Fuzzy Neural Network Controller Research Articles

Total harmonic distortion reduction based energy harvesting using grid-based three phase system and integral-derivative

Distributed generation (DG) and solar photovoltaic (PV) systems are just two of the many places multilevel inverters have found a home. The total harmonic distortion (THD) of an inverter's output voltage or current is widely regarded as a useful quality indicator. In order to lessen THD while enhancing the performance of the network, the authors of this research presented a unified controller.An integrated controller consisting of PID (Proportional-Integral-Derivative),Fuzzy,and Artificial Neural Network (ANN) controllers is used in the 31-level multilevel inverter to reduce THD in the PV system. The fundamental frequency of the suggested integrated controller is 50 Hz,and the simulation results show that the THD is reduced to 0.94 percent. The proposed controller's performance shown a considerable decrease in 5th order harmonics for the Cascaded H-bridge Multilevel Inverter,as well as an average percentage reduction in line voltage total harmonic distortion of 17.04 compared to the existing approach.

Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.

Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

Nonlinear bridge deflection monitoring and prediction system based on network communication

Abstract In order to study the bridge deflection monitoring and prediction system based on network communication, first, the development status of the bridge deflection monitoring system, overall demand of the system, hardware composition of the system, realization of the system software, and the timely processing and analysis of monitoring data are discussed. Then, the dynamic prediction of the change trend of the external load and the decay information of the structural resistance contained in the deflection monitoring data is carried out, and the prediction function of the external effect of the structure is established at the same time. Finally, a rapid monitoring system specially designed to deal with accidental bridge disasters was developed, and it was installed and applied in the engineering experiment of Chongqing Gaojia Garden Bridge. The deflection data analysis of Gaojia Huayuan Bridge was carried out, and suggestions for the operation and maintenance of the bridge were put forward, and the safety status assessment of the bridge was realized. The results show that the system has strong practicability, real-time monitoring and accuracy. It provides a convenient and accurate way for bridge managers to supervise bridges and formulate specific bridge maintenance plans in a timely manner. During the radio frequency modulation of communication data, harmonic oscillations occur due to the nonlinear characteristics of oscillating data, so it is difficult to improve the wireless ability to modulate and demodulate transmitted data in communications. The traditional method uses neural network fuzzy control distribution estimation harmonic balance algorithm and nonlinear rolling. The performance of dynamic predictive control is poor in quality, harmonic balance, and stability control. An improved harmonic-based communication network is proposed to balance the stability control model of nonlinear communication system, construct nonlinear communication system model, and extract signal and channel characteristics of the communication system. The channel model is designed and the communication network control method is adopted to improve the control algorithm. The simulation results show that the proposed algorithm can be used to improve the stability of nonlinear communication system, reduce the bit error rate, overcome the interference of coherent component in sidelobe, and autocorrelated the impulse response of receiver. The stability of the cumulative output is good, which can overcome the communication error caused by the harmonic oscillation due to the nonlinear characteristics of the oscillating data, and improve the communication quality.

Design of an interval type-2 fuzzy neural network sliding mode robust controller for higher stability of magnetic spacecraft attitude control

This paper designs an interval type-2 fuzzy neural network sliding mode robust controller (IT2FNNSMRC) to improve the stability of the vibrational angle of the orbital plane in magnetic rigid spacecraft attitude control. The control system consists of an interval type-2 fuzzy neural network (IT2FNN) controller, a PD controller, and a robust controller in parallel connection. The IT2FNN controller, as a nonlinear regulator, compensates the nonlinearity of the controlled object; the PD controller, as a feedback controller, ensures the global asymptotic stability of the control system; the robust controller inhibits input load disturbance. The IT2FNN controller hereof has a self-organizing function which enables it to automatically determine the network structure and parameters online. At the stage of IT2FNN structure learning, the standard on rule growth is set according to the incentive intensities of IT2FNN rule premises. A new rule is generated when the incentive intensities of rules are all smaller than a certain threshold; next, a significance index is set for each rule. When the significance index of some rule decays to a certain threshold, the corresponding rule shall be deleted to achieve the goals of optimizing IT2FNN structure and reducing system complexity. At the stage of parameter learning, adaptive adjustment of IT2FNN parameters is made via the sliding mode control theory learning algorithm, and the stabilities of the algorithm and control system are proven using Lyapunov function. Finally, the proposed control scheme is used in the control of a magnetic rigid spacecraft, as compared to three other designed control methods. Simulation results show that IT2FNNSMRC has superior control precision and stability. And the IT2FNN which adopts the proposed learning algorithm can address uncertainty satisfactorily, with higher computational implementability.

