Traditional Fuzzy Control Research Articles

Energy management strategy for extended range electric logistics vehicle based on type-2 fuzzy control

To overcome the limitations of fuzzy energy management strategy (EMS) in describing uncertainty, this paper proposes an EMS based on type-2 fuzzy (T2F), which optimizes energy consumption for extended range electric logistics vehicle (ERELV). Firstly, establish an energy consumption simulation model for ERELV. Then, an EMS based on T2F controller is designed. The strategy takes the state of charge (SOC) and the demand power as the input, and the range extender power as the output. This paper analyses the influence of T2F control strategy on the energy consumption of the ERELV, and uses the model-based design method to verify the EMS in the Hardware-in-the-loop (HIL) simulation test. The test results show that compared with the traditional fuzzy control strategy, the T2F strategy can reduce the engine’s energy loss by 12.58%. Finally, actual fuel consumption tests were conducted on the established ERELV model. By comparing the results of C-WTVC and CHTC-HT operating conditions, the model was verified to meet the research requirements of EMSs in this paper and has practical significance.

Adaptive Factor Fuzzy Controller for Keeping Multi-UAV Formation While Avoiding Dynamic Obstacles

The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness.

Purity control of simulated moving bed based on advanced fuzzy controller

Simulated moving bed (SMB) technology is considered one of the most successful techniques in chromatographic separation. However, due to the nonlinearity caused by discrete events and sensitivity to numerous separation performance parameters, purity control in SMB systems has been a challenging issue. Fuzzy controllers are increasingly popular in industrial environments due to their simplicity and effectiveness in handling nonlinearity. However, traditional fuzzy controllers used in industry often overlook considerations of error acceleration, resulting in slight deviations from target values under steady-state conditions and oscillatory behavior when system parameters change. This study proposes an advanced fuzzy controller, where in a series of experiments, the purity control targets for component B are set at 94% and 96%, and for component A are set at 96% and 96%, respectively. Experimental results indicate that the advanced fuzzy controller achieves higher precision, with an average deviation of around 0.1%, for both components B and A. Importantly, under variations in adsorbent parameter(from 0.01 to 0.03), feed concentration(from 4.5 to 5.2), and switching time(from 178 to 182), the experimental results demonstrate smoother control with the advanced controller, particularly when oscillations occur with conventional fuzzy controllers due to switching time variations, indicating robust control with the advanced fuzzy controller.

Design and implementation of facility intelligent carbon dioxide incubator control system based on POA optimized fuzzy PID

To solve the problem that traditional carbon dioxide incubators are difficult to accurately control the required carbon dioxide environment to the detriment of experimental cultivation, and given the difficulties of time lag, nonlinearity, time-varying, and poor adaptive ability of the incubator control system, this study designs a set of intelligent carbon dioxide incubator control system suitable for laboratory cultivation. The system is based on the POA (Pelican optimization algorithm) to optimize the fuzzy PID (Proportional Integral Derivative) control to regulate the opening degree of the solenoid valve to accurately control the carbon dioxide concentration in the incubator to the set range. In this study, the simulation and comparison tests of POA-based optimized fuzzy PID control, PID control, and fuzzy PID control are carried out by MATLAB/Simulink, and it is found that the overshooting amount of the system is reduced by 11.6%, the regulating time is shortened by 69.3 s, and the steady state error is reduced by 0.1% compared with that of the traditional fuzzy PID control. It can be obtained through practical tests that the accuracy, response, and stability of the optimized fuzzy PID control of the pelican algorithm are better than those of the fuzzy PID control method, and it has better robustness. Therefore, the carbon dioxide incubator control system has high practical application value.

Intelligent control decision integrating fuzziness and randomness for automatic management of cash flow.

Automatic management of cash flow from the perspective of cybernetics decisions can improve work efficiency and accuracy of cash flow management. Disadvantage of traditional fuzzy control method is that it only expresses fuzziness and ignores randomness. The automatic management of cash flow involves variables representing the fuzziness and randomness of human cognition which need new calculation methods to solve. Based on fuzzy control this paper proposes a cloud set control decision method for cash flow management. Cloud set and its I operation and P operation are described. Methods are studied including observation variables and control variables, fuzziness of observation variables and control variables, description of rules, and cloud reasoning based on cloud set. The method is applied successfully in automatic management of cash flow in which control amount of expenditure intensity is -2.285. It is shown that this method can effectively obtain reasonable control quantities considering fuzzy and random properties by the comparison with fuzzy control method. The method for automatic management of cash flow proposed has greater objectivity and effectiveness for the integration of fuzzy and randomness representing human cognition and decision.

