Fuzzy Control Research Articles

Prediction of Power Equipment Emergency Repair Based on Adaptive Neural Network Fuzzy Inference Method

If the emergency repair prediction of power equipment is only made from the perspective of historical spare parts inventory data, it cannot reflect the impact of disaster-causing elements and disaster evolution on the demand for emergency repair spare parts in the future. Therefore, this paper aims to propose a reliable power equipment emergency repair prediction method, and constructs a demand prediction method for emergency repair spare parts of power equipment based on scenario analysis. Constructing a power equipment repair system through intelligent reasoning methods to improve the efficiency of power equipment repair. This article comprehensively uses methods such as literature analysis, model inference, and case simulation verification, this paper innovatively combines the adaptive neural network fuzzy inference system with expert experience. This paper validates the superiority of the prediction method constructed in this paper through comparative analysis. The results show that with the increase of the amount of data, the prediction accuracy of the method proposed in this paper will be improved, which can provide a reference for the subsequent emergency repair prediction of power equipment.

Flood risk assessment of the Narmada Basin, India, under climate change scenarios

ABSTRACT Climate change has led to a significant rise in the occurrence of extreme events such as floods and droughts. It is essential to accurately assess the hazards linked to climate change to effectively tackle flood risks in the future. This study investigates the risk of flooding in the Narmada Basin under climate change scenarios. Future precipitation data of global climate models are used to create intensity–duration–frequency curves and peak flows are estimated using a hydrological model. These peak flows serve as inputs for the hydraulic model to simulate floods across various return periods. The fuzzy inference system (FIS) is employed to optimize releases and reduce peak flows to mitigate flood impacts. The findings indicate that the extent of flooding and maximum water depths increase with higher return periods and future scenarios, and optimizing the reservoir releases, decreases the extent of flooding. This decrease in inundation area ranges from approximately 9 to 10% in the Bargi-to-Indira Sagar section to 20–23% in the Indira Sagar-to-Omkareshwar Sagar segment. In the Omkareshwar Sagar-to-Sardar Sarovar segment, the flooded area diminishes by 17–18% under the Radioactive Concentration Pathway (RCP) 4.5 and by 12–14% under the RCP 8.5 scenarios.

Advanced Prediction Models for Scouring Around Bridge Abutments: A Comparative Study of Empirical and AI Techniques

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

Retraction Note: Fault location measurement of sensor nodes based on fuzzy control algorithm

Retraction Note: Fault location measurement of sensor nodes based on fuzzy control algorithm

Collaborative Optimization of Multi-regional Integrated Energy System Based on Improved Beluga Whale Optimization Algorithm

With the installed capacity of the renewable energy power generation is growing at a high speed, a large number of the renewable energy is connected to the grid, the energy problem has been solved to a large extent, but the ensuing problems can not be ignored, the first is the consumption of the renewable energy, which is followed by the stability problem of the power system, and a multi-regional integrated energy system (MRIES) is constructed to solve the problem. In view of the wind power uncertainty, the absorption treatment is carried out by the equipment layer and the optimization layer respectively. A hybrid energy storage system (HESS) is introduced in the equipment layer to suppress the influence of the wind power uncertainty on the system stability. A combination of the convolutional filtering algorithm, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm and the fuzzy control algorithm is introduced in the optimization layer to develop the charging and discharging strategy of the HESS. With the strategy, the impact of the electric power fluctuations on the power system during the optimization process can be reduced largely. Then, on the basis of fully considering the energy coupling in different equipments, a collaborative optimization model of the MRIES is constructed. And the integrated demand response model considering the time-of-use electric price is introduced in the load side. Finally, the improved Beluga optimization(IBWO) algorithm is developed to optimize the model. The optimization results show that the IBWO algorithm plays a good optimization effect both in the participation of energy supply equipments and the economy, plays a collaborative optimization role in the MRIES and ensures the stability of the whole power system.

