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ObjectiveThe traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing.MethodsInitially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments.Results and DiscussionThe adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.025 6 m) and course error (0.025 5 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC.ConclusionsThe experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.

Abstract Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2–7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section—water level—rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings.

Complicated terrains, specific regions, and high wind turbulence intensity frequently trigger shutdown protection of wind turbines due to vibration beyond the designated limit, resulting in serious loss of power generation. This paper aims at reducing the frequency of shutdowns and improving operational stability of wind turbines. Operational characteristics of wind turbines under complicated conditions such as specific regions and strong turbulent wind were analyzed. A fuzzy control approach was proposed based on pertinent theory, to deal with complicated operating conditions at specific sites. This strategy incorporates a vibration recognition algorithm to classify vibration conditions and a fuzzy control algorithm for vibration suppression based on the categorized conditions. Compared with the conventional shutdown or sector-based control methods, the proposed fuzzy control strategy of vibration suppression eliminates the frequent starting and stopping of wind turbines, thus ensuring power generation and improving operational stability. Furthermore, a simulation analysis was performed using Bladed software, taking 3.x MW wind turbines as the research object. Subsequent experimental verification was carried out at a wind farm. In the experiment, the application of the fuzzy vibration suppression control method effectively led to the quick ride-through of the wind turbine amid the complicated operating conditions including the specific region and strong turbulent wind. A 23.87% reduction in the maximum RMS of the front and rear accelerations of the nacelle demonstrated a substantial enhancement in the wind turbine reliability.

Structural control as a countermeasure against unwanted structural vibrations is divided into three general categories known as passive, active, and semi-active. Among various structural control devices, the tuned Liquid Damper (TLD) can lend itself well to reducing structural demands through all of the control scenarios. In this paper, demands of Single degree of freedom (SDOF) structures subjected to near and far field ground motions are controlled by a recently developed TLD, named a variably baffled tuned liquid damper (VBTLD), through semi-active and passive control. The effects of the controlled structural period variations on the performance of the linear optimal control algorithm and a fuzzy controller have been examined comparatively. Results show that the semi-active fuzzy controller has better performance than the linear optimal control algorithm. The fuzzy controller can reduce both RMS and peak responses, effectively and its performance improves with increasing the controlled structural period. Furthermore, the fuzzy controller has the same controlling effects under near and far-field earthquakes.

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.

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.

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.

