Search

An innovative technique for developing fuzzy preview tracking control nonlinear systems using Takagi‐Sugeno (T‐S) fuzzy models is discussed. The design considers robustness against state time‐changing latency, input saturation, and reference tracking. To achieve this, the T‐S fuzzy system was combined with time‐changing latency and input saturation to create an augmented error system, transforming the original fuzzy preview tracking control issue into a stability issue for the augmented error systems. Next, a new fuzzy preview tracking controller was developed by considering the T‐S fuzzy systems' states or outputs, a distinct integrator, and a previewed reference signal to solve the tracking control issue. For the augmented error system's asymptotic stability, new adequate conditions were derived by employing the fuzzy Lyapunov function, small gain theory, and linear matrix inequality (LMI) method. Finally, the proposed method's effectiveness was demonstrated using two numerical examples. [ABSTRACT FROM AUTHOR]

ABSTRACT This article proposes an adaptive predefined‐time event triggered fuzzy tracking control strategy for strict‐feedback nonlinear systems with unknown control direction and mismatched disturbances by applying command filters. The Nussbaum‐type function is employed to tackle the unknown control direction problem, and the fuzzy logic systems (FLSs) are applied to model the nonlinear functions. And then, command filters are adopted to deal with the problems of complexity explosion, and the event‐triggered technique is exploited to conserve communication resources. With the help of backstepping mechanism and predefined‐time stability theory, the constructed adaptive control scheme is proposed to ensure all signals in the closed‐loop system are bounded and that the tracking error converges to a residual set. Finally, a simulation example proves the validity of presented control mechanism. [ABSTRACT FROM AUTHOR]

The article considers fuel cell electric vehicle energy efficiency improvement by means of traction battery remanent state of charge modern control algorithms application which based on fuzzy logic. Fuel cell vehicle traction system used as experimental setup for the study includes brushless dc motor, lithium-ion batteries pack and electronic differential approach implementation. The results of simulation modelling in MATLAB-Simulink software comparing conventional PID-controller based and fuzzy logic based controller for vehicle speed control systems are presented. Fuzzy logic controller provides an electric vehicle dynamic behavior tuning for different driving modes automatically transferring the control system to economy mode when it possible. However, the control system is able to provide fast dynamic behavior when it is required. The proposed control algorithm is tested by means of developed experimental setup. Experimental results validate conducted simulation modelling results and the proposed control algorithm. Application of the proposed fuzzy logic control algorithm to control the electric vehicle dynamic behavior allows energy efficiency improvement and driving range increase. [ABSTRACT FROM AUTHOR]

This article addresses the problem of designing an event‐triggered fault detector within a networked nonlinear system described by a Takagi–Sugeno (T–S) fuzzy model, accounting for time‐varying delays and denial‐of‐service (DoS) attacks characterized by a binary Markov chain. Our focus is on constructing an event‐triggered residual system, which aims to detect malfunctions in the original fuzzy system while optimizing communication resource allocation. Specifically, it is tailored to combat DoS attacks in the event generator‐to‐fault detector channel and mitigate network‐induced time‐varying delays. Utilizing the Lyapunov–Krasovskii function approach, we formulate linear matrix inequalities to establish the necessary conditions ensuring the asymptotic mean‐square stability of the residual system, meeting a predefined H∞$$ {H}_{\infty } $$ performance criterion. Finally, the simulation results are presented to validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the problem of predictor-based fast finite-time fuzzy dynamic surface control for the nonlinear systems with strict-feedback structure is considered. Different from the traditional fuzzy dynamic surface control method, the proposed method utilizes prediction errors to update learning parameters for improving fuzzy logic systems learning behaviors in this paper. This technique can estimate the system unknown function smoothly and cannot result in high-frequency oscillations due to the existence of the overlarge adaptive gains. In addition, based on fast finite-time theorem and backstepping control technique, the developed controller can ensure all signals of the closed-loop are bounded at a finite time. Eventually, the illustrative examples are given to validate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow. [ABSTRACT FROM AUTHOR]

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle's dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility. [ABSTRACT FROM AUTHOR]