Fuzzy Logic-Based Smart Control of Wind Energy Conversion System Using Cascaded Doubly Fed Induction Generator

This paper introduces a robust system designed to effectively manage and enhance the electrical output of a Wind Energy Conversion System (WECS) using a Cascaded Doubly Fed Induction Generator (CDFIG) connected to a power grid. The solution that was investigated is the use of a CDFIG that is based on a variable-speed wind power conversion chain. It comprises the electrical and mechanical connection of two DFIGs through their rotors. The originality of this paper lies in the innovative application of a fuzzy logic controller (FLC) in combination with a CDFIG for a WECS. To demonstrate that this novel configuration enhances control precision and performance in WECSs, we conducted a comparison of three different controllers: a proportional–integral (PI) controller, a fractional PID (FPID) controller, and a fuzzy logic controller (FLC). The results highlight the potential of the proposed system in optimizing power generation and improving overall system stability. It turns out that, according to the first results, the FLC performed optimally in terms of tracking and rejecting disturbances. In terms of peak overshoot for power and torque, the findings indicate that the proposed FLC-based technique (3.8639% and 6.9401%) outperforms that of the FOPID (11.2458% and 10.9654%) and PI controllers (11.4219% and 11.0712%), respectively. These results demonstrate the superior performance of the FLC in reducing overshoot, providing better control stability for both power and torque. In terms of rise time, the findings show that all controllers perform similarly for both power and torque. However, the FLC demonstrates superior performance with a rise time of 0.0016 s for both power and torque, compared to the FOPID (1.9999 s and 1.9999 s) and PI (0.0250 s and 0.0247 s) controllers. This highlights the FLC’s enhanced responsiveness in controlling power and torque. In terms of settling time, all three controllers have almost the same performance of 1.9999. An examination of total harmonic distortion (THD) was also employed to validate the superiority of the FLC. In terms of power quality, the findings prove that a WECS based on an FLC (0.93%) has a smaller total harmonic distortion (THD) compared to that of the FOPID (1.21%) and PI (1.51%) controllers. This system solves the problem by removing the requirement for sliding ring–brush contact. Through the utilization of the MATLAB/Simulink environment, the effectiveness of this control and energy management approach was evaluated, thereby demonstrating its capacity to fulfill the objectives that were set.

Relaxed Event Triggered Control of Discrete‐Time Takagi‐Sugeno Systems Based on Homogeneous Polynomial Method

ABSTRACTThis article presents an event triggered control approach for discrete‐time T‐S fuzzy systems, which integrates the homogeneous polynomial method and a dynamic event triggering mechanism. First, a kind of discrete‐time loop functions and the dynamic event triggering scheme based on periodic sampling are constructed to establish stability conditions with known system information. Second, by introducing the homogeneous polynomial method to relax the stability criteria, the sampling interval is increased and the system conservatism is reduced. Additionally, an integrated framework for collaboratively designing the event triggering functions and controllers is proposed, which reduces the complexity of sampling transmission while ensuring system stability. Finally, numerical and practical examples are used to illustrate the feasibility and effectiveness of the proposed method.

Online Three-Dimensional Fuzzy Reinforcement Learning Modeling for Nonlinear Distributed Parameter Systems

Distributed parameter systems (DPSs) frequently appear in industrial manufacturing processes, with complex characteristics such as time–space coupling, nonlinearity, infinite dimension, uncertainty and so on, which is full of challenges to the modeling of the system. At present, most DPS modeling methods are offline. When the internal parameters or external environment of DPS change, the offline model is incapable of accurately representing the dynamic attributes of the real system. Establishing an online model for DPS that accurately reflects the real-time dynamics of the system is very important. In this paper, the idea of reinforcement learning is creatively integrated into the three-dimensional (3D) fuzzy model and a reinforcement learning-based 3D fuzzy modeling method is proposed. The agent improves the strategy by continuously interacting with the environment, so that the 3D fuzzy model can adaptively establish the online model from scratch. Specifically, this paper combines the deterministic strategy gradient reinforcement learning algorithm based on an actor critic framework with a 3D fuzzy system. The actor function and critic function are represented by two 3D fuzzy systems and the critic function and actor function are updated alternately. The critic function uses a TD (0) target and is updated via the semi-gradient method; the actor function is updated by using the chain derivation rule on the behavior value function and the actor function is the established DPS online model. Since DPS modeling is a continuous problem, this paper proposes a TD (0) target based on average reward, which can effectively realize online modeling. The suggested methodology is implemented on a three-zone rapid thermal chemical vapor deposition reactor system and the simulation results demonstrate the efficacy of the methodology.