In sensorless control of permanent magnet synchronous motor (IPMSM) with sliding mode observer, the problem of parameter robustness and poor stability seriously affects the control effect of sensorless operation. Therefore, a fuzzy sliding mode observer (SMO) phaselocked loop (PLL) combining particle swarm optimization (PSO) parameter identification and recursive least squares adaptive linear (RLS-Adaline) harmonic extractor is proposed. First, the fuzzy controller is used to process the control parameters of the sliding mode observer and the phaselocked loop. Secondly, the RLS-Adaline harmonic extractor is used to effectively filter the higher harmonic component of the back electromotive force (EMF), thus significantly improving the sensorless control effect. Thirdly, the PSO algorithm is used to identify the parameters of the motor, and the effect of parameter identification is judged by the performance index. The experimental results show that the proposed control method can effectively reduce the speed error and rotor position error. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Tracked vehicles have demonstrated superior passability in hilly and mountainous terrains. Among them, the control system of path tracking has been focused mainly on wheeled agricultural equipment rather than tracked vehicles, particularly with single-sided braking tracks. In this study, the fuzzy control of adaptive preview tracking was proposed to improve the parallel control and tracking accuracy with the lower turning count of tracked vehicles. The 3B55 tracked transport vehicle was modified as the experimental platform from Jiangsu Zhushui Agricultural Machinery. A preview tracking model was constructed via the kinematic analysis of single-sided brake-tracked vehicles. Efficient parallel control was achieved in the steering and straight-line travel within the same control cycle. A multiple input/output fuzzy controller was designed with the lateral and heading deviation as the inputs, while the motion distance and turning ratio as the outputs. Furthermore, an adaptive line-of-sight solution was introduced with the improved Sparrow Search algorithm (SSA) to enhance the accuracy of path tracking with less turning count. The chaotic factor and clustering scavengers were also added to the sparrow population, in order to improve the convergence accuracy and susceptibility to local optima. This adaptive Los solution with the improved SSA was analytically determined as the optimal Los, considering the lateral deviation and steering path angle constraints of the vehicle's current state. Simulation and field experiments were carried out to evaluate the tracking accuracy and turning count. Simulation results demonstrate the convergence speed was significantly improved during the early iterations of the improved Sparrow Search. Moreover, the superiority of the improved SSA was validated to successfully escape the local optima with the optimal fitness value after 37 iterations. The on-line modelling test was implemented with the Los distance of 0.8, 1.5, 2, and 3 m. The lateral deviation increased in the same control cycle when the vehicle was reduced the heading deviation. A longer time was required for the vehicle to complete the on-line, with the increase of the Los distance. The accuracy of vehicle tracking decreased gradually, but the number of turning counts was also reduced. An optimal Los distance was obtained in the fuzzy control of adaptive preview tracking in real time, according to the current position and steering path angle of the tracked vehicle during path tracking. When tracking multi-angle planned paths, the turning count was 89 times, with an error area of 1.73 m2. The field experiments were conducted at the Panax Notoginseng Planting Test Field of the Yunnan University Traditional Chinese Medicinal Materials Mechanization Engineering Research Center. The test field was measured as 40 m in length and 6 m in width. The accuracy of path tracking decreased for the tracked transport vehicle in the field, due to the unevenness of the terrain. However, the trends of tracking accuracy and turning count with the Los were consistent with the simulation. Both the turning count and path tracking accuracy decreased gradually, as the driving speed increased, especially in the tracking fuzzy control with fixed Los. When the vehicle tracked at the speeds of 0.14, 0.47, and 0.83 m/s, respectively, the fuzzy control of adaptive preview tracking reduced turning count by 13.59%, 9.87%, and 11.25%, respectively, and the error areas by 19.93%, 48.48%, and 54.59%, respectively. The field and simulation proved that the Adaptive Predictive Tracking Fuzzy Control for Tracked Vehicles can be expected to improve the tracking accuracy of the vehicle and the number of steering control times in the field tracking. The tracking trajectory was closer to the planned path, indicating better performance. This study can provide innovative ideas and technical support to the automatic navigation of agricultural machinery in the single-sided brake-tracked vehicle. [ABSTRACT FROM AUTHOR]

In this study, we propose a fuzzy control design for a table tennis robot to accurately strike ping-pong balls, regardless of whether they are topspinning or backspinning. The classification of topspin and backspin is determined through a comparison of ball trajectories measured by binocular vision with that of an ideal flying model derived from aerodynamics. By inputting trajectory features into the corresponding model, the striking signal, including contact position and hitting time, can be predicted. A fuzzy inference system is then utilized to derive the face angle of the racket at the hitting moment, which ultimately controls the robot to strike topspinning or backspinning balls. With an average error of approximately 3 cm for contact position prediction and less than 13.5 ms for hitting time prediction, our proposed fuzzy control design has demonstrated its effectiveness in striking spinning balls. [ABSTRACT FROM AUTHOR]

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration's frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications. [ABSTRACT FROM AUTHOR]

