Fuzzy Control Research Articles

Adaptive fuzzy PI control for the dynamic operation of the transcritical CO2 thermal system in electric vehicles

Process control is essential for ensuring the stable and efficient operation of EVs’ transcritical CO2 thermal management systems. Traditional PI control generally shows an insufficient dynamic response for the variable and complex operating conditions and then causes fluctuant process control. This study proposed an adaptive fuzzy PI control strategy to provide real-time varying PI gains for fast-tracking response and fluctuation suppression, considering the compressor discharge pressure, coolant, and air outlet temperature. A high-fidelity transcritical CO2 thermal model in EVs was first built in the Amesim platform to simulate real-time operation, and the adaptive fuzzy PI control algorithm was then developed to optimize the process control. The co-simulation is conducted to validate the effectiveness and adaptability of the proposed control methodology by comparing it with a benchmark traditional PI control strategy. The results indicated that the proposed adaptive fuzzy PI could provide a 9.22 % overshoot suppression for discharge pressure, a maximal 22.29 % overshoot inhibition and 86 s settling time reduction for the air outlet temperature, and a 25 % settling time reduction for the coolant temperature control. In addition, the fuzzy PI improved the start-up characteristics by avoiding excessive response. Such findings demonstrate that the proposed fuzzy PI control algorithm can effectively optimize the dynamic operation of the transcritical CO2 thermal system in EVs.

Novel Controller Methodology for the Cessna Citation X Under Turbulence During Cruise

The design of a new control approach for the longitudinal motion of the Cessna Citation X during cruise is performed using a combination of sliding mode control (SMC) system, type 1 fuzzy logic system, and adaptive control system. This methodology is presented for 1) controlling the aircraft pitch rate and 2) stabilizing the aircraft speed during turbulence. The nonlinear model of the aircraft was generated using a simulation platform, which was designed based on flight data obtained from the highest Federal Aviation Administration–certified Level-D Research Aircraft Flight Simulator. The type 1 adaptive fuzzy logic system was implemented to approximate unknown functions for constructing the equivalent part of the SMC system that handled the effects of uncertainties and turbulence. The adaptation laws, derived from the Lyapunov theorem, were used to update the approximated functions in the control law at each flight condition and simulation iteration. Using the control systems combination, the pitch rate could follow the given reference signal, while the aircraft speed remained at a reference value with and without turbulence across the whole flight envelope. Results have shown that the proposed controllers satisfied tracking performance while generating smooth elevator deflection, both of which are important for real-aircraft applications.

Thermal environment optimization and process product design of CNC grinding machine based on improved neural network

In the machining process of CNC grinding machine, the non-uniformity of heat source often leads to the deformation of the workpiece, the intensification of wear and the machining error, so the optimization of thermal energy environment has become an important research direction to improve the machining performance. The aim of this study is to optimize the thermal energy environment of CNC grinding machine by improving the neural network algorithm, so as to achieve the goal of improving the machining precision and surface quality. We hope that by establishing thermal environment model, we can deeply analyze the characteristics of thermal energy distribution and put forward effective optimization measures. The improved neural network model is used to model the heat energy distribution of CNC grinding machine. The accuracy of the model is improved by introducing multi-layer perceptron and convolutional neural network. At the same time, combined with the experimental data, training and testing are carried out to verify the effectiveness of the model. Genetic algorithm and fuzzy control are used to adjust the thermal energy environment and obtain the best process parameters. The experimental results show that the prediction accuracy of the improved neural network model is significantly improved, and the prediction error of the model is significantly reduced compared with the traditional method. After thermal optimization, the surface roughness of the workpiece is reduced, the dimensional accuracy is significantly improved, and the overall processing efficiency is significantly improved. By improving the neural network model, the thermal energy environment of CNC grinding machine is effectively optimized, and the machining precision and surface quality are successfully improved.

Intelligent Generating Controller a Desflurane Concentration Value Which Helps to Decrease Blood Pressure.

The aim of the article is to determine the appropriate concentration of desflurane to effectively counteract the increase in blood pressure resulting from surgical stress. In medical practice, this increase is often limited by using additional doses of opioid drugs. Additional medications or higher doses of those already used may adversely affect your health. During anesthesia, physician must note the use of drugs and remember them, especially those that he has recently administered, which affect his concentration. For this purpose, the authors decided to propose support for the selection of desflurane concentration so that frequent use of opioid drugs is not necessary. The authors used a system based on AI issues to accomplish this task. The learned system supports the anesthesiologist's work by imitating him. The proposed method for selecting the desflurane concentration is based on a fuzzy controller. This system includes a learning mechanism that allows for minimizing the operating error. The main advantage of this system is the ability to build a function allowing the selection of anesthesia parameters without knowledge of the mathematical description of the process. To accomplish this task, you need an expert who will provide information in the construction of logical if-then sentences (points in space). The fuzzy controller connects the points in the consideration space appropriately, generating a hypersurface. The algorithm test was performed only by computer without the participation of patients. The operation of the proposed algorithm was verified by computer simulation. The authors of the article analyzed the compliance of the obtained results with the table provided by the expert. The desflurane concentration values obtained by computer simulation are similar to those given in the table Minimal driver error does not affect the patient's clinical response. This error results from the functions used in the fuzzy system and its settings. The results of the performance test of the proposed algorithm are presented in a time course, and it has the shape of a step function. The work proposes a function that allows you to enter the time needed for the body's reaction to reach the desired Etdes level. In this study, a controller was created to support the selection of the concentration of desflurane allowing for a reduction in blood pressure (resulting from surgical stress). The results obtained by computer simulation provide valuable insights for optimizing anesthesia. This system can also be used as an important simulation program for teaching purposes.