Application of hedge algebras algorithm for mobile robot controller via hand gestures and wireless protocol

With the superior development of technology, mobile robots have become an essential part of humans’ daily life. Consequently, interacting and dealing with them pushes us to develop and propose different suitable Human-Robot Interaction (HRI) systems that can detect the interacted user’s actions and achieve the desired output in real-time. In this paper, we propose a closed-loop smart mechanism for two main agents: the hand gloves’ controller and the mobile robot. To be more specific, the developed model employs flex sensors to measure the curve of the finger. The sensor signals are then processed by aiding the Hedge Algebras Algorithm to control the movement direction and customize the speed of the mobile robot via wireless communication. Numerical simulation and experiments demonstrate that the mobile robot could operate reliably, respond rapidly to control signals, and vary its speed continually based on the different finger gestures. Besides, the control results are also compared with those obtained from the traditional fuzzy controller to prove the superiority and efficiency of the proposed method.

A Fused Deep Fuzzy Neural Network Controller and Its Application to Pneumatic Flexible Joint

With multilayered deep neural networks (DNNs) distinguished from the traditional single-layer neural networks (SNNs) by the depth, deep learning is a powerful paradigm possessing the capability of feature learning from big data. However, as a completely deterministic model, deep learning does not perform well in dealing with uncertain information and must be pretrained before being applied, which hinders the broad application of deep learning in real-time control. In this article, we introduce the knowledge-based fuzzy logic system (FLS) into deep learning and propose a fused deep fuzzy neural network (FDFNN) controller. The FDFNN controller derives the control signal from both deep fuzzy neural networks and the knowledge-based FLS. At the beginning of control, the FDFNN controller performs like a traditional fuzzy logic controller (FLC), which eliminates the pretraining procedure of deep learning. With another FLS providing supervised signal, the network weights of DFNN's hidden layers can be updated during the control process, which endows the FDFNN controller with online learning capability. A Lyapunov-based convergence analysis is conducted to guarantee the stability of the learning procedure. Both numerical simulations and real-world experiments validate that the proposed method effectively improves the real-time tracking control performance of a pneumatic flexible joint system.

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.

Squirrel search algorithm based intelligent controller for interconnected power system

ABSTRACT In this paper, a novel controller, namely fractional order fuzzy proportional-integral-derivative (FOFPID) has been proposed for the dual area power system of hydro-thermal-gas units (DAHTG). The squirrel search algorithm (SSA) is implemented to achieve the optimal values of the controller tuning parameters. Further, various non-linearity constraints, such as governor dead band (GDB), generation rate constraint (GRC) and communication time delays (CTDs) have also been considered to test the effectiveness of the proposed controller in a real-time environment. The step load of 10% is provided as the disturbance under the regulation. In addition, the coordinated strategy of a static synchronous series compensator (SSSC) device and superconducting magnetic energy storage (SMES) is employed in the proposed system to enhance the system’s performance. Further, the accuracy, effectiveness and robustness of the proposed controller are revealed through the comparative analysis with classical PID and traditional fuzzy PID controllers. Furthermore, presented controller efficacy is revealed with the other recently reported control techniques by implementing it on the two-area thermal system with GDB. The comparative analysis shows the better performance of the proposed controller than these two. Finally, the sensitivity analysis is performed to check the controller robustness.

An Improved Differential Evolution Adaptive Fuzzy PID Control Method for Gravity Measurement Stable Platform.