The high-precision trajectory tracking and chatter-free performance of the manipulators are of great significance to the improvement of the automation level. Therefore, in view of the problems of chattering and tracking performance degradation of multi-joint manipulators caused by uncertain factors such as external disturbances and modeling errors, an adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm is proposed. First, fuzzy control is chosen to approximate the uncertainty of the manipulator model, which reduces the influence of unknown disturbances on the system, and then the adaptive law is utilized to automatically modify and improve the fuzzy rules to enhance the approximation capability; Second, an improved super-twisting algorithm is used on this basis to put the discontinuous term into the integral term to avoid chattering and eliminate the approximation error; Finally, a comparative simulation experiment is carried out with the 3-DOF manipulator as the research object. The experimental results show that the method in this paper improves the convergence speed by up to 69.74% and the tracking error accuracy by up to 85.11% over the fuzzy control based on adaptive reaching law with no chattering and little difference in control inputs, thus verifying the feasibility and superiority of the method. [ABSTRACT FROM AUTHOR]

In order to improve the control effect of the traditional AC asynchronous motor vector control system and solve the problems of slow response speed and steady-state jitter caused by the characteristics of AC motor, a fuzzy adaptive vector control system was designed, the traditional vector control system was improved, a four-loop vector control system oriented by the rotor magnetic field was designed, and a fixed value controller was added for the steady-state fluctuation problem of traditional vector control. A simulation system was built in MATLAB/ Simulink, and the rectifier inverter circuit used in AC asynchronous motor was designed to make the experimental results more realistic. The simulation results show that the dynamic performance of the designed four-loop control system is better, faster response, improved load resistance, and has stronger adaptability and robustness. [ABSTRACT FROM AUTHOR]

With the continuous increase in the number of irregular buildings, ensuring the safety of building structures has become the primary concern. The study used finite element analysis to estimate the natural frequency of vibration of irregular building structures, and further designed a fuzzy control algorithm combined with magnetorheological dampers to improve their seismic performance. The research results showed that the longitudinal and transverse natural frequencies of Model 1 were 10.44 Hz and 10.51 Hz, respectively, while those of Model 2 were 10.31 Hz and 9.89 Hz, respectively. Using the fuzzy control method, the peak displacement of the building structure was reduced to 11.64 cm, and the peak acceleration was 7.9 m/s². Comparing the open- plus-closed-loop control with open-loop control methods, it was found that although the fluctuation amplitude of the open-loop control method was relatively large, its control effect was poor, while the open-plus-closed-loop control methods had good overall control effect, with the peak acceleration of 8.26 m/s² in the open-loop control. The study provides an accurate method for estimating the natural vibration frequency of irregular building structures and demonstrates the effectiveness of the designed fuzzy control algorithm in controlling building vibration. [ABSTRACT FROM AUTHOR]

To analyze the impact of the power system parameter matching design and energy management scheme on the vehicle performance, taking a fuel cell vehicle as research object, the parameters of main power system components were matched. The power-following energy management strategy and the fuzzy control-based energy management strategy were proposed to improve the economic performance of the vehicle, and the sliding average filtering algorithm was used to optimize the output of the fuzzy control strategy. To model the vehicle and energy management system, the joint simulation platform was established based on AVL-Cruise and Simulink to verify the power performance of the vehicle, and the economic performance of the vehicle under three control strategies was comparatively analyzed. The results show that the fuel cell output power curve of the optimized energy management strategy is smoother and is always within the high-efficiency output interval. Based on the principle of fuel cell priority protection, the optimized energy management strategy has the best performance. [ABSTRACT FROM AUTHOR]

The aero-pendulum is a non-linear system used broadly to develop and test new controller strategies. This paper presents a new methodology for an adaptive PID fuzzy-based tracking controller using a Hunting Search (HuS) algorithm. The HuS algorithm computes the parameters of the membership functions of the fuzzification stage. As a novelty, the algorithm guarantees the overlap of the membership functions to ensure that all the functions are interconnected, generating new hunters to search for better solutions in the overlapping area. For the defuzzification stage, the HuS algorithm sets the singletons in optimal positions to evaluate the controller response using the centroid method. To probe the robustness of the methodology, the PID fuzzy controller algorithm is implemented in an embedded system to track the angular position of an aero-pendulum test bench. The results show that the adaptive PID fuzzy controller proposed presents root mean square error values of 0.42, 0.40, and 0.49 for 80, 90, and 100 degrees, respectively. [ABSTRACT FROM AUTHOR]