Input-to-state stabilization of discrete-time delayed fuzzy systems via aperiodically intermittent control

This paper studies input-to-state stabilization of delayed discrete-time Takagi–Sugeno (T-S) fuzzy systems via aperiodically intermittent control. We first consider aperiodically intermittent time-triggered control, where we present sufficient conditions via the mathematical induction under the hypotheses of the quasiperiodicity condition. Based on the derived sufficient conditions, we apply a Lyapunov-Krasovskii (L-K) method together with the descriptor method to derive the explicit linear matrix inequalities (LMIs) that ensure the exponential stability and input-to-state stability (ISS), and show the existence of the aperiodically intermittent time-triggered controller that leads to efficient results with much less numerical complexity. We next consider aperiodically intermittent dynamic event-triggered control with an additional parameter that is larger than one. This strategy allows that the introduced dynamical variable does not remain constant but increases during the control rest interval. As a result, the proposed dynamic event-triggered strategy leads to a smaller number of sent signals than that for the case of the additional parameter which equals to one. Finally, numerical examples including a practical inverted pendulum on a cart are presented to verify the validity of the proposed method.

Classification of high-resolution remote sensing images based on interval type-2 fuzzy logic system

Abstract Aiming at the complexity and uncertainty of remote sensing image classification, this paper proposes a high-resolution remote sensing image classification method based on Interval type-2 Fuzzy Logic Systems for Remote Sensing (IT2FLS-RS), which focuses on dealing with multiple uncertainties and aims to improve the accuracy and reliability of remote sensing image classification. The method establishes a more complex interval two-type fuzzy system covering two types of fuzzifiers, a rule base and an inference machine, and finally uses an integrated algorithm as a defuzzifier to achieve accurate pixel-level classification. In addition, the model uses Constrained Optimization BY Linear Approximations (COBYLA) to optimise the key parameters. In the DLRSD dataset, the accuracy of this model is improved by about 20%, 14%, 24% and 10% compared to the state-of-the-art interval two-type fuzzy neural network algorithm and the benchmark model XGBoost, respectively. On the WHDLD dataset, the accuracy of the proposed method is improved by about 12% and 10% compared to the state-of-the-art interval type-2 fuzzy neural network algorithm and the benchmark model XGBoost, respectively. The experimental results confirm the robustness of the proposed method in processing high-resolution remote sensing image classification, especially the excellent adaptability and scalability in complex feature scenes.

Local Fuzzy-Basis-Dependent Stabilization of Interval Type-2 T–S Fuzzy Systems via an Improved Adaptive Memory-Based Event-Triggered Mechanism

Local Fuzzy-Basis-Dependent Stabilization of Interval Type-2 T–S Fuzzy Systems via an Improved Adaptive Memory-Based Event-Triggered Mechanism

Fully Connected Neural Network-Based Fixed-Time Adaptive Sliding Mode Control for Fuzzy Semi-Markov System

Fully Connected Neural Network-Based Fixed-Time Adaptive Sliding Mode Control for Fuzzy Semi-Markov System

Event-triggered sliding mode control for networked T–S fuzzy delayed systems against random injection attacks

This paper investigates the event-triggered sliding mode control (SMC) problem for networked T–S fuzzy delayed systems subject to random injection attacks, where the event-triggered mechanism (ETM) is introduced to alleviate data congestion and improve network efficiency. Under an open network environment, malicious attackers may randomly inject false information into the controller–actuator channel to compromise the integrity of the data. A new fuzzy sliding mode controller is constructed based on a non-parallel distributed compensation strategy, which introduces the distribution information of the attack probability. Then, the membership function dependent (MFD) analysis technique is adopted to reconstruct the membership functions in order to reduce the conservatism caused by the mismatch between the membership functions of fuzzy model and controller. Furthermore, the co-design conditions for the ETM and the fuzzy sliding mode controller are provided, which can ensure that the resulting closed-loop fuzzy system is mean-square exponentially ultimately bounded (EUB) and the sliding surface is reachable. Finally, a simulation experiment with tunnel diode circuit application is utilized to demonstrate the validity of the proposed SMC scheme.