Photovoltaic MPPT algorithm based on adaptive particle swarm optimization neural-fuzzy control

Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm.

Model Predictive Controlled IM Drive based on IT2FNN Controller

Abstract In this paper, the predictive torque control (PTC) based induction motor (IM) drive using an interval type-2 fuzzy neural network (IT2FNN) controller in the speed control loop is designed and tested in simulations. The states required for the proposed motor drive are estimated by extended complex Kalman filter (ECKF). The ECKF performs online estimations of stator currents, rotor fluxes, rotor mechanical speed, and rotor resistance. Compared to conventional extended Kalman filter (EKF), which estimates the same states/parameters, the designed ECKF has less computational burden because it does not contain matrix inverse and the matrix dimensions have been reduced. In addition, the rotor resistance estimated by ECKF is updated online to the PTC system. Thus, the performance of the PTC-based IM drive is improved against variations in the rotor resistance, whose value changes with operating conditions such as frequency and temperature. In order to force both the ECKF observer and the proposed IM drive, a challenging scenario containing the wide speed range operation of the IM is designed. Simulation results confirm the performance of the proposed speed-sensorless PTC-based drive that uses an IT2FNN controller in the speed control loop and the estimation performance of the ECKF observer.

Improved fuzzy neural network control for the clamping force of Camellia fruit picking manipulator

During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia fruit tree trunk wouldn't loosen or damage, which may affect its later growth, during the picking process. In this regard, this paper derived the state space model of the valve-controlled clamping hydraulic motor system of the push-shaking camellia fruit picking manipulator, and the fuzzy wavelet neural network (FWNN) was designed on the basis of the traditional incremental PID control principle and the parameters of the neural network were optimized by the improved grey wolf optimizer (GWO). And then, the control system was simulated with the MATLAB/Simulink software without and with external interference, and compared and analyzed it with traditional PID controller and fuzzy PID (FPID) controller. The results show that the traditional PID controller and the FPID control have slow response and poor robustness, while the improved fuzzy wavelet neural network PID (IFWNN PID) controller possesses the characteristics of fast response and strong robustness, which can well meet the requirement of the constant clamping force of hydraulic motors. Finally, the field clamping test was carried out on the picking manipulator. The results show that the manipulator controlled by the IFWNN PID controller shortens the clamping time by 20.0% and reduces the clamping damage by 13.6% compared with the PID controller, which is verified that the designed controller can meet the clamping operation requirements of the camellia fruit picking machine.

Predefined-Time Fuzzy Neural Network Control for Omnidirectional Mobile Robot

In this paper, a fuzzy neural network based predefined-time trajectory tracking control method is proposed for the tracking problem of omnidirectional mobile robots (FM-OMR) with uncertainties. Considering the requirement of tracking error convergence time, a position tracking controller based on predefined-time stability is proposed. Compared with the traditional position tracking control method, the minimum upper bound of the convergence time can be explicitly set. In order to obtain more accurate angular velocity tracking, the inner loop controller combines Type 1 fuzzy neural network (T1FNN) to estimate the uncertainty. In addition, considering the problem of feedback channel noise, a Kalman filter combining velocity and position information is proposed. Finally, the simulation results verify the effectiveness of this method.

Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID

To improve the comfort and smoothness of vehicle driving and reduce the vehicle vibration caused by uneven road surface. In this paper, a new active suspension control strategy is pro-posed by combining a fuzzy neural network and a proportional-integral-derivative (PID) controller, taking body acceleration as the main optimization target and adjusting the parameters of the PID controller in real time. Meanwhile, a fuzzy neural network parameter optimization algorithm combining a particle swarm optimization algorithm and gradient descent method is proposed to realize offline optimization and online fine-tuning of fuzzy neural network parameters. Finally, the active suspension model of a 2-degree-of-freedom 1/4 vehicle is established using MATLAB/Simulink, and the proposed control scheme is verified through simulation studies. The results show that the active suspension system with a particle swarm-optimized fuzzy neural network control method improves the spring mass acceleration, dynamic deflection of suspension, and dynamic tire deformation by 30.4%, 17.8%, and 15.5%, respectively, compared with the passive suspension. In addition, there are also 14.6%, 12.1%, and 11.2% performance improvements, respectively, compared to the PID-controlled active suspension system. These results indicate that the control strategy proposed in this paper can improve the vehicle driving performance and can support the design and development of active suspension systems.

Neural-Observer-Based Terminal Sliding Mode Control: Design and Application

In view of the fact that most devices are nonlinear systems, this article proposes a recurrent probabilistic compensation fuzzy neural network (RPCFNN) control scheme based on global fast terminal sliding mode control (GFTSMC) for a class of nonlinear systems with uncertainties. First, GFTSMC is developed to impose a finite-time convergence feature for the considered systems. Second, a novel RPCFNN framework is designed to further enhance the ability to deal with uncertainty. Due to the added probabilistic estimation and dynamic fuzzy operator, the developed RPCFNN controller possesses superior nonlinearity handling capability. Meanwhile, the stability of RPCFNN is proved by the Lyapunov theory. Finally, active power filter is taken as the representative of nonlinear systems to verify the effectiveness and feasibility of the proposed method. Simulation and experimental results show that the novel RPCFNN controller has good steady-state and dynamic performance and has excellent robustness to deal with uncertain disturbances.

Modeling of Mobility as a Service (MaaS) Collaborative Dispatching System of Railway Passenger Transport Hub Based on Neural Network Algorithm

In order to solve the modeling problem of travel as a service (MaaS) collaborative dispatching system of railway passenger transport hub based on neural network algorithm, meet people’s needs, make up for the lack of high traffic travel pressure, and improve people’s living standards, through 30 random questionnaires, it is found that 28 people think that travel convenience improves their quality of life, and 2 people think that owning a car has little effect on travel convenience. Travel has always been people’s basic living needs. In recent years, with the improvement of people’s living standards, there are more and more urban vehicles. At the same time, the increase of vehicles also directly leads to the increasing pressure of urban traffic travel, and the problems of vehicle emission pollution and traffic congestion are becoming more and more obvious. With the national low-carbon environmental protection policies, green transportation has become the theme of the times. With the continuous development of shared transportation mode and intelligent information technology, the new transportation concept of “Mobility as a Service” based on this technical mode will highly integrate the existing transportation modes and travel services. With the support of MaaS system, people’s travel modes will change greatly. Starting with the MaaS cooperative dispatching system of railway passenger transport hub, a multiparameter fuzzy neural network control system dispatching algorithm is proposed to better help the modeling of MaaS cooperative dispatching system of railway passenger transport hub.

Fuzzy variable impedance-based adaptive neural network control in physical human–robot interaction

This article focus on the problems of trajectory tracking and motion constraint for physical human–robot interaction, and a compliant adaptive control method is proposed for stable and safe physical human–robot interaction during the interaction. First, a fuzzy variable impedance control strategy is given to make the robot to use suitable impedance parameters in different motion states, which can improve positioning accuracy and reduce the interaction force. Second, by transforming the safety interaction constraint into output constraint, a tan-type barrier Lyapunov function is presented to guarantee the safety of human partner in physical human–robot interaction. Third, an adaptive neural network is employed to design the adaptive controller to handle with the dynamic uncertainties and improve the robustness of the system. Finally, simulation results of a 2-degree-of-freedom manipulator are presented to show the effectiveness of the proposed method.

