Fuzzy Neural Network PID Control Research Articles

Research on Pneumatic Control of a Pressurized Self-Elevating Mat for an Offshore Wind Power Installation Platform.

Efficient deep-water offshore wind power installation platforms with a pressurized self-elevating mat are a new type of equipment used for installing offshore wind turbines. However, the unstable internal pressure of the pressurized self-elevating mat can cause serious harm to the platform. This paper studies the pneumatic control system of the self-elevating mat to improve the precision of its pressure control. According to the pneumatic control system structure of the self-elevating mat, the pneumatic model of the self-elevating mat is established, and a conventional PID controller and fuzzy PID controller are designed and established. It can be seen via Simulink simulation that the fuzzy PID controller has a smaller adjustment time and overshoot, but its anti-interference ability is relatively weak. The membership degree and fuzzy rules of the fuzzy PID controller are optimized using a neural network algorithm, and a fuzzy neural network PID controller based on BP neural network optimization is proposed. The simulation results show that the overshoot of the optimized controller is reduced by 9.71% and the stability time is reduced by 68.9% compared with the fuzzy PID. Finally, the experiment verifies that the fuzzy neural network PID controller has a faster response speed and smaller overshoot, which improves the pressure control accuracy and robustness of the self-elevating mat and provides a scientific basis for the engineering applications of the self-elevating mat.

Research and Design of Improved Wild Horse Optimizer-Optimized Fuzzy Neural Network PID Control Strategy for EC Regulation of Cotton Field Water and Fertilizer Systems

Xinjiang is the largest cotton-producing region in China, but it faces a severe shortage of water resources. According to relevant studies, the cotton yield does not significantly decrease under appropriate limited water conditions. Therefore, this paper proposes a water and fertilizer integrated control system to achieve water and fertilizer conservation in the process of cotton field cultivation. This paper designs a fuzzy neural network Proportional–Integral–Derivative controller based on the improved Wild Horse Optimizer to address the water and fertilizer integrated control system’s time-varying, lag, and non-linear characteristics. The controller precisely controls fertilizer electrical conductivity (EC) by optimizing parameters through an improved Wild Horse Optimizer for the initial weights from the normalization layer to the output layer, the initial center values of membership functions, and the initial base width of membership functions in the fuzzy neural network. The performance of the controller is validated through MATLAB simulation and experimental tests. The results indicate that, compared with conventional PID controllers and fuzzy PID controllers, this controller exhibits excellent control accuracy and robustness, effectively achieving precise fertilization.

Research on Two-Stage Semi-Active ISD Suspension Based on Improved Fuzzy Neural Network PID Control.

To better improve the ride comfort and handling stability of vehicles, a new two-stage ISD semi-active suspension structure is designed, which consists of the three elements, including an adjustable damper, spring, and inerter. Meanwhile, a new semi-active ISD suspension control strategy is proposed based on this structure. Firstly, the fuzzy neural network's initial parameters are optimized using the grey wolf optimization algorithm. Then, the fuzzy neural network with the optimal parameters is adjusted to the PID parameters. Finally, a 1/4 2-degree-of-freedom ISD semi-active suspension model is constructed in Matlab/Simulink, and the dynamics simulation is carried out for the three schemes using PID control, fuzzy neural network PID control, and improved fuzzy neural network PID control, respectively. The results show that compared with adopting PID control and fuzzy neural network PID control strategy, the vehicle body acceleration and tire dynamic loads are significantly reduced after using the grey wolf optimized fuzzy neural network PID control strategy, which shows that the control strategy proposed in this paper can significantly improve the vehicle smoothness and the stability of the handling.