This article deals with the security sliding mode control (SMC) issue for T‐S fuzzy systems under random injection attacks. As a way to increase the communication efficiency of network control systems, the weighted try‐once‐discard (WTOD) protocol is utilized to decide whether the sensor nodes transmit or not. A compensating strategy is introduced in this controller side, based on which a fuzzy sliding mode controller depending on the scheduling signal is constructed. Then, the reachability of the given sliding surface together with the stochastic stability of the closed‐loop system are analyzed by incorporating the membership function, and the relaxed stability conditions are derived. Moreover, a GA‐based solving algorithm is proposed to search an optimized sliding matrix and improve the SMC performance. Finally, numerical simulation results are provided to verify the proposed SMC strategy. [ABSTRACT FROM AUTHOR]

This paper investigates adaptive fuzzy finite-time output feedback fault-tolerant tracking control problem for multiple-input multiple-output (MIMO) stochastic non-strict feedback nonlinear systems with non-affine nonlinear faults and states unmeasured. Firstly, high-gain fuzzy state observers are constructed to overcome the state unmeasured problem in the system. Secondly, the mean-value theorem and input compensation techniques are employed to decouple non-affine nonlinear faults, and the resulting unknown control coefficients problem is handled using Nussbaum functions. In addition, dynamic surface control techniques and error compensation mechanisms are considered in the controller design, which effectively decreases the computational complexity of the control scheme. By combining the adaptive backstepping technique with finite-time control, a novel adaptive fuzzy dynamic surface finite-time output feedback fault-tolerant tracking control algorithm is proposed. The designed controller guarantees that all signals in the stochastic systems are semi-global finite-time stable in probability (SGFSP) as well as the tracking errors can converge to a small neighborhood within the origin in finite-time. Finally, two simulation examples are introduced to verify the effectiveness of the suggested control strategy. [ABSTRACT FROM AUTHOR]

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs. [ABSTRACT FROM AUTHOR]

This paper studies the sampled-data control problem for Takagi-Sugeno (T-S) fuzzy systems with variable sampling. To lessen the conservatism of stability criteria, we introduce a refined looped Lyapunov functional (LLF). These functionals incorporate additional information on split sampling intervals and delayed states. Moreover, sampling-dependent matrix functions are presented to relax the conservativeness of the developed LLFs. By resorting to the refined LLFs, new stability and stabilization criteria for T-S fuzzy systems incorporating an H ∞ performance are established. To validate the established conditions, a nonlinear permanent magnet synchronous motor and the Lorenz system are used to demonstrate the reduced conservatism and the merits of the presented methods. [ABSTRACT FROM AUTHOR]

This paper presents an innovative method to tackle the predictive-control challenges associated with large-scale fuzzy polynomial systems comprising interconnected polynomial fuzzy systems. This study models large-scale nonlinear fuzzy systems in a polynomial framework, which can reduce the number of fuzzy rules. We derive the conditions for controller synthesis in the main theorem using the Lyapunov theory and sum-of-squares technique. Simulation results confirm the validity and efficiency of this approach. [ABSTRACT FROM AUTHOR]

This paper designs a preview repetitive control (PRC) policy for a class of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay and external disturbances. First, the T–S fuzzy model is employed to represent a nonlinear plant, once the nonlinear plant is represented by a T–S fuzzy model, a fuzzy improved-equivalent-input-disturbance (IEID)-based PRC law can be designed to achieve better tracking performance and disturbance rejection. Next, based on a new equality constraint, we derive a continuous-discrete two-dimensional (2D) system that paves the way to solve the design problem of the IEID-based PRC law using linear matrix inequality (LMI) approach. Then, by solving LMIs, the gains of the controller and state observer can be obtained. Finally, a numerical example is included to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel fuzzy logic switched model predictive control (MPC) algorithm for articulated steering vehicles, addressing significant path tracking challenges due to varying road conditions and vehicle speeds. Traditional single-model and parameter-based controllers struggle with tracking errors and computational inefficiencies under diverse operational conditions. Therefore, a kinematics-based MPC algorithm is first developed, showing strong real-time performance but encountering accuracy issues on low-adhesion surfaces and at high speeds. Then, a 4-DOF dynamics-based MPC algorithm is designed to enhance tracking accuracy and control stability. The proposed solution is a switched MPC strategy, integrating a fuzzy control system that dynamically switches between kinematics-based and dynamics-based MPC algorithms based on error, solution time, and heading angle indicators. Subsequently, simulation tests are conducted using SIMULINK and ADAMS to verify the performance of the proposed algorithm. The results confirm that this fuzzy-based MPC algorithm can effectively mitigate the drawbacks of single-model approaches, ensuring precise, stable, and efficient path tracking across diverse adhesion road conditions. [ABSTRACT FROM AUTHOR]