In the platform gravimeter, the stabilization accuracy of the gravimetric stabilization platform is crucial to improve the accuracy of gravity value measurements due to its uncertainties, such as mechanical friction, inter-device coupling interference, and nonlinear disturbances. These cause fluctuations in the gravimetric stabilization platform system parameters and present nonlinear characteristics. To resolve the impact of the above problems on the control performance of the stabilization platform, an improved differential evolutionary adaptive fuzzy PID control (IDEAFC) algorithm is proposed. The proposed enhanced differential evolution algorithm is used to optimize the initial control parameters of the system adaptive fuzzy PID control algorithm to achieve accurate online adjustments of the gravimetric stabilization platform's control parameters when it is subject to external disturbances or state changes and attain a high level of stabilization accuracy. The results of simulation tests, static stability experiments, and swaying experiments on the platform under laboratory conditions, as well as on-board experiments and shipboard experiments, all show that the improved differential evolution adaptive fuzzy PID control algorithm has a higher stability accuracy compared with the conventional control PID algorithm and traditional fuzzy control algorithm, proving the superiority, availability, and effectiveness of the algorithm.

A Control Method for Water Cannon of Unmanned Fireboats Considering Wind and Ship Motion Disturbances

In order to realize accurate and fast firefighting at sea, a control method for water cannons of unmanned fireboats considering wind and ship motion disturbances is presented. This method combines information fusion, computer vision, and prediction technology based on neural network. Firstly, a prediction model of the jet trajectory of the fire water cannon considering the disturbances of constant horizontal wind is established, and the effective range of the water cannon’s angles under the target working environment is obtained. Secondly, fusing the visual recognition information and predicted ship motion attitudes information, a double adaptive fuzzy controller is designed to compensate for the disturbances caused by the change in ship motion attitudes. Meanwhile, the online particle swarm optimization (PSO) is applied to fuzzy control to improve operational accuracy while enhancing the ability to adapt to environmental changes. The proposed control method was experimentally verified. As a result, the adaptive fuzzy controller based on the PSO can self-adjust the parameters to adapt to the changes in the working environment within 0.6 s, and the efficiency is improved by about 20%∼50% compared with the traditional fuzzy control. The double adaptive fuzzy control can reach a stable and effective working state within 10 simulation steps, and the RMSE of the drop point error is only 3 ×10−3 m for 40 simulation steps after stabilization, which can effectively resist disturbances and improve efficiency and control accuracy. The control method can provide a practical reference for engineering applications of water cannon control of unmanned fireboats.

Research on the Intelligent Energy Governance of Parallel Hybrid Vehicle Based on Deep Learning

To realize the intelligent energy governance of hybrid vehicles, a Deep-Q-Network energy controller based on the construction of parallel hybrid vehicle model is proposed, which aiming at energy loss problem of parallel hybrid vehicles and combining deep learning with reinforcement learning, and it is simulated through the ADVISOR software platform and compared with the traditional fuzzy logic strategy. The experimental results indicate that the DQN-based control strategy proposed in this paper reduces both the energy consumption and exhaust emissions of parallel hybrid vehicles. Compared with the traditional fuzzy control strategy, fuel consumption is reduced by 0.43L while the fuel economy increases by 10.9%. and exhaust gas such as CO44, CO, NOxx the emission were reduced by 28.9%, 0.2%, and 7.4%, respectively. It shows the feasibility and effectiveness of the proposed methods.

A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem

When the control rules of traditional fuzzy controller are determined, it comes to be time-consuming and laborious to adjust for different usage conditions. Therefore, the timeliness cannot be guaranteed to solve the timeliness problem, and a fuzzy controller with modifiable factors is designed. While the entire control table is affected by the modifiable factors selection table, all previous control parameters need to be reset. In light of above problems, this paper firstly proposes new fuzzy controller design methods, which retain strengths of traditional controller and controller with modifiable factors. It effectively overcomes the shortcomings of the two controllers mentioned above, and only need to adjust the compromise factor for different working conditions of proposed controller, which is more convenient and efficient. Secondly, the proposed fuzzy controller also adopts a four-layer neural network to optimize the control rules of compromise to improve control precision and system robustness. Finally, the excellent characteristics of proposed controller are verified through simulation research, and the simulation result proves the proposed fuzzy controller has the advantages of higher control precision and smaller transition.

