Universe Fuzzy Control Research Articles

Improved beluga whale optimization-based variable universe fuzzy controller for brushless direct current motors of electric tractors

Brushless direct current (BLDC) motors are widely used in electric tractor powertrains, but torque ripple remains a challenge. Proportional Integral Derivative (PID) controllers are effective in steady-state regulation but struggle with load-induced uncertainties. A new method for tuning sensorless BLDC motors by integrating improved Beluga Whale Optimization (IBWO) with an optimal variable universe fuzzy (VUF) controller is proposed. The enhanced IBWO addresses limitations in solving nonlinear systems, optimizing the VUF controller for precise torque control. A fast non-singular terminal sliding mode observer is also introduced for accurate state estimation. The IBWO adjusts the VUF controller parameters in real time, enabling adaptive torque and speed regulation, thereby reducing overshoot and torque ripple. To validate the proposed approach, a dual closed-loop control model is designed to simulate motor behavior under no load, variable load, and variable speed conditions during plowing operations. The results show that the proposed controller reduces torque ripple by at least 75 % and 60 % compared to PID and fuzzy controllers, respectively, and improves speed regulation time by over 26 %, with steady-state errors of 0.6, 0.7, and 0.12 rpm (rpm) under different conditions.

Adaptive Variable Universe Fuzzy Droop Control Based on a Novel Multi-Strategy Harris Hawk Optimization Algorithm for a Direct Current Microgrid with Hybrid Energy Storage

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit.

Modeling and Regulation of Dynamic Temperature for Layer Houses Under Combined Positive- and Negative-Pressure Ventilation.

The environmental control of layer houses with multi-tiered cage systems is influenced by factors such as the structure of the henhouses and the heat dissipation of the flock, leading to low precision and large fluctuations in temperature control. Based on a new combined positive- and negative-pressure ventilation (CPNPV) mode, a dynamic temperature model is constructed. Additionally, a temperature control method for a layer house is designed using a variable universe fuzzy PID control algorithm (VFPID). First, based on the principles of energy and mass balance, and by decoupling the relationship between positive- and negative-pressure ventilation volumes, a dynamic temperature model for layer houses under CPNPV was established. Then, the PID parameters and the proportional relationship between positive- and negative-pressure ventilation were optimized through fuzzy rules, and a proportional exponential function was introduced to adjust the scaling of the universe, enabling fine-tuned control. Finally, a temperature control model for the layer house was built using Simulink. The results show that the coefficients of determination (R2) of the constructed dynamic temperature models are between 0.79 and 0.88, respectively, indicating high accuracy. The designed VFPID method outperformed traditional on-off control and improved control precision by 20-23.53% and 10.34-22.22% compared with PID control and fuzzy PID(FPID) control methods, respectively. This study provides new insights for the development of environmental control equipment and precise environmental regulation of layer houses.

Application of Variable Universe Fuzzy PID Controller Based on ISSA in Bridge Crane Control

Bridge crane control systems are complex, multivariable, and nonlinear. However, traditional fuzzy PID control methods rely heavily on expert experience for initial parameter tuning and lack adaptive adjustment for the fuzzy universe. To address these issues, we propose a variable universe fuzzy PID controller based on the improved sparrow search algorithm (ISSA-VUFPID). First, tent chaotic mapping is introduced to initialize the sparrow population, enhancing the algorithm’s global search capability. Second, the positioning strategy of the northern goshawk exploration phase is integrated to improve the search thoroughness of sparrow discoverers within the solution space and to accelerate the optimization process. Last, an adaptive t-distribution perturbation strategy is employed to adjust the positions of sparrow followers, enhancing the algorithm’s optimization ability in the early search phase and focusing on local exploitation in the later phase to improve solution accuracy. The improved algorithm is applied to tune the initial parameters of the PID controller. Additionally, system error and its rate of change are introduced as dynamic parameters into the scaling factor, which is used to achieve adaptive adjustment of the fuzzy universe, thereby enhancing the safety and reliability of the control system. Simulation results demonstrate that the proposed ISSA-VUFPID control method outperforms ISSA-FPID and ISSA-PID control methods. It reduces the trolley’s positioning time and minimizes the load’s maximum swing angle, demonstrating strong adaptability and robustness. This approach greatly enhances the robustness and safety of bridge crane operations.