Mathematical models, especially complex nonlinear systems, are always difficult to analyze and synthesize, and researchers need effective and suitable control methods to address these issues. In the present work, we proposed a hybrid method that combines the well-known Takagi-Sugeno fuzzy model with wavelet decomposition to investigate nonlinear systems characterized by the presence of mixed nonlinearities. Here, one nonlinearity is super-linear and convex, and other is sub-linear, concave, and singular at zero, which leads to difficulties in the analysis, as is known in PDE theory. Linear and polynomial fuzzy models were combined with wavelets to ensure an improvement in both methods for investigating such problems. The results showed a high performance compared with existing methods via error estimates and Lyapunov theory of stability. The model was applied to a prototype nonlinear Schrodinger dynamical system. [ABSTRACT FROM AUTHOR]

In practice, the time-varying communication delays and actuator failures are the main inevitable issues in nonlinear multilateral teleoperation systems, which can reduce the performance and stability of the considered systems. This article proposed a novel failure-distribution-dependent H ∞ fuzzy fault-tolerant control scheme to realize position synchronization and force tracking simultaneously for multilateral teleoperation systems. Firstly, the nonlinear multilateral systems were modeled as a kind of T-S fuzzy systems with multiple time-varying delays. Then, based on the distribution characteristic of failures, by introducing a series of tradeoff coefficients, a novel failure-distribution-dependent fault-tolerant control algorithm was provided to ensure force tracking in spite of failures, and the purpose of position synchronization was achieved (not only the master and slave robot position synchronization but also the position synchronization between each slave robot). Finally, a numerical simulation example was given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the development and research of the advanced IoT-based fuzzy control system of the irrigation process for smart farming complexes of various types. The proposed automatic control system makes it possible to attain sufficiently high quality indicators of the soil moisture and pH control, which significantly improve the overall efficiency of irrigation processes and, as a result, the processes of growing various plants. In particular, more accurate control of soil moisture and pH allows improving soil microbial activity, optimizing nutrient uptake, increasing water utilization efficiency within the cultivated plants, which directly contribute to increased crop yields and sustainable resource management in agriculture. The designed system is created based on the principles of (a) hierarchical two-level IoT-based control, (b) simple and reliable two-channel fuzzy logic control with high performance and accuracy, as well as (c) easy customization and adaptability for various smart farming complexes. To evaluate the effectiveness of the proposed advanced system, the simulation experiments for automatic control of an irrigation process using the developed fuzzy controllers are carried out in this study at given optimal parameters (soil moisture and pH level) of growing conditions for two different crops: tomato and beet. The analysis of the obtained results of computer simulation shows that the designed system has higher efficiency and quality indicators compared to existing analogs when used for two different crops with significantly different optimal parameters of growing conditions. [ABSTRACT FROM AUTHOR]

An important group of decision-making problems is decision-making under uncertainty, including with incomplete or linguistically described data. Command and control systems, fitting into the multi-sensor paradigm of Industry 4.0/5.0, are becoming increasingly multifactorial. This trend will intensify, requiring uncertainty and incompleteness to be considered and mathematical description and data-processing systems better adapted to them. Aggregators are a group of tools used in solving the aforementioned decision problems, including within fuzzy systems. Aggregating functions are a useful tool mainly in those artificial intelligence systems with problems arising from incomplete data. The aim of this article is to review and describe existing aggregators used in fuzzy control in terms of their usefulness and limitations of their use. Particular attention is paid to the criteria for matching a suitable aggregator to a particular computational problem. This represents an important step towards the further use of this group of technologies in electronic devices and IT systems. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy backstepping controller design to improve the control performance of GUNT RT 512 industrial liquid level system. This system performs the process of reaching the liquid in the level tank to the level reference entered by the controller on it. The original control algorithm run on the ontroller is PID. In this study, the system is tested with a classical backstepping controller and fuzzy backstepping controller. The fuzzy logic controller determines one of the most important parameters of the backstepping controller. Constant and variable liquid levels are applied to the liquid level controller system. The system's dynamic responses are evaluated on response time, settling time, overshoot, and especially steady-state error. The parameter most in need of improvement is the steady-state error for the classical backstepping controller as this controller typically offers asymptotic tracking. Furthermore, the fuzzy backstepping proposes the exact tracking. The simulation results show that improvement has been achieved in almost all performance parameters with the proposed controller. [ABSTRACT FROM AUTHOR]