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

Multilayer decoupling control of photovoltaic integrated heat pump based on expected value

Abstract In this paper, a multilayer decoupling control method for photovoltaic heat pumps based on expected values is proposed, which aims to achieve the optimal operation of the photovoltaic heat pump system and photovoltaic heat pump system. This method predicts the output energy of photovoltaic photovoltaic system and realizes multilayer decoupling control based on the load demand of the heat pump system. In order to adapt to the change in environment, the adaptive fuzzy PID compound control method is adopted to optimize the control strategy. Experimental results show that this method can effectively improve the system efficiency, reduce energy consumption, and demonstrate good adaptability and stability.

Fuzzy adaptive impedance control for the two-layered vertical cable-driven parallel robot

This study unveils a novel two-layered vertical octahedron cable-driven parallel robot (TLVO CDPR), distinctively engineered for effective force interactions with vertical surfaces while preventing collision with cables. It pioneers an innovative control strategy integrating a position-based fuzzy adaptive impedance controller with a fuzzy Proportional – Integral – Derivative (PID) controller, adeptly managing both the pose and contact force of the robot. While dual control application is often found in rigid-link robots, it remains a largely unexplored frontier in the realm of CDPRs, despite its critical importance in sectors like manufacturing and assembly. The fuzzy adaptive mechanism significantly boosts impedance control efficacy in the face of unpredictable, non-uniform working surfaces, ensuring algorithmic stability and convergence. Concurrently, fuzzy logic is harnessed to optimize PID controller performance. The forward kinematics challenge is efficiently tackled using a least squares method coupled with an Inertial Measurement Unit (IMU), ensuring swift and precise solutions. The robustness and adaptability of the robot and its control systems are thoroughly validated through extensive experimental trials, involving diverse trajectories and varying uncertainties on vertical working surfaces.

Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites

Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

Exponential Synchronization Control of Reaction-Diffusion Fuzzy Memristive Neural Networks: Hardy-Poincarè Inequality.

This article is devoted to solving the exponential synchronization problem of a new type of fuzzy memristive neural network with reaction-diffusion terms. By introducing adaptive laws, two controllers are designed. After combining the inequality technique with the Lyapunov function approach, some easily verified sufficient conditions are established to ensure the exponential synchronization of the reaction-diffusion fuzzy memristive system under the proposed adaptive scheme. In addition, by using the Hardy-Poincarè inequality, the diffusion terms are estimated associated with the information of the reaction-diffusion coefficients and the regional feature, which improves some existing conclusions. Finally, an illustrative example is presented to demonstrate the validity of the theoretical results.

Hybrid FCMG-OP-FIS model approach to convert regression into classification data for machine learning-based AQI prediction

Air pollution from vehicle emissions, industrial activities, and medical facilities poses significant health risks in urban areas, underscoring the necessity for robust air quality index (AQI) monitoring. This paper presents a novel method for AQI prediction by integrating a fuzzy centre merge graph with an optimal value-based fuzzy inference system (FCMG-OP-FIS) and machine learning (ML). Traditional ML techniques encounter difficulties when converting regression datasets into classification formats, particularly when unable to label the dataset using the traditional method. The proposed FCMG-OP-FIS model efficiently converts regression data into a classification framework. Unlike traditional AQI prediction methods that rely solely on pollutant data, this approach incorporates both pollutant and meteorological data to improve prediction accuracy. The innovative fuzzy centre merge graph (FCMG) balances the dataset for optimal solutions and facilitates input grouping for Simulink, simplifying rule management. The FCMG-OP-FIS model generates a regression output for AQI, which is subsequently classified into levels (healthy, moderate, or unhealthy) using IF-THEN rules. To enhance accuracy further, a random forest classifier (RFC) is trained on the FCMG-OP-FIS classified output data. The regression output of the FCMG-OP-FIS model is validated using metrics such as RMSE (0.48), MSE (0.23), MAE (0.23), and MAPE (1.77%). Additionally, the classification output from the RFC model employs advanced validation techniques including stratified shuffle validation, grid search cross-validation, and confusion matrix analysis, achieving an accuracy rate of 99%, with the F1 score, precision, and recall over all at 99%. These results demonstrate the effectiveness of the proposed model in accurately labelling data for classification and predicting AQI through ML, highlighting its potential for practical application in environmental monitoring and management.