Fuzzy neural network PID control design of camellia fruit vibration picking manipulator

Due to the growth characteristics of the flowers and fruits of camellia in the same period, the vibrating camellia fruit picking machine needs to ensure the constant rotational speed of the vibrating hydraulic motor when the picking mechanism is operating, to achieve a constant vibration frequency, to ensure that the camellia fruit can smoothly fall off the branches through vibration. In contrast, the camellia fruit does not fall off. In this regard, this paper deduced the state space equation of the camellia fruit picking machine’s valve-controlled vibrating hydraulic motor system and designed a fuzzy wavelet neural network PID controller (FWNN PID controller) based on the traditional incremental PID control principle. Then the designed vibration picking manipulator control system was simulated under no-load, 5 s load conditions, and load start conditions with MATLAB/Simulink, a general PID controller and a fuzzy RBF neural network PID controller (FRBFNN PID controller) were used to contrast with it. The results show that the general PID controller has a slow response speed and poor robustness, while fuzzy neural network PID controllers (including FWNN PID controller and FRBFNN PID controller) have a fast response speed and strong robustness, which can well meet the requirements of a specific vibration frequency. Finally, a field test was carried out. The results show that the FWNN PID control is better than the FRBFNN PID control. Furthermore, the FWNN PID controller obviously reduced the drop rate of camellia flowers within 6% while ensuring the picking efficiency above 90%, which can well meet the needs of the camellia fruit picking operation.

Fuzzy Neural Network PID Control Used in Individual Blade Control

In order to further reduce the vibration level of helicopters, the active vibration control technology of helicopters has been extensively studied. Among them, individual blade control (IBC) independently applies high-order harmonics to each blade with an actuator, which can improve the aerodynamic environment of the blade and effectively reduce the vibration load of the hub. The rotor structural dynamics model based on the Hamilton energy variation principle and the medium deformation beam theory were established firstly, and the aerodynamic model based on the dynamic inflow model and the Leishman–Beddoes unsteady aerodynamic model were also established. The structural finite element method and the direct numerical integration method were used to calculate the vibration response of the rotor to determine the vibration load of the hub. After these, the steepest descent-golden section combinatorial optimization algorithm was used to find the optimization parameters of IBC. Based on this, the input parameters of fuzzy neural network PID control were determined, and the rotor hub vibration load control simulation was conducted. Under the effect of IBC, the vibration loads of the hub could be reduced by about 60%. The article gives the best control laws of individual harmonic pitch control and their combinations. These results can theoretically be applied to the design of control law to reduce helicopter vibration loads.

PID control of evaporation temperature control system based on fuzzy RBF neural network

In this thesis, a fuzzy RBF neural network PID (RBFfnn-pid) control method is designed for temperature deviation concerning the steam curing temperature control system in the steam curing kettle of the aerated concrete block production line. The temperature control system (TCS) models of steam curing are established respectively under the control of fuzzy PID and RBFfnn-pid. Then, the control effect is analyzed by simulation in different situations. The results show that, under this condition of step signal inputting, the RBFfnn-pid control method shortens the rising time by 160 s and reduces the overshoot by 8% compared with the fuzzy PID controlling means. When the system is in a stable state, then the interference signal is added, which in contrast to the controlling means of fuzzy PID, the time for adjusting the RBFfnn-pid control to the set value is shortened by 70 seconds and there is no overshoot. The RBFfnn-pid control method enables the controlling parameters of the steaming temperature control system to be adjusted to the optimum quickly and iteratively, which improves the temperature control accuracy.

Research on Robotic Compliance Control for Ultrasonic Strengthening of Aviation Blade Surface

In order to satisfy the requirement of the automatic ultrasonic strengthening of an aviation blade surface, this paper puts forward a robotic compliance control strategy of contact force for ultrasonic surface strengthening. By building the force/position control method for robotic ultrasonic surface strengthening., the compliant output of the contact force is achieved by using the robot’s end-effector (compliant force control device). Based on the control model of the end-effector obtained from experimental determination, a fuzzy neural network PID control is used to optimize the compliance control system, which improves the adjustment accuracy and tracking performance of the system. An experimental platform is built to verify the effectiveness and feasibility of the compliance control strategy for the robotic ultrasonic strengthening of an aviation blade surface. The results demonstrate that the proposed method maintains the compliant contact between the ultrasonic strengthening tool and the blade surface under multi-impact and vibration conditions.