The heating source temperature of the vacuum belt system (VBD) is an important factor affecting the drying rate and the material quality. However, it has problems with large fluctuation, instability, and hysteresis due to interference from various factors, which increases the drying time and energy consumption. To address these issues, this study proposes fuzzy control and integral control synergistic (FCICS) control to realize temperature regulation of the VBD system, enhancing the performance and stability of the heating source. Simulations were conducted in Simulink, and an experimental verification was carried out based on the constructed experimental system. The results show that the FCICS control outperforms the conventional PID control in terms of material warming rate, temperature stability, and energy consumption, and the transient and stable state performance is improved. Specifically, the material warming rate increased by 15%, temperature stability improved by 20%, and energy consumption decreased by more than 1.74% with the FCICS control strategy. [ABSTRACT FROM AUTHOR]

There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks. [ABSTRACT FROM AUTHOR]

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional–integral–derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional–integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion–extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent perception and adaptability of the 6G network, drones joined this challenge. For large-scale long-range Unmanned Aerial Vehicle (UAV), most of the time during normal flight belongs to fixed altitude flight. It is required to sail along the planned optimal path. Whether it can fly along the optimal path is mainly attributed to the tracking problem of horizontal flight trajectory. In order to minimize the UAV horizontal plane tracking error, it is necessary to consider the influence of many factors (such as strong winds, heavy rain, obstacles, etc.). Due to the complexity of High-Altitude environment, these disturbances are uncertain. In addition, there are some dynamic errors in the model of UAV control system, and these errors also have uncertainties. And, due to the change of global planning path coordinates, the control system needs to adjust the set value in real time during AUV horizontal trajectory tracking, and the conventional control algorithm is difficult to meet the requirements. Therefore, firstly, the influence of prediction uncertainty of grey prediction on AUV horizontal track tracking control is used; Then the grey prediction is improved according to the practical application; Ultimately, the control law is designed by combining the grey prediction with the control method. Finally, the grey prediction fuzzy adaptive PID method of UAV flight control is applied to the planned path simulation, and good control results are obtained. [ABSTRACT FROM AUTHOR]

Summary: The problem of adaptive fuzzy fixed‐time tracking control based on a category of nonlinear systems with unmodeled dynamics and dynamic disturbances is investigated in this article. By introducing the novel fixed‐time dynamic signal, the unmodeled dynamics can be disposed of. On the basis of the fuzzy logic systems (FLSs) and adaptive backstepping technology, the disposing difficulty of the unknown nonlinear portions is reduced. Then, all signals in the closed‐loop system are ensured to be bounded under the fixed‐time Lyapunov stability theory. Simultaneously, the tracking error converges to a small neighborhood of the origin. Eventually, simulation consequences reveal the validity of the presented control method. [ABSTRACT FROM AUTHOR]

With the rapid development of marine engineering in recent years, offshore operations have become increasingly common, making wave compensation platforms crucial for safe operations at sea. This paper presents a pendulum-type wave compensation platform specifically designed for wave compensation applications. The main components of this wave compensation platform include a chassis, support base, hydraulic cylinders, telescopic rods, upper platform, three sets of balancing mechanisms, three sets of tilt angle sensors, and a control system. Firstly, to thoroughly understand the compensatory motion of the pendulum-type three-degree-of-freedom wave compensation platform, kinematic analysis of the entire system was conducted, and the motion inverse solution curves of the mechanism were obtained through simulation using motion simulation software. Secondly, to enhance the compensatory response performance of the platform, a fuzzy PID control algorithm was employed to control the system and achieve attitude control of the platform. Finally, through control system simulation, compared to PID control, fuzzy PID reduces system response delay and successfully meets the expected technical requirements and application needs. [ABSTRACT FROM AUTHOR]

