately, simulation results exhibit the feasibility and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

As online shopping has increased, the business models of online stores have diversified. When consumers cannot experience an actual product, merchants will promote products through a display to attract customers. Virtual reality (VR) provides an immersive platform for consumers to interact with virtual scenarios. Unfortunately, cybersickness remains a problem in VR. The uncomfortable effects of VR hinder its commercial expansion and the broader adoption of 3D virtual stores. Cybersickness has many causes, including personal characteristics, hardware interfaces, and operation behavior. This study develops a fuzzy-control anti-cybersickness intelligent system (FCACIS) with these factors dynamically and actively. The system retrieves the operation value and inferences the cybersickness symptom value (CSSV). When the CSSV exceeds the alarm value, a dialog mode is introduced to remind users to be aware of possible cybersickness. If the CSSV continues to increase, a cybersickness defense mechanism is activated, such as decreasing the field of view and freezing the screen. The experimental results revealed a significant difference in SSQ scores between subjects who navigated a 3D virtual store with and without the FCACIS. The SSQ scores of subjects with the FCACIS (SSQ = 20.570) were significantly lower than those of subjects without the FCACIS (SSQ = 32.880). The FCACIS effectively alleviated cybersickness for subjects over 40 years old. Additionally, the FCACIS effectively slowed the onset of cybersickness in men and women. The anti-cybersickness effect of the FCACIS on flat-panel displays was greater than that on HMDs. The symptoms of cybersickness for a 3DOF controller were also reduced. [ABSTRACT FROM AUTHOR]

Conventional units can regulate grid frequency but often face challenges such as slow response times and accelerated aging. As a solution for frequency modulation (FM), the battery energy storage system offers a promising alternative, enabling efficient frequency regulation while maintaining the state of charge (SOC) within an optimal range. This article proposes a master–slave FM strategy based on fuzzy control to facilitate SOC restoration during the FM process. Initially, the principles of FM for both control types are examined, focusing on the output characteristics of virtual sag control and virtual inertia control. Subsequently, a collaborative output approach combining sag control and inertia control is presented as the primary FM strategy, where frequency changes are analysed, and frequency deviations and SOC are regionally categorized. An adaptive regulator is then designed to dynamically allocate weights to the outputs of the sag and inertia slave FM controls. Finally, simulation results validate the effectiveness and feasibility of the proposed master–slave FM strategy. Under conditions of continuous load disturbance, the strategy demonstrates a 4.07% improvement in SOC recovery compared to conventional control strategies that coordinate outputs through sag and inertia control. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cell water-thermal management systems suffer from slow response time, system vibration, and large temperature fluctuations of load current changes. In this paper, Logistic chaotic mapping, adaptively adjusted inertia weight and asymmetric learning factors are integrated to enhance the particle swarm optimization (PSO) algorithm and combine it with fuzzy control to propose an innovative improved particle swarm optimization-Fuzzy control strategy. The use of chaotic mapping to initialize the particle population effectively enhances the variety within the population, which subsequently improves the ability to search globally and prevents the algorithm from converging to a local optimum solution prematurely; by improving the parameters of learning coefficients and inertia weight, the global and local search abilities are balanced at different stages of the algorithm, so as to strengthen the algorithm's convergence certainty while reducing the dependency on expert experience in fuzzy control. In this article, a fuel cell experimental platform is constructed to confirm the validity and efficiency of the recommended strategy, and the analysis reveals that the improved particle swarm optimization (IPSO) algorithm demonstrates better convergence performance than the standard PSO algorithm. The IPSO-Fuzzy-PID management approach is capable of providing a swift response and significantly diminishing the overshoot in the system's performance, to maintain the system's safe and stable execution. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of observer‐based proportional–integral–derivative control for Takagi–Sugeno fuzzy dynamic systems using a memory triggering protocol. To tackle the challenges of managing nonlinear systems, the Takagi–Sugeno fuzzy approach is employed to approximate these systems with a series of local linear models. A novel memory event‐triggering mechanism is introduced, utilizing a computer's storage unit to store historical data and adjust the triggering threshold based on this data, thereby reducing unnecessary network resource usage. Given that the system state can be affected by external disturbances and become unpredictable, an observer‐based proportional–integral–derivative controller is designed to manage these uncertainties. Finally, a car‐damper‐spring model is presented to validate the proposed methodology. [ABSTRACT FROM AUTHOR]