Analysis and design of wire winding tension control system based on genetic algorithm and fuzzy PID

Wire winding is a key process in transformer manufacturing, and the accuracy of tension control directly affects the quality of the products. To achieve the requirement of constant tension winding of wire, the influence of roll diameter, speed, and other factors on the dynamic performance of tension is analyzed by establishing the dynamic model of unwinding and winding as well as the pendulum part. A control strategy combining proportional-integral-derivative (PID) control with fuzzy control is adopted, and the parameters of the fuzzy PID controller are optimized using an improved genetic algorithm. Simulation results demonstrate that the optimized fuzzy PID controller significantly reduces tension overshoot and effectively suppresses the tension fluctuation when the speed and roll diameter change, and the tension fluctuation is controlled within 2%. Finally, the feasibility of the optimized fuzzy PID controller in the tension control system is experimentally validated.

Decomposition prediction fractional-order active disturbance rejection control deep Q network for generation control of integrated energy systems

The number of distributed renewable energy sources of integrated energy systems (IESs) are increasing. The volatility of renewable energy exacerbates active power imbalances and increase frequency deviations in power systems. This work proposes a decomposition prediction fractional-order active disturbance rejection control deep Q network (DPFOADRCDQN) to control each generator of IESs more accurately and to reduce systemic frequency fluctuation. According to the fluctuation size of frequency deviation, the sub-signal obtained after the prediction of the modal decomposition of frequency deviation is classified into big fluctuating signals and small fluctuating signals; fractional-order active disturbance rejection control (FOADRC) controls small fluctuating signals; deep Q network (DQN) controls big fluctuating signals. Frequency deviation signals in complex power systems with a large percentage of renewable energy have higher smoothness after modal decomposition, FOADRC can efficiently follow the small fluctuating signals, and DQN can learn the rich data features in the big fluctuating signals and give precise control commands. The DPFOADRCDQN, proportional-integral-derivative, Q-learning, fuzzy control, and the previous state-of-the-art reinforcement learning are simulated in IESs where the renewable energy generators output is 0 %, 20 %, 40 %, 65 %, and 80 % of all generators output, the results show the average frequency deviation, total generation cost, and carbon emission cost are at least 42.7 %, 0.92 %, and 0.93 % smaller than the comparison algorithm, respectively.

A Fuzzy Adaptive PID Coordination Control Strategy Based on Particle Swarm Optimization for Auxiliary Power Unit

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively.

Research on anti-swing control system of slewing crane based on fuzzy PID.

The slewing crane is easily affected by the wind and the manipulation level of the operator when it is working, which in turn impacts its swing angle, affects the working efficiency and safety of the crane. Toimprove the operation safety andreduce the swing angle, this study design and research the anti-swing control system for the slewing crane. Firstly, based on the Lagrange equation, the dynamic model of the crane hoisting system is established. Then, the mechanical anti-swing mechanism driven by a four-motor rope is established. Finally, based on fuzzy PID control, an anti-swing platform is established, and the performance of anti-swing control is verified. Compared to no anti-swing control, the in-plane swing angle θ1 and out-of-plane swing angle θ2 of the lifting arm are reduced by 88% and 75% respectively. The results show that the four-motor rope-driven anti-swing mechanism can achieve better anti-swing effect, which provides an effective swing suppression method for the slewing crane in the actual operation.

Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

ABSTRACT Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input–output patterns and provide highly accurate estimation results.