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed. [ABSTRACT FROM AUTHOR]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero. [ABSTRACT FROM AUTHOR]

The focus of this study is to investigate the critical matter of voltage sags in power systems, which have a substantial adverse effect on both the functionality of equipment and the quality of power. Central to this investigation is the integration of hydrogen fuel cells and a D-STATCOM system, which is suggested as a substitute approach for mitigating voltage fluctuations. The hydrogen fuel cell serves as the pivotal component, renowned for its ability to significantly improve power quality through the efficient mitigation of voltage sag concerns. The paper provides a comprehensive analysis of the contribution that hydrogen fuel cell integration with D-STATCOM makes to the reliability of power systems. A systematic methodology is employed, supported by simulations and modeling, to emphasize the critical significance of hydrogen fuel cells in surmounting obstacles related to voltage injection and enhancing power quality. By utilizing a Type-3 Fuzzy system, the research evaluates the dependability of the control system and proposed model. The effectiveness of this method, which is focused on hydrogen fuel cells, is verified by means of MATLAB simulations, which demonstrate its superior performance in comparison to conventional PI and ANFIS controllers. • Integrates hydrogen fuel cells with D-STATCOM for improved power quality. • Achieves notable reduction in voltage sags in power systems. • Enhances power system reliability and stability. • Employs Type-3 Fuzzy system for advanced control. • Validated by MATLAB simulations, outperforming traditional controllers. [ABSTRACT FROM AUTHOR]

Control technologies are of great importance in the rapidly developing electric vehicle industry. Control systems, especially in electric vehicles with more than one drive system or energy source, are at the top of the issues focused on to achieve low fuel and emission values, to provide adequate performance, and not to compromise on safety. Fuzzy logic controller (FLC) based systems are among the most preferred control systems with their adjustable, adaptable features, flexibility, and robust structure against uncertainty. Control systems developed using FLC are divided into sub-branches of propulsion, energy source, vehicle dynamic system, and environmental effects. In this review study, after revealing what needs to be done about the control system in these branches, it is explained why fuzzy logic is preferred in the solution of these problems and the studies done with FLC are summarized. It has been emphasized that fuzzy logic can be a leading solution tool in cases where the mathematical model cannot be presented. In addition, attention was drawn to optimization algorithms that provide the most accurate parameters to operate a fuzzy system in the most appropriate way, and examples of this were given. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the L.N. Gumilyov Eurasian National University. Technical science & Technology series is the property of L.N. Gumilyov Eurasian National University 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.)

Mould bed is crucial for supporting the ship subsection and controlling its deformation during shipbuilding. However, its visualisation and control process intelligence are often inadequate, leading to reduced accuracy and efficiency in ship subsection construction. Aiming at the problem, a three-dimensional (3D) visual monitoring method for the marine intelligent mould bed is proposed based on digital twin, and the overall framework of the management and control system is designed. Meanwhile, a fuzzy PID control principle optimised by the genetic algorithm is proposed to better control the deformation of the ship subsection. On this basis, the verification of the developed system is carried out based on the physical intelligent mould bed, and the 3D visual management and control of the marine intelligent mould bed are realised. It provides a foundation for improving the accuracy and efficiency of ship subsection construction. [ABSTRACT FROM AUTHOR]

A novel combination of three control systems is presented in this paper: an adaptive control system, a type-two fuzzy logic system, and a super-twisting sliding mode control (STSMC) system. This combination was developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE). This controller incorporates two methods to calculate the gains of the switching term in the STSMC utilizing the particle swarm optimization algorithm: (1) adaptive gains and (2) optimized gains. This methodology was applied to a nonlinear model of the Cessna Citation X business jet aircraft generated by the simulation platform developed at the LARCASE in Simulink/MATLAB (R2022b) for aircraft lateral motion. The platform was validated with flight data obtained from a Level-D research aircraft flight simulator manufactured by the CAE (Montreal, Canada). Level D denotes the highest qualification that the FAA issues for research flight simulators. The performances of controllers were evaluated using the turbulence generated by the Dryden model. The simulation results show that this controller can address both turbulence and existing uncertainties. Finally, the controller was validated for 925 flight conditions over the whole flight envelope for a single configuration using both adaptive and optimized gains in switching terms of the STSMC. [ABSTRACT FROM AUTHOR]