Adaptive Finite-Time Fault-Tolerant Control for Uncertain Flexible Flapping Wings Based on Rigid Finite Element Method

The bionic flapping-wing robotic aircraft is inspired by the flight of birds or insects. This article focuses on the flexible wings of the aircraft, which has great advantages, such as being lightweight, having high flexibility, and offering low energy consumption. However, flexible wings might generate the unexpected deformation and vibration during the flying process. The vibration will degrade the flight performance, even shorten the lifespan of the aircraft. Therefore, designing an effective control method for suppressing vibrations of the flexible wings is significant in practice. The main purpose of this article is to develop an adaptive fault-tolerant control scheme for the flexible wings of the aircraft. Dynamic modeling, control design, and stability verification for the aircraft system are conducted. First, the dynamic model of the flexible flapping-wing aircraft is established by an improved rigid finite element (IRFE) method. Second, a novel adaptive fault-tolerant controller based on the fuzzy neural network (FNN) and nonsingular fast terminal sliding-mode (NFTSM) control scheme are proposed for tracking control and vibration suppression of the flexible wings, while successfully addressing the issues of system uncertainties and actuator failures. Third, the stability of the closed-loop system is analyzed through Lyapunov's direct method. Finally, co-simulations through MapleSim and MATLAB/Simulink are carried out to verify the performance of the proposed controller.

The Brushless DC motor control system Based on neural network fuzzy PID control of power electronics technology

In order to improve The Brushless DC motor control system Based on Particle Swarm Optimization Algorithm with Improved Inertia Weights development of power electronics technology emergence, The brushless director current (DC) motor is a new type of mechatronic motor that has been developed rapidly with the development of power electronics technology, The performance of the brushless DC motor control system, this paper starts with the brushless DC motor to improve the motor structure or use new materials. This paper uses the neural network fuzzy PID control method to carry out the study of the brushless DC motor control system, and chooses the correct control model and control target to solve the influence of the commutation process on the system. Moreover, this paper analyzes and designs the application of model prediction in BLDCM current loop control, and uses this control system to build a simulation model of brushless DC motor control system in MATLAB. The simulation results show that the brushless DC motor control system based on neural network fuzzy PID control can effectively improve the control effect.

Fuzzy Neural Network PID Strategy Based on PSO Optimization for pH Control of Water and Fertilizer Integration

In the process of crop cultivation, the application of a fertilizer solution with appropriate pH value is more conducive to the absorption of nutrients by crops. If the pH of the irrigation water and fertilizer solution is too high, it will not only be detrimental to the absorption of nutrients by the crop, but will also damage the structure of the soil. Therefore, the precise regulation of pH in water and fertilizer solutions is very important for agricultural production and saving water and fertilizer. Firstly, the article investigates the hybrid control of fertilizer and water conditioning systems, then builds a fuzzy preprocessing controller and a neural network proportional–integral–differential controller, and optimizes the neural network parameters by means of an improved particle swarm algorithm. The effectiveness of the controller was verified by comparison with the common proportional–integral–differential control and fuzzy algorithm control for fertilizer control and fuzzy preprocessing neural network control. Simulation experiments for this study were designed through the MATLAB/Simulink simulation platform, and the simulation results show that the algorithm has good tracking and regulation capabilities in the system. Finally, the four control algorithms are experimentally validated under different pH regulations using designed field experiments. The results show that, compared with other control algorithms, the control algorithm in this paper has a smaller overshoot and good stability with a shorter rise time, which can achieve the purpose of better regulating the fertilizer application system.