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.

BP fuzzy neural network PID based constant tension control of traction winch

As part of the ocean winch, the traction winch mainly plays the role of attenuating the load tension. It generally uses simple speed control. For the ship-based collection and release system, complex sea conditions make the cable tension constantly changes. The traditional control method will make the cable in the traction winch too slack or too tight. Meanwhile, it can result in the wrong rope, bite rope and slippage, and other phenomena. Eventually, it is leading to cable damage or breakage and equipment loss. Moreover, with the introduction of expensive photoelectric composite cables, there is a greater need for a good control algorithm to extend the life of the cable and ensure the safety of retrieval and release. Therefore, this paper focuses on the constant tension control of the traction winch cable. In this paper, the NARX neural network-based cable tension compensation value prediction model is established as the feedforward part based on historical ship heave displacement data and cable tension compensation data as training data. On this basis, the simulation model of cable constant tension winch based on common PID control, fuzzy PID control, and BP fuzzy neural network PID control are established. By comparing the tension of the cable with the above controller, the results show that the tension control of the cable based on the NARX neural network predicted tension compensation value plus BP fuzzy neural network PID control has high accuracy, fast dynamic response, smooth system, and no overshoot.

Fuzzy Neural Network PID Control of Valve-controlled Hydraulic Motor for Camellia Fruit Picking Machine

Fuzzy Neural Network PID Control of Valve-controlled Hydraulic Motor for Camellia Fruit Picking Machine

Application of Fuzzy Neural Network PID In Laser-guided Mortar Projectile

Based on the high control performance requirement of laser-guided mortar control system, the permanent magnet synchronous motor (PMSM) is adopted in this paper as the electromechanical actuator of the system, the mathematical model of the motor is analyzed, and the vector control technology is adopted to achieve precise control of position, speed and torque of the electromechanical actuator. Aiming at the characteristics of non-linearity, strong coupling and large parameter changes of the system in flight, an improved fuzzy neural network PID control method is proposed by combining the classical PID control algorithm with fuzzy control and neural network control algorithm to realize the real-time tuning and optimization of PID parameters. The mathematical model of the electromechanical actuator control system is established and simulated. The results show that the fuzzy neural network PID control has good tracking performance, small amplitude error, and strong adaptability to load changes.

Comparative study on control effect of permanent magnet synchronous motor based on Fuzzy PID control and BP neural network PID control

Aiming at the problems of low control precision, poor anti-interference ability and poor stability of traditional PID controller, the improvement effect of fuzzy control and BP neural network in closed-loop control of PMSM is compared. In the case of variable speed and variable load, the stability, real-time, anti disturbance and robustness of PMSM system with traditional PID controller, fuzzy self-tuning PID controller and BP neural network PID controller as speed regulator are simulated and analyzed. The simulation results show that the fuzzy self-tuning PID controller has more advantages in the response speed of the system, and can make the system quickly recover to the initial state in case of sudden change. The BP neural network PID controller can ensure the relative stability of the system is better, and can restrain the sudden fluctuation.

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

Research of Oil Pump Control Based On Fuzzy Neural Network PID Algorithm

Abstract In view of the fact that oil pumps exist in the more complex mountains and plains, external temperature, humidity, crude oil characteristics and other adverse factors make the oil pumps control system appear the problem of drift, Fuzzy neural network PID algorithm is studied and designed to realize controling the oil pump system in this paper, combined with the advantages of conventional PID control, fuzzy control and neural network control, the composition and structure of the oil pump control system are analyzed, the model structure and algorithm implementation of fuzzy neural network PID control algorithm are designed in detail. The simulation results show that the control algorithm has good adaptability and anti-interference ability.