The traditional electrical control system has the problem of insufficient function control. This paper designs the engineering electrical control system through the learning algorithm based on fuzzy neural network. The control strategy design module combines fuzzy reasoning with neural network to design intelligent control strategy. Develop a variety of control principles and strategies that can meet the needs and characteristics of the system. The user interface module assists users to monitor and adjust the system, and has the functions of real-time data display and setting control parameters. Fuzzy neural network modeling module combines fuzzy logic with neural network to model the system. In this paper, the sliding window technology is used to collect and process data in real time. When the running time is 1 hour, the system output value is 100.2 and the output fluctuation value is 0.6; When the running time is 2 hours, the system output value is 99.8 and the output fluctuation value is 0.7. The learning algorithm based on fuzzy neural network has little fluctuation and stable performance. [ABSTRACT FROM AUTHOR]

The rotational speed of the hydraulic turbine generator set will change with the load change of the power system, therefore, the speed control system of the unit needs to adjust its operating parameters in time according to the load change. The conventional PID control algorithm is widely used in the current hydraulic turbine speed control system, but it is unable to self-tune the parameters, which makes it difficult to achieve the desired control effect. In order to solve the above problems, this paper combines the neural network and fuzzy control to optimize the PID control parameters, and through the Matlab simulation experiments, it is verified that the control described in this paper can make the hydraulic turbine speed control system have satisfactory control effects in different operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents the synthesis results of a robust H∞ (H-infinity) loop-shaping controller combined with a PID (Proportional-Integral-Derivative) controller using fuzzy logic for the pitch autopilot of a tail control missile. The results take into consideration the change of mass, moment of inertia, and aerodynamic coefficients. The combination of the control signal of the robust controller and the PID controller is formed on the basis of adjusting the membership function of fuzzy logic according to the ITAE (Integral of Time-multiplied Absolute value of Error) optimization criterion. The performance and robustness of the missile autopilot are verified by nonlinear simulation. [ABSTRACT FROM AUTHOR]

In this technical note, the problem of the extended dissipative control for interval type-2 fuzzy model with nonlinear network systems in the event-triggered framework is investigated. A novel dynamic event-triggered strategy is developed to ascertain the instants of activation. It has the advantage of reducing data transmission and optimising the utilisation of communication resources. On the basis of the proposed mechanism, the stochastic Lyapunov function including internal dynamic variables is constructed. Based on this function, coupled with the inequality scaling technique and the application of Dynkin's formula, we derive a sufficient condition for the system's stochastic asymptotic stability and extended dissipativity. The design of the dynamic event-triggered extended dissipative controller becomes challenging because the new sufficient condition is nonlinear for some matrix variables. However, utilising the Projection Lemma and the co-design approach, such a controller can be developed based on the obtained analysis result. The methodology is demonstrated through a rigorous numerical simulation case study. [ABSTRACT FROM AUTHOR]

This article is concerned with the stability analysis of the static output‐feedback polynomial fuzzy‐model‐based (SOF PFMB) control systems through designing a novel membership grade integration (MGI) approach. The nonconvex problems of the SOF PFMB control systems are convexificated into the convex conditions by introducing block diagonal positive‐definite Lyapunov matrix and nonsingular transformation matrix. We proposed new approximate membership functions, that is, Lagrange membership functions (LMFs), which can be introduced into the stabilization process to relieve the conservativeness of stability results. The LMFs are general representations of piecewise‐linear membership functions, which makes the number of stability conditions not limited by the number of sample points. In a fixed subdomain, arbitrary sample points can be employed by the LMFs method and achieve higher approximation capability by increasing more sample points so that membership grades can be incorporated into the system analysis. Furthermore, a novel MGI approach, including the information of premise variables and LMFs, is proposed making the stability conditions more relaxed. Finally, two simulation examples show the merits of the developed techniques. [ABSTRACT FROM AUTHOR]