A novel framework for adaptive energy management, rooted in deep learning principles, is proposed to minimize fuel consumption in extended‐range electric vehicles amidst intricate driving scenarios. This innovative approach integrates a long short‐term memory (LSTM) network for pattern recognition across three driving patterns and an adaptive fuzzy controller. To mitigate the impact of poor hyperparameter selection on recognition accuracy, Gray Wolf Optimization is employed to optimize the hidden layer nodes, training times, and learning rate of the LSTM. Simultaneously, a genetic algorithm is utilized to optimize the vertex coordinates of the fuzzy control membership function, enabling the adaptive adjustment of parameters in the fuzzy energy management strategy. The condition recognition model accurately identifies the vehicle's driving status and seamlessly transitions to an energy management strategy tailored to the present conditions. This ensures optimal operation, enhancing overall fuel efficiency and performance. The simulation results robustly validate the efficacy of this approach: the GWO‐LSTM network achieves an impressive 97.7% accuracy in recognizing working conditions, surpassing the 88.9% accuracy of the traditional LSTM network. Furthermore, the fuel consumption reduction achieved by the adaptive fuzzy energy management strategy amounts to 11.9% compared with the conventional fuzzy energy management approach. This outcome underscores the tangible enhancement in vehicle fuel economy resulting from the seamless integration of deep learning techniques. [ABSTRACT FROM AUTHOR]

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Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs. [ABSTRACT FROM AUTHOR]

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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

Copyright of Smart Agriculture is the property of Smart Agriculture Editorial Office 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.)

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Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2–7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section—water level—rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings. [ABSTRACT FROM AUTHOR]

In this paper, a predictive-based adaptive anti-saturation tracking control scheme is designed for uncertain multiple-input and multiple-output (MIMO) nonlinear systems subject to input constraints and external disturbance. Taking the safety of the MIMO nonlinear systems into account, a safety saturation threshold, which is less than the physical device saturation threshold, is given. In addition, since all signals of the auxiliary systems are not immediately employed to the systems when the input saturation happens, a new predictive-based auxiliary system is designed. Furthermore, to estimate the compound disturbance, with the help of the fuzzy control technology, the nonlinear disturbance observer (NDO) is used. Then, the stability of the closed-loop systems is proved under the designed controller. Finally, a simulation example is given to illustrate the feasibility of the designed anti-saturation controller. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

With the progress of China's economy, there is an increasing focus on accessibility systems. Enhancements to accessibility infrastructure are being implemented in all types of structures, with a particular focus on educational institutions such as college campuses. This research was carried out to examine the campus accessibility system of Northeastern University's Nanhu and Hunnan Campuses in Shenyang City, China, using failure mode and effects analysis (FMEA) as a methodology and using incorporating fuzzy control, which overcomes the limitations of traditional FMEA. Fuzzy-FMEA integrates the fuzzy linguistic assessment to assist the analysis process, in contrast to standard analysis which mainly relies on subjective judgment. Through calculations, it is known that ramps, barrier-free toilets, and barrier-free entrances are the items with the highest failure modes. The construction of the campus accessible environment needs to prioritize solving the problems of these facilities. The research results also found that there is a lack of research specifications for accessible environments in China's cold regions, and universal specifications are not fully suitable. The accessibility of the new campus was improved and management was improved, reflecting the school's increased awareness of accessible environment construction in recent years. However, there are still many common problems in the old and new campuses, proving that they are not aware of the importance and urgency of improving these problems. In addition, the construction of barrier-free facilities in the administrative office buildings where management and faculty are located on both campuses is generally in good condition. The accessible design of dormitories and canteens commonly used by students is often ignored. Moreover, the rough detailing in many buildings prevents these facilities from being used properly. These are the challenges faced by the Northeastern University in building an accessible environment. [ABSTRACT FROM AUTHOR]