Development of Intelligent Controlled Microgrid for Power Sharing and Load Shedding

An online trained intelligent voltage controller is developed in this article to improve the transient responses of the voltage and frequency of an islanded microgrid with droop control and achieve fast load shedding. Although the droop control algorithm has been extensively adopted in microgrid, some shortcoming of the droop control, such as poor disturbance rejection and slow transient response, seriously affect the power quality and stability in islanded microgrid. Moreover, when the power consumption of the demand exceeds the generated power of the distributed generators in microgrid, the decreased frequency will lead to the microgrid blackout owing to the slow load shedding characteristics of the droop control. Hence, in this article, an online trained Petri probabilistic wavelet fuzzy neural network (PPWFNN) controller is developed as the voltage controller to replace the traditional proportional--integral controller in a battery energy storage system for an islanded microgrid with droop control to achieve fast load shedding. The network structure and the online learning algorithm of the proposed PPWFNN are detailedly introduced. Finally, some experimental results are provided to validate the effectiveness of the microgrid using the proposed PPWFNN controller for the improvements of the power sharing and load shedding.

A Kind of Electro-hydraulic Servo System Cooperative Control Simulation: An Experimental Research

Background: The single position control or force control of the electro-hydraulic servo system has long been unable to meet the actual needs of production and life. To achieve a good control effect and solve various problems in real life, people no longer only consider a single position control or force control. Objective: The proposed method provides technical support for multi-objective collaborative control of the electro-hydraulic servo system, completes the requirements of multitasking operation, improves the positioning accuracy and response velocity of the electro-hydraulic servo system, and realizes the synergy between the position and force. Methods: A control method of the outer loop control of force as feedforward compensation is adopted to realize the coordinated control of force and position based on position control. The position control part adopts the PID control algorithm to ensure real-time response accuracy and positioning accuracy. The outer force loop uses an adaptive fuzzy neural network control algorithm to reduce vibration and shock caused by system instability and nonlinear factors during the force control process. Results: The control effect of the designed position/force collaborative controller is verified by simulation and experiment. It can effectively reduce the vibration and impact caused by the contact force during the working process of the system, at the same time greatly improving the accuracy of position control and improving the stability of the system. Conclusion:: The designed force/position collaborative control system has a good overall control effect.

Bioinspired Central Pattern Generator and T-S Fuzzy Neural Network-Based Control of a Robotic Manta for Depth and Heading Tracking

Aiming at the difficult problem of motion control of robotic manta with pectoral fin flexible deformation, this paper proposes a control scheme that combines the bioinspired Central Pattern Generator (CPG) and T-S Fuzzy neural network (NN)-based control. An improved CPG drive network is presented for the multi-stage fin structure of the robotic manta. Considering the unknown dynamics and the external environmental disturbances, a sensor-based classic T-S Fuzzy NN controller is designed for heading and depth control. Finally, a pool test demonstrates the effectiveness and robustness of the proposed controller: the robotic manta can track the depth and heading with an error of ±6 cm and ±6°, satisfying accuracy requirements.

Intelligent Controlled DSTATCOM for Power Quality Enhancement

In this study, a three-phase four-wire distribution static compensator (DSTATCOM) is proposed to improve power quality, including the compensation of the three-phase unbalanced grid currents, the total harmonic distortion (THD) reduction of the grid current, and the power factor (PF) correction. Moreover, when different types of loads vary in the power system, the instantaneous power follows into or out of the DC-link capacitor in the DSTATCOM and results in poor transient responses of the grid current and DC-link voltage and performance deterioration. Hence, the DC-link voltage control plays a significant part in the DSTATCOM under load variation. For the purpose of mending the transient responses of the grid currents and DC-link voltage control and the performance of the DSTATCOM, the conventional proportional-integral (PI) controller is substituted with a novel online trained wavelet Takagi-Sugeno-Kang fuzzy neural network (WTSKFNN) controller in this study. Furthermore, the network structure and the online learning method of the proposed WTSKFNN controller are described in detail. Finally, the experimental results are given to certify the feasibility and effectiveness of the DSTATCOM using the proposed WTSKFNN controller for the power quality enhancement and the DC-link control improvement under load variation.