Variable universe fuzzy control of once-through steam generator feedwater

The feedwater control of once-through steam generators (OTSGs) is critical to the safety and stability of nuclear power plants. As traditional PID control methods are difficult to ensure good control of OTSGs under flexible operating conditions, a variable universe fuzzy control (VUFC) method is proposed for OTSG feedwater. The widely-used cascade control structure was first adopted for the OTSG feedwater control system. Then the outer-loop pressure and inner-loop feedwater controllers were designed as fuzzy controllers with their fuzzy universes adjusted adaptively during plant operations. To assess the performance of the VUFC method, dynamic responses of an OTSG employing the fuzzy controllers were compared with those adopting the well-designed PID controllers under typical load change transients. Comparison results illustrate that the VUFC method can provide much better OTSG pressure control with smaller overshoots and shorter settling times, demonstrating the applicability of the VUFC method for OTSGs to provide excellent feedwater control.

M3C outer loop control strategy based on variable universe fuzzy PI control

M3C outer loop control strategy based on variable universe fuzzy PI control

Research on air-fuel ratio of rotary engines based on variable universe fuzzy control

Abstract A study on the air-fuel ratio control system for the Wankel rotary engine was conducted, where the overall model of the engine was developed in MATLAB/Simulink based on the mean value model. The variable universe fuzzy integral composite control algorithm was applied to feedback control, and the construction and simulation of the control algorithm were completed in MATLAB/Simulink. The results indicate that compared to PID control, variable universe fuzzy control reduced the engine’s fuel injection error by 60.37% and 35.7% under steady-state and transient conditions, respectively. This demonstrates the favorable control effectiveness of variable universe fuzzy control in rotary engines, providing insights and methods for future research on rotary engine control systems.

Pitch motion suppression of electric vehicle active suspensions based on multibody dynamics

Active suspension control strategies are proposed to suppress pitch motion and address the issue of motion sickness among electric vehicle passengers. In this work, we investigate the performance of the linear quadratic regulator (LQR), variable universe fuzzy control, adaptive-network-based fuzzy inference system (ANFIS), and fuzzy PID control under continuous acceleration and deceleration and bumpy road conditions. First, a 14-degrees-of-freedom (DOF) dynamics model of an electric vehicle is developed based on a semirecursive multibody dynamics method, which was originally proposed by Javier García de Jalón. The vehicle dynamics simulation is performed to accurately model the vehicle states and responses. Second, the LQR, variable universe fuzzy control, ANFIS, and Fuzzy PID control algorithms are tailored via vehicle states, including pitch, rate, and acceleration. Their performances are investigated and compared in detail. Finally, the robustness of the proposed control strategies is further discussed based on various speed bump parameters and initial velocities. The results indicate that the proposed control strategies are capable of suppressing the pitch motion of electric vehicles, enhancing the riding comfort under diverse operating conditions, thereby reducing the likelihood of motion sickness among passengers of electric vehicles.

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions

The vehicle suspension system is a complex system with multiple variables, nonlinearity and time-varying characteristics, and the traditional variable universe fuzzy PID control algorithm has the problems of over-reliance on expert experience and non-adaptive adjustment of the contracting-expanding factor parameters, which make it difficult to achieve a better control effect. In this paper, the system error e(t) and its change rate ec(t) are introduced into the contracting-expanding factor as dynamic parameters to realize the adaptive adjustment of the contracting-expanding factor parameters, and propose a variable universe fuzzy PID control based on dynamic adjustment functions (VUFP-DAF), which uses the real-time contracting-expanding factor to realize the adaptive adjustment of the fuzzy universe, so as to improve the ride comfort of vehicles. The research results show that the proposed VUFP-DAF has strong adaptability and can effectively improve the ride comfort and handling stability of vehicles under different speeds and road excitations, providing a certain technical basis for the development of the semi-active suspension system.

Research on roll temperature compensation of variable domain fuzzy controller based on improved cat swarm optimization