Introduction: The tendon-sheath actuated bending-tip system (TAB) has been widely applied to long-distance transmission scenes due to its high maneuverability, safety, and compliance, such as in exoskeleton robots, rescue robots, and surgical robots design. Due to the suitability of operation in a narrow or tortuous environment, TAB has demonstrated great application potential in the area of minimally invasive surgery. However, TAB involves highly non-linear behavior due to hysteresis, creepage, and non-linear friction existing on the tendon routing, which is an enormous challenge for accurate control. Methods: Considering the difficulties in the precise modeling of non-linearity friction, this paper proposes a novel fuzzy control scheme for the Euler-Lagrange dynamics model of TAB for achieving tracking performance and providing accurate friction compensation. Finally, the asymptotic stability of the closed-loop system is proved theoretically and the effectiveness of the controller is verified by numerical simulation carried out in MATLAB/Simulink. Results: The desired angle can be reached quickly within 3 s by adopting the proposed controller without overshoot or oscillation in Tracking Experiment, demonstrating the regulation performance of the proposed control scheme. The proposed method still achieves the desired trajectory rapidly and accurately without steady-state errors in Varying-friction Experiment. The angle errors generated by external disturbances are <1 deg under the proposed controller, which returns to zero in 2 s in Anti-disturbance Experiment. In contrast, comparative controllers take more time to be steady and are accompanied by oscillating and residual errors in all experiments. Discussion: The proposed method is model-free control and has no strict requirement for the dynamics model and friction model. It is proved that advanced tracking performance and real-time response can be guaranteed under the presence of unknown bounded non-linear friction and time-varying non-linear dynamics. [ABSTRACT FROM AUTHOR]

To improve the handling and directional stability of three-axle heavy vehicles, this paper suggests a control strategy that combines direct yaw moment control (DYC) and active front steering (AFS). The control system's structure is divided into three main layers. Based on an online adjustable index, a fuzzy controller acting as a supervised system decides the cooperation of DYC and AFS in the upper layer. In the intermediate layer, the DYC system controller uses a sliding mode controller to calculate the corrective body moment. The AFS system uses a fuzzy controller to generate the corrective steering angle necessary to achieve the three-axle vehicle motion objective. The algorithm for distributing braking force and the slip ratio control (SRC) system comprises the lower layer. The anti-lock braking system (ABS) in the SRC system is built to produce the necessary braking forces at low slip ratios while preventing the wheels from locking up at high slip ratios. Consideration has been given to a heavy, three-axle, 9-DOF nonlinear vehicle with uncertain dynamics. Trucksim software and simulation tests have validated the model. The proposed control system's satisfactory performance is shown through various maneuvers. [ABSTRACT FROM AUTHOR]

This study is motivated by the need to develop generic neuro-fuzzy motion controllers for autonomous vehicles that may traverse rugged terrains. Three types of target problems are investigated. These problems differ in terms of the expected motion behavior, including cautious, intermediate, and courageous behaviors. The target problems are defined as evolutionary multi-objective problems aiming to evolve near optimal neuro-fuzzy controllers that can operate in a variety of scenarios. To enhance the evolution, sequential transfer optimization is considered, where each of the source problems is defined and solved as a bi-objective problem. The performed experimental study demonstrates the ability of the proposed search approach to find neuro-fuzzy controllers that produce the required motion behaviors when operating in various environments with different motion difficulties. Moreover, the results of this study substantiate the hypothesis that solutions with performances near the edges of the obtained approximated bi-objective Pareto fronts of the source problems provide better transferability as compared with those that are associated with performances near the center of the obtained fronts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

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.)

