Fuzzy Neural Network Control Algorithm Research Articles

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.

Research and Design of Intelligent Greenhouse Control System Based on AIoT Fusion Technology

This article designs the entire intelligent Greenhouse system architecture, collects a large amount of data such as temperature, humidity, light, and water through Multi-sensors, and implements an improved fuzzy neural network control algorithm. The advantage of this algorithm over traditional neural networks is that it can express fuzzy and qualitative the knowledge,which also improves the overall learning ability of the system. This intelligent system combines advanced technologies such as AIoT, NB-IoT, and 5G and uploads sensor data to the cloud platform. Through this improved learning algorithm, it provides optimized temperature, humidity and lighting conditions for plants in the greenhouse, and optimal growth conditions. In addition, this system design also optimizes NB-IoT power management, which can improve the overall system information transmission rate and decoding efficiency, and better achieve connection between the 5G and NB-IoT in order to link up each operations and for transmissions. That design and research of the intelligent greenhouse control system has improved the function and the value of the traditional greenhouse control system. Through the AIoT fusion technology, the entire intelligent greenhouse system is made more convenient, intelligent and integrated.

Fuzzy neural network control algorithm for asymmetric building structure with active tuned mass damper

Asymmetric structures experience torsional effects when subjected to seismic excitation. The resulting rotation will further aggravate the damage of the structure. A mathematical model is developed to study the translation and rotation response of the structure during seismic excitation. The motion equations of the structures which cover the translation and rotation are obtained by the theoretical derivations and calculations. Through the simulated computation, the translation and rotation response of the structure with the uncontrolled system, the tuned mass damper control system, and active tuned mass damper control system using linear quadratic regulator algorithm are compared to verify the effectiveness of the proposed active control system. In addition, the linear quadratic regulator and fuzzy neural network algorithm are used to the active tuned mass damper control system as a contrast group to study the response of the structure with different active control method. It can be concluded that the structure response has a significant reduction by using active tuned mass damper control system. Furthermore, it can be also found that fuzzy neural network algorithm can replace the linear quadratic regulator algorithm in an active control system. Because fuzzy neural network algorithm can control the process on an uncertain mathematical model, it has more potential in practical applications than the linear quadratic regulator control method.

Research on Robot Motion Control Based on Variable Structure Fuzzy Neural Network Based on T-S Model

The motion robot system is a nonlinear, strong coupling, non-integrity constraint control system, and its motion control has always been a hot topic in the control field. Aiming at the problem that the fuzzy neural network controller has too large computational complexity and poor anti-interference ability to the outside world, the paper proposes a fuzzy neural network control algorithm for TS, which reduces the computational complexity of the neural network and makes the closed-loop system of the robot more stable. The simulation experiment proves that the fuzzy neural network algorithm based on T-S makes the controller more resistant to external disturbances, and can maintain a high level of control even in harsh environments.

Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms

Through an analysis of the kinematics and dynamics relations between the target positioning of manipulator joint angles of an apple-picking robot, the sliding-mode control (SMC) method is introduced into robot servo control according to the characteristics of servo control. However, the biggest problem of the sliding-mode variable structure control is chattering, and the speed, inertia, acceleration, switching surface, and other factors are also considered when approaching the sliding die surface. Meanwhile, neural network has the characteristics of approaching non-linear function and not depending on the mechanism model of the system. Therefore, the fuzzy neural network control algorithm can effectively solve the chattering problem caused by the variable structure of the sliding mode and improve the dynamic and static performances of the control system. The comparison experiment is carried out through the application of the PID algorithm, the sliding mode control algorithm, and the improved fuzzy neural network sliding mode control algorithm on the picking robot system in the laboratory environment. The result verified that the intelligent algorithm can reduce the complexity of parameter adjustments and improve the control accuracy to a certain extent.