Maintaining adequate temperatures for preserving food in a domestic refrigerator is a task that is affected by several factors, including the daily use of the appliance. In this sense, this work presents the development of a novel control system based on fuzzy logic that considers usage habits such as the amount of food entering the refrigerator and the frequency of opening doors. Thus, the control comprises input variables corresponding to the internal temperatures of both compartments, the thermal load entered, and the refrigerator door-opening signal. By simulating the usage habits of a refrigerator with a variable-speed compressor, the control performance was evaluated. The results showed that implementing fuzzy control using usage habits was robust enough to maintain adequate thermal conditions within the compartments and a lower thermal fluctuation concerning the reference control of the refrigerator (factory control). In terms of energy, the fuzzy control resulted in an energy saving of 3.20% with the refrigerator empty (without thermal load) compared to the reference control. On the other hand, the individual integration of the thermal load in the fuzzy control resulted in 2.08% energy savings and 5.45% for the integration of the thermal load compared to the reference control. Finally, considering the combination of usage habits, the fuzzy control presented a higher energy consumption than the reference control, around 9.7%. In this case, the fuzzy control maintained more favorable thermal conditions in both compartments, whereas the reference control presented a warmer thermal condition in the freezer. [ABSTRACT FROM AUTHOR]

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control. [ABSTRACT FROM AUTHOR]

CNC machining realizes the automatic control of machine tools with digital information, which is an advanced technology widely used in today's machinery manufacturing industry. In the process of NC lathe machining of parts, thin‐walled workpieces are prone to deformation due to poor rigidity and thinner wall thickness. Therefore, it is necessary to study the deformation suppression of NC parts. By combining fuzzy control with single neuron PID and introducing fast non dominated sorting genetic algorithm, the parameters of thin‐walled workpiece milling are optimized. The results show that the neuron PID algorithm of fuzzy control has no overshoot in the environment with or without interference, and has fast response speed and strong anti‐interference ability. In the case verification, the cutting force controlled by fuzzy neuron PID can quickly reach the reference 240 N and remain stable. The removal rate fluctuates less in the range of 8000–12,000. It can improve the metal removal rate while maintaining a constant cutting force, so as to restrain the machining deformation of parts. At the same time, the introduced fast non dominated sorting genetic algorithm can increase the maximum rotational speed to 10,486.5, the maximum feed rate per tooth from 0.075 to 0.112, and the rotational speed is nearly doubled, effectively improving the processing quality, providing a technical reference for the stable control of the NC machining system, and providing a new idea and method for part deformation suppression. [ABSTRACT FROM AUTHOR]

To reduce both the seat's vertical acceleration and cab pitching vibration in vibratory rollers, an active horizontal seat suspension (AHSS) is proposed and researched based on the dynamic model of the vibratory rollers moving and working and off-road grounds. The fuzzy control is applied to calculate the control force of the AHSS. The indexes in the Root Mean Square acceleration in the seat (𝑅𝑀𝑆􀯔􀯦) and cab pitch angle (𝑅𝑀𝑆􀯔􀰝) have been selected as the objective functions. The isolation performance of the HASS is then compared with the cab isolations controlled (CC) and without control (WC) under two conditions of the vibratory roller moving and working on the off-road surface. The study results show that the vibratory roller's quality using the AHSS has been strongly ameliorated in comparison with both the CC and WC under both moving/working conditions. Especially, comparison with the WC, both the values of 𝑅𝑀𝑆􀯔􀯦 and 𝑅𝑀𝑆􀯔􀰝 with the AHSS are greatly reduced by 54.2 % and 52.6 % under the moving case and by 44.5 % and 50.0 % under working conditions of the vibratory roller. Therefore, the AHSS should be applied to improve both the seat's vertical comfort and shaking in vibratory rollers. [ABSTRACT FROM AUTHOR]

The treatment of low back pain usually includes exercise, analgesics, prostheses, and, in severe cases, surgery. Early treatments based on postural control are essential to prevent low back pain and mitigate permanent damage. This paper presents a wearable device with an estimated cost below $100 that uses inertial units with triaxial accelerometers, gyroscopes, and magnetometers to measure the orientation of three sections of the spine. The device integrates the absolute and relative orientations from the sensors to estimate the posture of the back in real-time and uses a fuzzy system to control a vibration unit that indicates to the user when to correct the posture of the back. This paper validated the device in controlled conditions, obtaining an rms deviation ≤1.24, and conducted a preliminary clinical pilot study with patients afflicted by lumbar hyperlordosis. This paper observed improved postural control and a reduction of low back pain in all cases. These results show the device's promising potential to reduce pain, improve postural therapies, and raise postural awareness in patients with low back pain. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)