This study aims to investigate an optimal design problem for vibration control of vehicle suspension models for reducing dynamic responses of the systems. The controller is constructed and optimized based on the approach of the hedge-algebras theory. Optimized parameters include fuzziness measures and reference ranges of state and control variables. Objective functions are considered to minimize the car body vibration and other essential objectives. Initial and optimized controllers have validated their stability through a newly proposed method using rule surfaces of the hedge-algebras–based controller. The controllers based on hedge-algebras theory have higher performance than controllers in previous publications and ensure the system stability and robustness to changes in the car body and wheel masses. The optimal approach in the present work allows determining values of design variables that are appropriate for the controlled models instead of using a trial–error method to evaluate these variables. In addition, the proposed approach to test the system stability allows simplifying this task for controllers using hedge-algebras theory. [ABSTRACT FROM AUTHOR]

In this study, we propose a corruption model that incorporates fuzzy transmission dynamics. We consider a population of individuals, each characterized by a corruption level ranging from 0 to 1, representing their degree of engagement in corrupt practices. The transmission of corruption is not strictly binary but rather influenced by fuzzy logic, where individuals with higher corruption levels are more likely to influence others towards corruption. To analyze the dynamical behavior of the corruption model, we employ mathematical techniques from fuzzy systems theory and dynamical systems theory. We investigate how the corruption levels evolve over time, considering factors such as social interactions, institutional interventions, and individual tendencies. Through simulations and mathematical analysis, we explore various scenarios and observe interesting dynamical behaviors. These include the emergence of corruption clusters, the impact of anti-corruption measures on reducing corruption levels, and the potential for corruption to spread or decline based on different initial conditions and external factors. Our findings highlight the importance of considering fuzzy transmission dynamics in corruption models, as it provides a more realistic representation of corruption in society. This research contributes to a better understanding of the complexity of corruption and provides insights for policymakers and anti-corruption agencies in designing effective strategies to combat corruption and promote ethical behavior in society. [ABSTRACT FROM AUTHOR]

A fuzzy control improvement method is proposed with an integral line-of-sight (ILOS) guidance principle to meet the needs of autonomous navigation and high-precision control of ship trajectories. Firstly, a three-degree-of-freedom ship motion model was established with the battery-powered container ship ZYHY LVSHUI 01 built by the COSCO Shipping Group. Secondly, a ship path-following controller based on the ILOS algorithm was designed. To satisfy the time-varying demand of the look-ahead distance parameters during the following process, especially under different navigation conditions, fuzzy logic controllers were designed for different navigation conditions to automatically adjust the look-ahead distance parameters. Thirdly, a controller was applied that uses a five-state extended Kalman filter (EKF) to estimate the heading, speed, and heading rate based on the ship's motion model with the assistance of Global Navigation Satellite System (GNSS) position measurements. This provides the necessary navigational information, reduces the algorithm's dependence on sensors, and improves its generalizability. Finally, path-following experiments were carried out in the MATLAB experimental platform, and the results were compared with different following algorithms. The simulation results showed that the new algorithm has a better following performance, and it can maintain a smooth rudder angle output. The research results provide a reference for the path-following control of ships. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

The fluctuation and intermittency of wind power generation seriously affect the stability and security of power grids. Aiming at smoothing wind power fluctuations, this paper proposes a flywheel–battery hybrid energy storage system (HESS) based on optimal variational mode decomposition (VMD). Firstly, the grid-connected power and charging–discharging power of the HESS are determined based on the sliding average algorithm. Secondly, the VMD algorithm, optimized using long short-term memory (LSTM), is used to decompose the hybrid energy storage power (HESP) into a series of sub-modes with frequencies from low to high. Then, the state of charge of the battery energy storage system and the speed of the flywheel energy storage system are monitored in real time, and the primary power of the HESS is modified according to the rules formulated by fuzzy control. Finally, through a simulation example, it is concluded that the method meets the requirements of smoothing wind power fluctuations and gives full play to the characteristics of energy storage battery and flywheel energy storage to ensure the stable operation of the energy storage system. The method presented in this paper can provide a reference for HESP configuration and control operation strategy formulation. [ABSTRACT FROM AUTHOR]