In the hot rolling process, the hot crown of the roll is determined by the roll temperature and the temperature distribution. Furthermore, the hot crown is an important factor, which causes variation in the strip gage along the plane perpendicular to the rolling direction, affecting the strip quality. However, the roll temperature response has the characteristics of nonlinearity, hysteresis and time-varying, which makes it difficult to control accurately by classical control theory and method. In order to accurately control the rolling temperature, a variable universe fuzzy controller based on improved cat swarm optimization (ICSO-VUFC) was established. Firstly, a dynamic mixture ratio and an improved tracking mode were used to improve the optimization capability of the cat swarm. In comparison with the conventional cat swarm optimization (CSO) controller, the proposed process showed better optimization performance. Secondly, a simulation analysis based on MATLAB was employed to compare the ICSO-VUFC with the conventional fuzzy controller (C-FC) and the fuzzy controller based on the improved cat swarm optimization (ICSO-FC). The results reveal that the ICSO-VUFC exhibits the best dynamic and steady performance. Finally, the temperature control accuracy of the rolled regions during different rolling passes under the three fuzzy controllers was examined and compared. The results show that the ICSO-VUFC exhibits the highest control accuracy and stability with a temperature error range of ±4 °C. Through the analysis of the strip crown, it can be seen that the control accuracy of the strip crown can be effectively improved by using ICSO-VUFC to control the roll temperature distribution.

Fuzzy control of temperature in gas flow control system based on mixed cold and hot gases

This study proposes a novel high-temperature gas flow temperature simulation system based on the mixing of cold and hot gas flow. The outlet gas flow temperature is controlled by adjusting the flow rate of the cold gas flow. To achieve consistent control of high-precision and wide-range temperature control of the system. The system mathematical model was constructed and the system characteristics were analyzed. However, traditional PID control suffers from inconsistencies in control performance at high and low temperatures, as well as inconsistent responses during temperature increasing and decreasing processes. To address these issues, fuzzy PID control was introduced, but its parameter tuning proved challenging, resulting in poor consistency of control performance. To expand the adaptability of fuzzy control, a function-based and fuzzy inference-based variable universe fuzzy PID controller was proposed. Experimental and simulation results demonstrate that the fuzzy inference-based variable universe fuzzy PID controller exhibits good dynamic and static performance under different operating conditions. The max settling time and static error are 16.5 s and ± 4 K, respectively. Compared with PID controller, the settling time and static error are reduced by 56.2% and 67.5% respectively. The fuzzy inference-based variable universe fuzzy PID controller overcomes the performance discrepancies during temperature increasing and decreasing processes, different operate temperatures, and various step amplitudes. Consequently, highly accurate simulation of system outlet gas flow temperature is realized. This methodology can be widely applied to control systems with strong nonlinearity and asymmetry, improving the consistency of the system control performance.

Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link. The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansion factor. The control performance is directly related to the contraction-expansion factor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Its simulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.

Impedance Force Control of Manipulator Based on Variable Universe Fuzzy Control

Impedance control is a classic and straightforward control method that finds wide applications in various fields. However, traditional constant impedance control requires prior knowledge of the environment’s stiffness and position information. If the environmental information is unknown, constant impedance control is not capable of handling the task. To address this, this paper proposes a variable universe fuzzy model reference adaptive impedance control method that achieves effective force tracking even in the presence of unknown environmental information. A variable universe fuzzy controller was employed to determine the impedance parameters. The force tracking error and its rate of change were used as two input parameters for the variable universe fuzzy controller, which utilizes fuzzy inference to obtain the incremental values of the impedance parameters. For the introduced model reference controller, a novel adaptive law was employed to obtain the coefficients for contact force and torque. Subsequently, the contact force of the manipulator in Cartesian space was taken as the research object, and a simulation model of a six-joint manipulator was established in MATLAB/Simulink. By comparing it with the constant impedance control method, the feasibility and effectiveness of this control approach were validated.

Path tracking control method for automatic navigation rice transplanters based on VUFC and improved BAS algorithm

AbstractDuring the operation of automatic navigation rice transplanter, the accuracy of path tracking is influenced by whether the transplanter can enter the stable state of linear path tracking quickly, thus affecting the operation quality and efficiency. To reduce the time to enter the path tracking stable state and improve the tracking accuracy and stability for the rice transplanter, path tracking control method based on variable universe fuzzy control (VUFC) and improved beetle antenna search (BAS) is proposed in this paper. VUFC is applied to achieve adaptive adjustment of the fuzzy universe by dynamically adjusting the quantization and scaling factors according to the variations of errors by the contraction–expansion factor. To solve the problem of setting the contraction–expansion factor in VUFC and real-time performance, an offline parameter optimization method is presented to calculate the optimal contraction–expansion factor by an iterative optimization algorithm in a path tracking simulation model, where the iterative optimization algorithm is the BAS algorithm improved by the isolated niching technique and adaptive step size strategy in this paper. To verify the effectiveness of the proposed path tracking control method, simulation and field linear path tracking experiments were carried out. Experimental results indicate that the proposed method reduces the time of entering the stable state of linear path tracking and improves the accuracy and stability of path tracking compared with the pure pursuit control method.