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

A robust adaptive fuzzy neural network control (RAFNNC) algorithm is proposed based on a generalized dynamic fuzzy neural network (GDFNN), proportion-integral-differential (PID), and improved bacterial foraging optimization (BFO) algorithm, for heading the control of the unmanned marine vehicle (UMV) in the presence of a complex environment disturbance. First, the inverse dynamic model of the motion control of UMV is established based on the GDFNN for the uncertain disturbance caused by the complex environment disturbance. Then, the adaptive rate of the fuzzy neural network is designed based on the error between the real UMV heading angle and designed reference heading angle, so as to further adjust the weight parameter of the GDFNN, and then, the output control value of the neural network is obtained. In order to further reduce the computation amount and computation time of the RAFNNC, the parameters of the PID control algorithm were optimized in advance by using the improved BFO algorithm. The fractal dimension step size and the intelligent probe operation are integrated into the BFO algorithm, in order to optimize the operation time and accuracy of the algorithm. Stability of the designed RAFNNC algorithm for the heading control of the UMV in the presence of complex marine environment disturbance is proved by the Lyapunov stability theory, and the effectiveness and accuracy of the control algorithm proposed are verified by semi-physical simulation experiment carried out in our laboratory.

Research on optimisation and application of improved fuzzy neural network control algorithm

Conventional neural fuzzy design techniques address only the parameter identification, and the structure of network is established according to the knowledge of experience. Therefore, a kind of self-organising fuzzy neural network FNN is proposed based on neural network and the advantages of FNN. First, the architecture of this proposed network is investigated, then the parameters of this new scheme are optimised by intelligent computing. At last, two experiments are done to test the performance of this new scheme. Through the analysis of experiment result, it can be seen that the proposed scheme is better than commonly used network.

Application of Fuzzy Neural Network in the Intelligent Control of Biomass Gasifier

In view of biomass gasify process which has nonlinear, non-minimum- phase, big delay and strong load interference characteristics, a fuzzy neural network control algorithm for biomass gasifier is presented. Gasified process of biomass gasifier has been researched, and intelligent control of temperature and primary air flow for biomass gasifier has been designed. The control objects are gasifier temperature and flue gas oxygen content , the regulation objects are the material feed flow and primary air flow. Simulation results show that, this system has better control effect than the traditional fuzzy control system.

An Intelligent Cruise Controller for High-Speed Train Operation Based on Fuzzy Neural Network Theory

An intelligent control strategy is proposed in this paper, which is applied to the high-speed train ATO (Automatic Train Operation) system in the cruise condition. The dynamics of a high-speed train is discussed based on a typical single-point-mass model and the force analysis in cruise state is studied. A fuzzy neural network control algorithm is incorporated into the ATO system aiming at improving the velocity and position tracking performance in the cruise operation of high-speed train. This control scheme adjusts the parameters of membership functions on-line and does not rely on the precise system parameters such as resistance coefficients which are very difficult to measure in practice. The numerical simulation verifies the effectiveness of this fuzzy neural network algorithm.

Tracking Control of Underactuated Unmanned Surface Vessels Based on the Dynamic Fuzzy Neural Network

This paper proposes a dynamic fuzzy neural network (DFNN) for the tracking control of the underactuated unmanned surface vessels. The dynamic fuzzy neural network control algorithm has the advantages of both fuzzy logical and neural network. The algorithm adjusts the structure and parameters on line at the same time to make the tracking effect of the system being fast and accurate, while it doesn’t need confirm the fuzzy rules and the nodes of hidden layer. The simulation experiments based on the proposed control algorithm are carried out and the simulation results validate its effectiveness.

Steering Control System for Autonomous Road Vehicle Using Fuzzy Neural Network.

This paper describes a new steering control system for an autonomous road vehicle using the fuzzy neural network algorithm. Most of conventional fuzzy controls are represented by linguistic rules based on an expert's or engineer's control knowledge for a system. But the expert's knowledge is not so clear that it is difficult to represent the characteristics of the control system by these linguistic rules perfectly.So, this paper proposes a fuzzy neural network control algorithm which includes both the advantages of the fuzzy logic and the neural network to overcome the disadvantages of them. The FCM (Fuzzy C-means Method) clustering algorithm is applied to create the optimal control rules with data measured in off-line control mode. These rules are learned by the error back propagation learning algorithm.A computer simulator is developed to compare the performance of the proposed fuzzy neural network with those of the conventional fuzzy logic and the CMAC (Cerebellar Model Articulation Controller) neural network for steering control of the autonomous road vehicle. The proposed FNNC is found to be superior in all aspects by computer simulations.