The Research and Simulation of PID Algorithm Optimization for Concrete Mixing

In order to fix the defects of the inaccurate control parameters in concrete intelligent mixing, this paper proposes a concrete mixing parameter control method based on fuzzy neural network PID control algorithm. The control method mainly includes two parts-control parameter calculation and mixing data feedback. According to the control parameter calculation, the proposed method is capable of calculating the control parameters required to control the concrete mixing. The industrial concrete mixing condition can be feedback to the control end via data feedback method. The control parameters can be adjusted according to the feedback data to accurately control the concrete mixing. The experimental results illustrate the proposed algorithm can effectively increase the control accuracy with satisfactory performance.

Study on Parameters Optimization Method of Fuzzy Neural Network PID Controller

In allusion to the insufficient of the traditional parameters optimization of the traditional fuzzy neural network PID controller, the parallel search characteristics of the ant colony algorithm in the whole parameter space is used. A parameters optimization method of the PID controller based combining the ant colony algorithm and fuzzy theory and neural network is proposed in this paper. The method used the ant colony algorithm to comprehensively optimize the parameters and structure of fuzzy neural network, which to be used to train and determine the parameters of the PID controller in order to get the fuzzy neural network PID controller. This method is used in the practical application of nonlinear coupled system, the experimental results show that the optimized fuzzy neural network PID controller takes the faster approximation control objectives, the shorter response time, the smaller overshoot and higher control accuracy. Consequently, the research has the theoretical significance and practical application value.

Beer Fermentation Temperature Control Curve Optimization Based on the Fuzzy - Neural Network PID Control Algorithm

An auto-control model is presented to the process of beer fermentation, which has the characteristics such as time-varying, inertia, time-delay and nonlinear. The traditional PID control is difficult to accurately control. This paper according to the beer fermentation of problems puts forward a new control algorithm: fuzzy-neural network PID control algorithm. The fuzzy logistic differential control and intelligent integral control were supplemented into the fuzzy set-point weight tuning, so that the insufficiency of original PID control method was effectively improved. The advantages of this control algorithm are not only constitute a simple, small static error, dynamic response speed but also the ability to learn, etc. Therefore it not only can strengthen robust and intelligence of the system, but also make design simple and easily be required.

Design on Fuzzy Neural Network PID Control System of Diesel Engine

The diesel engA control system of diesel engine speed regulation based on the structure of fuzzy neural network is proposed in this paper, which is combined with PID controller in order to regulate PID parameters dynamically. The simulation result proves that the system has good control capability and robust stability, as well as good self-study and self-adaptive ability.

Application Research of Pipe Racking System Based on Improved Fuzzy Neural Network PID Control

According to pipe racking system exist nonlinear characteristics and in order to get smooth velocity of the racking in moving process. This paper structures a new kind of fuzzy neural network PID which identifies the target model and also provides a non-linear relation model for dynamic programming. In addition, by adopting robust feedback controller, the stability of the closed-loop system and satisfactory control results in initial stage of fuzzy neural network learning are also guaranteed. And we analyze the error response curve of sine signal tracking, the experimental results show that the improved fuzzy neural network PID controller has a higher control performance. The control method has fast response speed, less overshoot and error, strong robust and can meet the requirements of the nonlinear system.

The Application Research of PLC Temperature Control System Based on Fuzzy Neural Network PID

it is hard to establish the accurate model by using the traditional PID algorithm, and hard to adjust the system parameter in nonlinear system. In order to solve this problem, this paper proposed PID algorithm based on Fuzzy Neural Network. This algorithm combined PID algorithm, fuzzy control algorithm and neural network algorithm together, and formed one kind intelligent control algorithm. This paper designed and researched this algorithm, and applied in the PLC temperature control system. The experimental result indicated that the fuzzy neural network PID controller improved the controller quality, conquered some question such as variable parameter and nonlinear, and enhanced the systems robustness.