Estimation of Blood Calcium and Potassium Values from ECG Records

Abstract The identification of diseases caused by changes in ion concentration is quite difficult and yet plays a decisive role in the success of clinical care, diagnosis and treatment. The clinically proven approach to diagnosing electrolyte concentration imbalance is blood tests. There is a need to provide a non-invasive diagnostic method that is not of a temporary nature. Bio-signals such as the electrocardiogram (ECG) can be used to meet this demand and become diagnostic tools that facilitate home monitoring of electrolyte concentration on a permanent basis. This study investigates the feasibility and efficiency of methods based on machine learning (ML) and ECG recordings in monitoring critical levels of existing potassium and calcium concentration. Morphological, frequency and frequency-time domain features were extracted to automatically estimate calcium and potassium levels. Furthermore, the potential of estimates based on modeling approaches will be demonstrated to gain insights into relevant clinical findings and improve the performance of monitoring approaches. Using the hold-out validation method, the best results in terms of mean square error (MSE) and R for estimating the calcium value are 0.7157 and 0.57347, using fuzzy inference systems (FIS). Here, R represents the proportion of the variance in the calcium value that is explained by the model.

Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications

As vehicular applications continue to evolve, the computational capabilities of individual vehicles alone are no longer sufficient to meet the increasing demands. This has led to the integration of edge computing in the Internet of Vehicles (IoV) as an essential solution. Due to the limited resources within vehicles, there is often a need to offload tasks to edge nodes. However, task offloading in IoV environments presents several challenges, including high mobility, dynamic network topology, and varying node density. Traditional offloading methods fail to effectively address these challenges. Moreover, tasks differ in importance, necessitating a mechanism for edge nodes to prioritize tasks based on their urgency. To overcome these challenges, we propose a Vehicle-to-Vehicle (V2V) fuzzy-based task offloading scheme. In this scheme, fuzzy logic plays a critical role by enabling dynamic prioritization of tasks based on their urgency and the available computational resources at edge nodes, ensuring intelligent, context-aware decision-making. The user vehicle selects an appropriate edge node using an edge selection mechanism, which guarantees prolonged connection time and sufficient computational resources. Tasks at the edge are then organized based on their latency requirements and evaluated using a fuzzy rule-based inference system. Our simulation results demonstrate improved task execution rates, reduced overall system delay, and minimized queuing delays.

Fuzzy Logic-Based Feedwater Controller for the Super Fast Reactor

Abstract Super fast reactor is a Generation IV reactor with water coolant at supercritical pressure. It has advantages of having high efficiency of about 44%, more economical and excellent safety features. Its last core design which adopted one heating in one coolant cycle, however, results a large density change along the fuel channels. It leads to a sensitive steam temperature change against a change of power which might mismatch with the flow change. The sensitivity should be reduced to maintain the structure integrity of the reactor components. A feedwater controller which based on fuzzy logic was used to reduce the sensitivity. Method of fuzzy-based control is applied. Two signals of the temperature and the power changes were taken as the inputs of the fuzzy controller, and change of the feedwater coolant flow was taken as the control output. Fuzzifications used triangular member functions with Tsukamoto fuzzy model for inferencing process. Two control parameters of member function spread and signal values which lead to fuzzy degree of 1 were optimized so that the criteria of outlet coolant temperature and the stability were fulfilled. Test results of the control system showed that the deviation of the water temperature at outlet side could be kept within the criteria when mismatch change of power and water flow happened. The temperature deviation could be supressed by using the fuzzy-based control.

A novel adaptive parameter zeroing neural network for the synchronization of complex chaotic systems and its field programmable gate array implementation

This article proposes a novel adaptive-parameter zeroing neural network (NAP-ZNN) to control the synchronization of complex chaotic systems. Different from previous zeroing neural networks (ZNNs), the NAP-ZNN model incorporates adaptive-parameter, a unique blend of time-varying convergence factor and fuzzy control parameter, which is adjusted continuously according to the real-time synchronizing error, offering greater flexibility. The NAP-ZNN model aims to address the time-varying problem in complex-valued domain, which is scarcely explored, it rapidly impels two complex chaotic systems to synchronize within predetermined time and effectively mitigates noise interference. Moreover, the upper bound of the convergence time is theoretically calculated in noiseless environments, and the disturbance-resistant property is demonstrated under various noisy disturbances. The control performance of NAP-ZNN model has been separately compared to other general ZNNs (NAP-ZNN synchronizes at 0.097 s while three general ZNNs spend 0.43 s, 0.78 s and 0.84 s, and only NAP-ZNN complete synchronization within 0.1 s under noisy interferences while general ZNNs fail) and the traditional adaptive control method through two synchronization simulation (RMSE of NAP-ZNN under sinusoidal noise is 3.165 × 10-3 while traditional is 3.383 at t = 1 s). The numerical outcomes clearly illustrate the superior performance of the NAP-ZNN model in comparison to other synchronization strategies. The hardware implementation of the NAP-ZNN model has been successfully realized through the Field Programmable Gate Array (FPGA), and the chaos synchronization process has been vividly captured via an oscilloscope, further reinforcing the practicality and effectiveness of the NAP-ZNN model.