Research on Hydraulic Servo Valve Control Based on Fuzzy RBF

Aiming at the problems of low control precision and slow response speed of traditional control of actuators in hydraulic systems, the mathematical model of the valve-controlled cylinder is established as the research object, and the control strategy of fuzzy RBF neural network is proposed to control hydraulic servo valves On the basis of traditional fuzzy control, fuzzy control and RBF neural network control are integrated. According to the mathematical model of the system, a tracking controller is designed to realize precise control and real-time feedback of the valve-controlled cylinder system. The simulation results show that compared with traditional PID and fuzzy PID systems, the fuzzy RBF neural network strategy has high control precision, fast response speed, better robustness, and stability.

A Novel Fuzzy Controller for Visible-Light Camera Using RBF-ANN: Enhanced Positioning and Autofocusing.

To obtain high-precision for focal length fitting and improve the visible-light camera autofocusing speed, simultaneously, the backlash caused by gear gaps is eliminated. We propose an improved RBF (Radical Basis Function) adaptive neural network (ANN) FUZZY PID (Proportional Integral Derivative) position closed-loop control algorithm to achieve the precise positioning of zoom and focus lens groups. Thus, the Levenberg-Marquardt iterative algorithm is used to fit the focal length, and the improved area search algorithm is applied to achieve autofocusing and eliminate backlash. In this paper, we initially adopt an improved RBF ANN fuzzy PID control algorithm in the position closed-loop in the visible-light camera position and velocity double closed-loop control system. Second, a similar triangle method is used to calibrate the focal length of the visible-light camera system, and the Levenberg-Marquardt iterative algorithm is used to fit the relation of the zoom potentiometer code values and the focal length to achieve the zoom position closed-loop control. Finally, the improved area search algorithm is used to achieve fast autofocusing and acquire clear images. The experimental results show that the ITAE (integrated time and absolute error) performance index of the improved RBF ANN fuzzy PID control algorithm is improved by more than two orders of magnitude as compared with the traditional fuzzy PID control algorithm, and the settling time is 6.4 s faster than that of the traditional fuzzy PID control. Then, the Levenberg-Marquardt iterative algorithm has a fast convergence speed, and the fitting precision is high. The quintic polynomial fitting results are basically consistent with the sixth-degree polynomial. The fitting accuracy is much better than that of the quadratic polynomial and exponential. Autofocusing requires less than 2 s and is improved by more than double that of the traditional method. The improved area search algorithm can quickly obtain clear images and solve the backlash problem.

Maximum power point tracking of photovoltaic power generation based on improved fuzzy control and conductance increment method

Abstract The National Development and Reform Commission of China announced in 2022 that it would promote the development of carbon peaking and carbon-neutrality. Therefore, gradual improvement in photovoltaic (PV) development technology would gradually show its advantages. Additionally, the development of the PV power generation industry is promoted significantly by the year-on-year increase in PV power generation. This study combines the traditional fuzzy control and incremental conductance methods by comparing the current maximum power point (MPP) intelligence with the traditional control algorithm. Furthermore, it proposes an optimization algorithm to improve the tracking speed of the MPP by using the partition variable step size. An incomplete partial differential equation is added to the judgment condition of the small step size to solve the problem of oscillation near the MPP caused by the ideal differential equation. A simulation model is established in MATLAB®/Simulink® and the method is simulated under specific conditions. The time to reach the first maximum output point is shortened by 0.21 and 0.14 s by analysing the oscillation process data of three simulation cycles of the incremental conductance, fuzzy control and fuzzy conductance methods, respectively. Additionally, the vibration loss caused by environmental mutation is reduced by 6.5% and 3.1%, respectively. The improved incremental conductance method utilizes the simulation results to optimize the problem of steady-state oscillation and improve the efficiency of the PV power generation. The feasibility and effectiveness of the improved incremental conductance method are verified by comparing the traditional control algorithm with the improved incremental conductance method.

Game Algorithm of Intelligent Driving Vehicle Based on Left-Turn Scene of Crossroad Traffic Flow.