The application of Caputo-Katugampola g H − differentiability to solve systems of fractional partial differential equations is investigated in this work. The existence and uniqueness of two types of g H − weak solutions for fuzzy fractional coupled partial differential equations are established. Lipschitz conditions are employed, and the Banach fixed-point theorem and mathematical induction are used in our approach. To address the challenges of the initial value problems, a matrix-form Cornwall's inequality is developed. Additionally, a novel analysis of the continuous dependence of the coupled system's solutions on the given conditions and approximate solutions is provided. An illustrative example is presented to validate the findings. [ABSTRACT FROM AUTHOR]

Due to the inherent nonlinearity of pneumatic systems, achieving accurate control of pneumatic actuators remains a challenging task. This study compares three control strategies-PID control, fuzzy control, and sliding mode control-used to guide the motion of pneumatic actuators driven by on/off solenoid valves. The primary objective is to evaluate and compare the performance of each controller in the context of servo-pneumatic systems, highlighting their respective advantages and disadvantages. On/off solenoid valves, which reduce system costs, are utilized in this study. Since these valves can only process on/off signals, pulse width modulation (PWM) is employed to modulate the input signal, adjusting the duty cycle and performance time of the valves. Initially, the governing equations for the actuator and valves are presented, followed by an introduction to each controller. The pulse width modulation algorithm is then discussed, which regulates the control signal to the valve's duty cycle and performance time. Finally, simulation results are provided, comparing the controllers and illustrating their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In addressing the mounting challenges of industrial supervision systems grappling with intricate processes, this study pioneers a transformative paradigm centered on the SCIMAT cement factory. By seamlessly integrating Machine Learning and Fuzzy Logic, the primary aim is to revolutionize real-time control systems, with a keen focus on cement production. SVM integration into the supervision system, coupled with connectivity to a Programmable Logic Controller (PLC), is complemented by fuzzy real-time controllers' regression analysis. Rigorous testing and evaluation validate the proposed approach's reliability, showcasing its effectiveness in discerning optimal system functioning. The system's practical application within a PLC environment underscores its prowess in issuing commands to industrial equipment, thereby enhancing operational efficiency. Going beyond conventional methodologies, our approach amalgamates SVM classification, fuzzy controllers, and real-time regression analysis, delivering a multifaceted solution for industrial supervision. The system's standout achievement is an SVM classification accuracy surpassing 94% compared to other classifiers. The K-Nearest Neighbors (K-NN) model demonstrated an accuracy rate of approximately 93.83%. The decision tree model attained an accuracy of around 83.73%. The logistic regression model achieved an accuracy of 80.25%. These models are not only adept at distinguishing optimal functioning from faults but also adept at preserving the linguistic language used by operators. The study's novelty lies in the holistic integration of SVM and Fuzzy Logic, offering a practical and adaptable solution that not only advances classification accuracy but also significantly reduces maintenance costs, marking a substantial improvement over the traditional methods. This transformative model, validated through SVM classification and practical application, establishes a new standard for flexibility, cost reduction, and overall productivity enhancement in industrial processes. [ABSTRACT FROM AUTHOR]

With the advancement of electric vehicles, their low energy recovery efficiency has become the main obstacle to development. This study focuses on the problem of braking energy loss in electric vehicles during urban road driving and proposes an improved fuzzy control strategy to optimize the energy management of electric vehicles. The exploration first introduces fuzzy control logic to adjust and optimize the energy recovery system of electric vehicles and then introduces a sparrow search algorithm to optimize the adjustment parameters. Finally, using MATLAB R2022a simulation software environment, a comparative analysis is conducted on two driving cycles: urban dynamometer driving schedule and New York City conditions. Simulation results show that the improved fuzzy control strategy can recover 906.41 kJ of energy under urban driving cycle conditions, and the energy recovery rate reaches 49.00%, while the ADVISOR strategy is 507.47 kJ and 27.13%, respectively. The energy recovery rate of the research method is 21.87% higher than that of the comparison method. Improved energy recovery rate of 80.68%. In the driving cycle with New York City, the improved strategy recovered 294.45 kJ of energy, and the energy recovery rate was 48.54%. Compared with the ADVISOR strategy, the energy recovery rate increased by 100.20%, and the energy recovery rate increased by about 110.77%. The research results indicate that the improved fuzzy control strategy is significantly superior to the ADVISOR control strategy, effectively improving energy recovery efficiency and battery charge state maintenance ability under an urban dynamometer driving schedule, achieving more efficient energy management. [ABSTRACT FROM AUTHOR]