Enhanced variable universe fuzzy PID control of the active suspension based on expansion factor parameters adaption and genetic algorithm

The optimal value of the expansion factor and quantization factor parameters is the key to determine the performance of the variable domain fuzzy proportion-integral-derivative (PID) controller. In view of the problem that the relevant parameters are fixed, difficult to determine and cannot be adjusted adaptively in the traditional variable domain fuzzy PID control, a kind of enhanced variable domain fuzzy PID control (EVUFP) is proposed to improve the ride comfort of vehicles. First, based on the traditional variable domain fuzzy PID control, an adaptive expansion factor controller is constructed to determine and adjust adaptively the parameters of the expansion factor in real time. Then the genetic algorithm is used to optimize the quantization factor and scale factor parameters to seek the optimal value of relevant parameters. And the obtained optimal parameters are substituted into the Simulink model to realize the design of the EVUFP controller. The simulation experiment results under different working conditions show that the proposed EVUFP control strategy can reduce the root mean square (RMS) values of the body acceleration, suspension dynamic deflection and tire dynamic load by more than 54%, 50% and 23%, respectively, improve the ride comfort of vehicles, and provide a new idea for the development of active suspensions.

Enhanced Variable Universe Fuzzy Control of Vehicle Active Suspension Based on Adaptive Contracting–Expanding Factors

Enhanced Variable Universe Fuzzy Control of Vehicle Active Suspension Based on Adaptive Contracting–Expanding Factors

Intelligent vibration control for ultra-high-speed elevators: A type 2 variable universe fuzzy neural network method with input saturation

When elevators accelerate to ultra-high speed, the horizontal vibration suppression is significantly challenged by the complex coupled excitation and uncertain time-varying parameters. Therefore, this paper proposes an intelligent control method with type 2 variable universe fuzzy neural network considering input saturation. Firstly, considering the multilevel relationship of the guide rails, guide shoes, frame and car, a horizontal vibration dynamic model is constructed. To improve the tolerance and adaptability of the control system to the uncertain parameters, the type 2 variable universe fuzzy control is used as the main controller. The discourse of universe is adjusted by the T-S fuzzy neural network, and the anti-saturation controller is designed to compensate for the over-saturation problem aggravated by the networks. The non-parametric inverse model of intelligent adjustable dampers represented by the magnetorheological damper is constructed to accurately calculate the ideal output damping force. The effectiveness of the proposed control method is verified by the elevator experimental data and the bidirectional gas-solid coupling aerodynamic excitation. The results show that the proposed intelligent control method can significantly improve the mean values, peak values and comfort indexes of the horizontal vibration acceleration and tilt angle acceleration of the elevator car. The research provides a new intelligent control scheme for high-speed/ultra-high-speed elevators and other similar vibration suppression systems.

Variable Universe Fuzzy Controller for an Independent Metering System of Construction Machinery

As a new type of hydraulic system, the independent valve control system (IMCS) has higher flexibility compared with the traditional four-side slide valve control system. The independent metering control system (IMCS) is divided into four quadrant modes, based on the speed and load of the actuator. The traditional control methods always use a discrete control strategy to realize the switching between different modes based on mode recognition. However, the spool signals frequently switch, and the actuator operating pressure and speed are not stable when the IMCS is switched between different modes. To solve the above problems, this paper proposes a continuous control strategy for the IMCS. Based on the fuzzy control principle, the variable universe fuzzy controller (VUFC) was designed. The controller contains 49 fuzzy rules, and the output of the controller at any time is calculated by the weighted sum of several fuzzy rules. The VUFC divides the system into 49 rules, which are activated by multiple modes at any time to achieve a smooth transition between different modes. The VUFC was verified in a 37 ton excavator model and compared with the traditional independent metering meter-out (IM_MO) control strategy. The results showed that the VUFC significantly improved the frequent switching of the spool signals, and the running speed and pressure stability of the hydraulic cylinder were significantly improved. At the same time, the response speed of the hydraulic cylinder became faster at the start of the system. The VUFC can realize the continuous control of the IMCS and improve the operating performance of excavators.

Research on variable universe fuzzy anti-rollover control of counterbalanced forklift Based on a general rollover index

Research on variable universe fuzzy anti-rollover control of counterbalanced forklift Based on a general rollover index