Vehicle networking and autonomous driving are hot areas of scientific research today, and they complement each other and play an important role in people's intelligent travel. Intelligent driving vehicle can enhance road safety, effectively reduce traffic flow and fuel consumption, and promote the overall social development. It has great application value in urban traffic system. The traffic condition of a city directly affects the economic development of the city and the improvement of people's quality of life. As the “core” of the urban traffic network, intersections are the frequent places where traffic jams occur. Game theory, as a win-win theory, mainly solves the problem of multiperson and multi-objective with contradictory objective functions and can be used to study the optimal signal control strategy. Aiming at this problem, the potential conflict behaviors of intelligent driving vehicles when turning left at urban intersections are analyzed and a decision model is established. A long-term trajectory prediction model of straight vehicles is established based on the Gaussian process regression model (GPR) considering the vehicle motion pattern. Combined with trajectory prediction, a decision-making process (model) for intelligent driving vehicles based on conflict resolution and a multifactor driving action selection method are proposed. A coordination algorithm based on game theory is designed for conflicting vehicles. The proposed algorithm is verified by the self-developed intelligent vehicle hardware simulation platform. The simulation results show that the PID method based on digital identification and positioning makes the intelligent vehicle obtain good system step response, can improve the disturbance tracking ability of intersection turning analysis, meet the requirements of turning control system, and reduce the complexity and randomness of parameter design, which is better than the traditional fuzzy control method.

Design and implementation of Adaptive Mixed Fuzzy Controller for MIMO nonlinear systems

The controller techniques for the Multiple-Input/Multiple-Output process is complex because of the difference in process dynamics that arise as a variation in process operating condition and the performance of non-linear dynamic coupling. In this work evolves real coded Genetic Algorithm tuned Adaptive Mixed Fuzzy Controller (AMFC) for Two Cylindrical Tank Interacting Level System (TCTILS). First, Adaptive Traditional Fuzzy Controller (ATFC) is developed and its output response is control action for every individual tank. Secondly, to reduce the coupling dynamics, a suitable Adaptive Coupling Fuzzy Controller (ACFC) is included into ATFC to reduce the coupling dynamic effect between each DOF. The real coded Genetic Algorithm (RGA) is used to compute the optimal scale factors for ATFC and ACFC. The simulation performance shows that the RGA tuned AMFC control scheme understands the proper dynamic behaviour of the system, a complete output response with minimum interaction and good disturbance rejection of external load.

Performance enhancement and power management strategy of an autonomous hybrid fuel cell/wind power system based on adaptive neuro fuzzy inference system

In this paper, a hybrid wind/fuel cell generation system which can be used for loads in remote areas as a micro grid application is considered. This micro grid mainly includes fuel cell (FC), wind generator as electrical power suppliers, resistive-inductive impedance as static load, induction motor (IM) as a dynamic load, DC/AC converter and water electrolizer for supplying hydrogen gas. The Fuel cell is used to compensate the decrease in the power generated by wind, which leads to an increase in the system efficiency. Furthermore, an adaptive control model and achievement refinements of a micro-grid using Adaptive Neuro Fuzzy Inference System (ANFIS) controller has been utilized to regulate the load voltage and frequency. This suggested microgrid system is achieved so that the wind generation unit supplies the loads, while any additional energy needed by the loads will be offset by the fuel cell generator unit. Thus, the main objective of this work is to apply an adaptive control method for improving the proposed electrical micro grid performance. In addition, the performance of the considered system is compared with the proposed ANFIS control when applying the traditional fuzzy control. The outcomes also demonstrated a better reaction and durability to the chosen control model. The MATLAB/SIMULINK programming software tools have been used for carrying out case studies towards the evaluation and validation of the methodology developed in this work with applications. The proposed solution achieved improvement in transient performance. However, the settling time is decreased to 21% in the case of using the suggested ANFIS controller comparing with conventional fuzzy control.

A neural-network based variable universe fuzzy control method for power and axial power distribution control of large pressurized water reactors

This paper proposes a neural network-based variable universe fuzzy controller (NN-VUFC) for power and axial power distribution control of large pressurized water reactors (PWRs). First, a two-node reactor kinetics model is introduced, and the fuzzy inference rules are formulated for the reactor power and axial power distribution control according to their deviations from corresponding setpoint values. Then, based on the idea of variable universe, the scaling factor of the input fuzzy universe is designed to maximize the utilization of fuzzy rules. Finally, a neural network is designed to optimize the proportionality coefficient of the scaling factor online to further improve the fuzzy control performance. Simulation results of the CPR1000 reactor under typical transient conditions show that the NN-VUFC can realize good control of both the reactor power and axial power distribution, and its control performance is effectively improved compared to traditional fuzzy controllers.