Automated Guided Vehicles (AGV) are an essential element of transport in industry 4.0. Although they may seem simple systems in terms of their kinematics, their dynamics is very complex, and it requires robust and efficient controllers for their routes in the workspaces. In this paper, we present the design and implementation of an intelligent controller of a hybrid AGV based on fuzzy logic. In addition, genetic algorithms have been used to optimize the speed control strategy, aiming at improving efficiency and saving energy. The control architecture includes a fuzzy controller for trajectory tracking that has been enhanced with genetic algorithms. The cost function first maximizes the time in the circuit and then minimizes the guiding error. It has been validated on the mathematical model of a commercial hybrid AGV that merges tricycle and differential robot components. This model not only considers the kinematics and dynamics equations of the vehicle but also the impact of friction. The performance of the intelligent control strategy is compared with an optimized PID controller. Four paths were simulated to test the approach validity. [ABSTRACT FROM AUTHOR]

During the robotic grinding of vertebral plates in high-risk laminectomy procedures, programmed operations may inadvertently induce force or temperature-related damage to the bone tissue. Therefore, it is imperative to explore a control methodology aimed at minimizing such damage during the robotic grinding of vertebral plate cortical bone, contingent upon optimal grinding parameters. Initially, predictive models for both the grinding force and temperature of vertebral plate cortical bone were developed using the response surface design (RSD) methodology. Subsequently, employing the satisfaction function approach, multi-objective parameter optimization of these predictive models was conducted to ascertain the optimal combination of parameters conducive to low-damage grinding. The optimum grinding parameters identified were a speed of 6000 r/min, a depth of grind of 0.4 mm, and a feed rate of 3.8 mm/s. Moreover, a multi-layer adaptive fuzzy control strategy was devised, and a corresponding multi-layer adaptive fuzzy controller (MFLC) was then implemented to dynamically adjust the grinding feed speed. The efficacy of this control module was corroborated through Simulink simulations. Simulation results demonstrated that the magnitude of the grinding force fluctuated within the range of 2.2–2.6 N after FLC control, while the fluctuation range of the grinding force was limited to 2.2–2.48 N after MFLC control. This indicates that MFLC control brings the force closer to the target expectation value of 2.39 N compared with FLC control. Finally, the dynamic fuzzy control method predicated on optimal grinding parameters was validated through experimental porcine spine grinding conducted on a robotic vertebral plate grinding platform. [ABSTRACT FROM AUTHOR]

With the speed growth of petroleum engineering, the requirements for the temperature control performance of petroleum heat transfer oil boilers are becoming higher. Traditional temperature control systems have problems such as poor temperature control accuracy. To address these issues, a temperature control system for petroleum heat transfer oil boilers based on microprocessors and proportional‐integral‐derivative is designed. The research first studies the fuzzy proportional‐integral‐derivative control system, and then combines it with a microprocessor to design a new temperature control system. Finally, experiments and practical applications are used to assess the effectiveness of the temperature control system. The results denote that in the simulation experiment, the temperature recognition accuracy of the microprocessor proportional‐integral‐derivative system is 93.26%. At the same time, the system increases the temperature of the oil outlet to around 100°C after about 4 min of boiler operation, and maintains the stable temperature of the oil outlet continuously. In the study of overshoot, the average overshoot value of the system is 10.03%, and the average steady‐state error value is 3.71%. These verification indicators are superior to the comparative control system, indicating that the fuzzy control proportional‐integral‐derivative temperature control system based on microprocessors has good effects in the application of petroleum heat transfer oil boilers. Through this system, the stability and control accuracy of boiler temperature can be improved, and intelligent control of the boiler can be achieved. This is of great meaning for raising the energy and work efficiency of boilers, and reducing energy waste. [ABSTRACT FROM AUTHOR]

In the process of torpedo homing guidance, it is difficult for a single guidance method to adapt to different guidance phases and ensure the torpedo guidance effect effectively. For this reason, this paper designs a fuzzy combined guidance law based on the principle of fuzzy control by combining three different typical guidance methods, namely, fixed lead angle guidance method, proportional guidance method, and variable structure guidance method. The results of simulation and comparison in different environments show that the comprehensive performance of the fuzzy combined guidance law is better than that of other typical guidance laws, which can provide reference for the practical application of torpedo homing guidance.

The application of Caputo-Katugampola gH−differentiability to solve systems of fractional partial differential equations is investigated in this work. The existence and uniqueness of two types of gH−weak solutions for fuzzy fractional coupled partial differential equations are established. Lipschitz conditions are employed, and the Banach fixed-point theorem and mathematical induction are used in our approach. To address the challenges of the initial value problems, a matrix-form Cornwall’s inequality is developed. Additionally, a novel analysis of the continuous dependence of the coupled system’s solutions on the given conditions and approximate solutions is provided. An illustrative example is presented to validate the findings.

Abstract Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.