In order to meet the necessities of steady and protected operation of a permanent magnet synchronous motor (PMSM) in electromechanical pressure gadget aviation beneath complicated working conditions, a three-phase four-arm inverter fuzzy self-disturbance suppression management (Fuzzy-ADRC) approach for PMSM is proposed to suppress the motor torque pulsation beneath complicated working conditions. Firstly, the defects of the common inverter are analyzed, the three-phase four-bridge inverter is changed via the standard three-phase three-bridge inverter, and the present-day harmonic suppression's overall performance of the three-phase four-bridge inverter is modeled, analyzed, and verified. Secondly, the ADRC and fuzzy management approach is analyzed, the Kalman filter is delivered into the motor pace loop management to enhance the overall performance of ADRC, and then the fuzzy manipulate and ADRC are blended to similarly enhance the torque ripple suppression's overall performance of the everlasting magnet synchronous motor. Finally, the proposed three-phase four-arm inverter and fuzzy-ADRC approach are combined, and contrasted with the normal three-phase three-arm inverter and ADRC method. The simulation consequences exhibit that the proposed manipulation technique can efficiently suppress the torque ripple of everlasting magnet synchronous motor and has robust reliability. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy 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.)

The traction control system (TCS) allows for better power performance and stability of the vehicle. On-demand four-wheel-drive (OD4WD) vehicles are expected to start up in complex situations such as only one wheel landing on the high friction road surface. Therefore, the TCS applied in OD4WD vehicles requires coordinated control of the engine, brake, and transfer case. If the controller is not well designed, the performance of the vehicle would decrease and the components may be damaged. Aiming to deal with the traction control problem of OD4WD vehicles, a coordinated controller is proposed in this paper. First, the models of the main components of the OD4WD vehicle are analyzed. Then, the coordinated controller is designed based on the active disturbance rejection controller (ADRC), the fuzzy controller, and the sliding-mode controller. The estimation and calculation methods of the necessary information are also designed. After that, vehicle experiments of the start-up process are carried out in typical situations to validate the effectiveness of the controller. Results show that the coordinated controller ensures power performance and improves the stability of the vehicle. [ABSTRACT FROM AUTHOR]

Precise temperature control in the stack is essential for optimal performance of proton exchange membrane fuel cells, and effective heat dissipation is required to manage temperature fluctuations during power generation. While traditional PID control of cooling systems can yield satisfactory outcomes, there may be better choices for vehicle applications, where the excessive robustness against the performance can result in cost and efficiency concerns. This study involved the design of a vehicle model to assess the cooling system's efficiency and performance using fuzzy control, which adapts actions to specific conditions, enhancing system efficiency. Fuzzy control was applied to regulate the cooling system's valve opening ratio, considering power demand, including parasitic power. In order to observe the improvement by applying fuzzy control, the evaluation protocol used to calculate the fuel economy of actual hydrogen vehicles was adopted, and fuel economy was measured through nine simulations with different initial SOC settings. As a result of simulations of four cases combining PI control and fuzzy control, the case where fuzzy control was applied showed an improvement in fuel economy of about 3.83%, at 18.527 km/kg, compared to 17.844 km/kg, which was the result when only PI control was applied. In addition to the quantitative evaluation of fuel economy, the impact of fuzzy control on improving fuel economy was qualitatively evaluated through analysis of dynamic data such as parasitic power of pumps and fans and valve opening rates. • The component models of the hydrogen vehicle were constructed and integrated into a system. • A power distribution strategy was implemented, and the reliability of the vehicle system was assessed. • Fuzzy logic aimed at reducing fuel efficiency was applied to bypass valves in fuel cell system. • The vehicle's fuel economy was evaluated through simulations under various initial SOC. [ABSTRACT FROM AUTHOR]