Distributed Improved RILOS Guidance-Based Formation Control of Underactuated ASVs for Cooperative Maritime Search

A distributed improved robust integral line-of-sight (RILOS) guidance-based sliding mode controller is designed for multiple underactuated autonomous surface vessels (ASVs) to perform cooperative maritime search operations. First, a parallel circle search pattern is designed based on the detection range of ASVs, which can provide the reference formation shape. Second, an improved RILOS method is presented by introducing an integral term into the improved robust LOS method, which can counteract the disadvantageous effect of the unknown sideslip angle and kinematic discrepancy simultaneously. Third, distributed improved RILOS guidance is presented by integrating the extended second-order consensus algorithm into the improved RILOS method; then, the desired heading angle and desired velocity are generated for the control system simultaneously. Finally, the fuzzy logic system is integrated into the sliding mode control (SMC) method to approximate the unknown nonlinear function; then, a distributed improved RILOS guidance-based SMC controller is presented for multiple ASVs. The close-loop signals are proved to be stable by the Lyapunov theory. The effectiveness of the presented method is verified by multiple simulations.

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.

Enhanced Looped Lyapunov Functional for Sampled-Data Control for T-S Fuzzy Systems with Time Delay

This paper addresses the problem of sampled-data control in T-S fuzzy systems with time delays. Initially, a negative-definite criterion is developed for the matrix containing membership functions with the help of the idea of a switching mechanism. Building upon this criterion, a membership-function-based looped Lyapunov functional is introduced. Furthermore, a sampling-time-dependent looped Lyapunov functional is constructed, incorporating crucial information about the sampling instance. Notably, this functional matrix is not constrained to satisfy positive-definiteness, and it varies with each sampling point. By utilizing the enhanced looped Lyapunov functional, less conservative stability conditions and a new controller criterion for the T-S fuzzy system with time delays are derived. This demonstrates that our proposed stability conditions allow for a larger sampling interval compared to existing work. Finally, the proposed method’s effectiveness is demonstrated through the validation of two simulation examples.

Fuzzy adaptive asynchronous sampled-data consensus control for nonlinear multi-agent systems under DoS attacks

This article investigates the problem of fuzzy adaptive resilient asynchronous sampled-data consensus control (SDCC) for a class of uncertain nonlinear strict-feedback multi-agent systems (MASs) under denial-of-service (DoS) attacks and with a directed graph. Firstly, the upper bounds on the sampling intervals and a distributed sampled-data observer are proposed to estimate the leader's output and its high-order derivatives under DoS attacks. Then, the sufficient conditions to ensure the asymptotic convergence of the designed distributed sampled-data observer are established. Based on the distributed sampled-data observer and backstepping control design technique, a distributed fuzzy adaptive resilient control algorithm is developed. It is proved that the proposed control scheme can guarantee that the controlled MASs are stable, and the consensus errors converge to a small neighbourhood of zero. Finally, a simulation example on heterogeneous vehicular platoons verifies the feasibility of the proposed control scheme.

Event-triggered fuzzy adaptive control for a class of MIMO discrete-time nonlinear systems with uncertain control gains

ABSTRACT An event-triggered output control technique is devised for the output anticipated trajectory tracking controller design problem for a class of unknown multiple-input-multiple-output (MIMO) discrete-time nonlinear systems. A parameterized state observer is established to estimate the non-measurable states accurately by utilizing the filter states and the gradient-based fuzzy parameter updated laws. A series of fuzzy filters are proposed to evaluate the immeasurable states, and a filter state observer is constructed to overcome the state estimation that is triggered by the unknown control gains and the coupling of control input. One non-zero parameter is added to a fuzzy logic system (FLS) to reduce the computed burden of the controller with several updating laws. A FLS with one non-zero parameter is introduced to reduce the calculated burden of the controller with fewer updating laws. The proposed method reduces the computed burden and communication consumption while realizing the immeasurable state estimations, guaranteeing the ultimately uniformly bounded (UUB) of the closed-loop system, and achieving a good tracking impact. Finally, simulation analyses are used as a numerical example to confirm that the proposed control strategy is valid.

Retraction Note: Investigation of different MPPT techniques based on fuzzy logic controller for multilevel DC link inverter to solve the partial shading

Retraction Note: Investigation of different MPPT techniques based on fuzzy logic controller for multilevel DC link inverter to solve the partial shading

SAPFIS: a parallel fuzzy inference system for air combat situation assessment

SAPFIS: a parallel fuzzy inference system for air combat situation assessment