Neural Network PID Research Articles

Mechanical Structure Design and Motion Simulation Analysis of a Lower Limb Exoskeleton Rehabilitation Robot Based on Human-Machine Integration.

Population aging is an inevitable trend in contemporary society, and the application of technologies such as human-machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. The lower limb exoskeleton rehabilitation robot has great potential in areas such as enhancing human physical functions, rehabilitation training, and assisting the elderly and disabled. This paper integrates the structural characteristics of the human lower limb, motion mechanics, and gait features to design a biomimetic exoskeleton structure and proposes a human-machine integrated lower limb exoskeleton rehabilitation robot. Human gait data are collected using the Optitrack optical 3D motion capture system. SolidWorks 3D modeling software Version 2021 is used to create a virtual prototype of the exoskeleton, and kinematic analysis is performed using the standard Denavit-Hartenberg (D-H) parameter method. Kinematic simulations are carried out using the Matlab Robotic Toolbox Version R2018a with the derived D-H parameters. A physical prototype was fabricated and tested to verify the validity of the structural design and gait parameters. A controller based on BP fuzzy neural network PID control is designed to ensure the stability of human walking. By comparing two sets of simulation results, it is shown that the BP fuzzy neural network PID control outperforms the other two control methods in terms of overshoot and settling time. The specific conclusions are as follows: after multiple walking gait tests, the robot's walking process proved to be relatively safe and stable; when using BP fuzzy neural network PID control, there is no significant oscillation, with an overshoot of 5.5% and a settling time of 0.49 s, but the speed was slow, with a walking speed of approximately 0.18 m/s, a stride length of about 32 cm, and a gait cycle duration of approximately 1.8 s. The model proposed in this paper can effectively assist patients in recovering their ability to walk. However, the lower limb exoskeleton rehabilitation robot still faces challenges, such as a slow speed, large size, and heavy weight, which need to be optimized and improved in future research.

Research on the intelligent control strategy of pressurizer pressure in PWRs based on a fuzzy neural network PID controller

Research on the intelligent control strategy of pressurizer pressure in PWRs based on a fuzzy neural network PID controller

Theoretical and experimental study of an automated kill procedure based on a neural network-PID controller

Theoretical and experimental study of an automated kill procedure based on a neural network-PID controller

Automatic Control System for Maize Threshing Concave Clearance Based on Entrainment Loss Monitoring

Complex harvesting environments and varying crop conditions often lead to threshing cylinder blockage and increased entrainment loss in maize grain harvesters. To address these issues, an electric-driven automatic control system for maize threshing concave clearance based on real-time entrainment loss monitoring was developed. The system automatically adjusts concave clearance parameters at different harvesting speeds to maintain grain entrainment loss within an optimal range. First, an adjustable concave structure based on a crank-link mechanism was designed, with a threshing clearance adjustment range of 15–47 mm and motor rotation angle of 0–48°. Subsequently, an EDEM simulation model of the mixed material discharge inside the threshing cylinder was established to determine the optimal installation position of the entrainment loss monitoring sensor based on piezoelectric ceramic-sensitive elements. The sensor was positioned at the left tail end of the concave sieve, with a minimum distance of 58 mm between the sensitive plate centerline and threshing concave sieve and an installation angle of 65° relative to the horizontal plane. A maize threshing clearance control method based on fuzzy neural network PID control algorithm was proposed, and Simulink simulation optimization verified its superior performance with fast response speed. After system integration, field trials were conducted at low, medium, and high operating speeds with preset ideal entrainment loss intervals. The results showed that control was unnecessary at low speed, the control system-maintained entrainment loss within set range at medium speed, and maximum threshing clearance was needed at high speed. Finally, comparative trials of threshing performance with and without the control system were conducted at medium harvesting speed. Results showed that the entrainment loss rate decreased by 43.75% with the control system activated, significantly reducing maize threshing entrainment losses. This study overcame the barrier of maize threshing parameter adjustment being heavily reliant on manual experience and provided theoretical support for the intelligent grain harvesting equipment.

Application of RBF neural network PID control on buck DC-DC converter

Abstract A structure to solve low control accuracy and poor tracking performance in traditional PID controlled DC-DC converters when input voltages and load currents fluctuate has been proposed: combining PID control with Radial Basis Function (RBF) neural networks. To optimize the PID controller’s parameters, this approach makes use of the RBF neural network’s approximation capability for nonlinear functions. Under different test conditions, the neural network takes the output voltage and its deviation from the reference value as inputs. The hidden layer employs Gaussian activation and adjusts by gradient descent. The output layer computes the PID controller’s Proportional (Kp), Integral (Ki), and Derivative (Kd) by the linear combination of hidden layer node outputs. Simulation results using MATLAB/Simulink demonstrate that, under different test conditions, the system employing RBF neural network-PID control exhibits a smaller overshoot compared to traditional PID control systems. Furthermore, in steady-state circumstances, the settling time is shortened by around 1/2 and is decreased by roughly 1/4 in the presence of disturbance signals. These results unequivocally demonstrate that, in the presence of fluctuating test conditions, the new structure of this combination greatly improves the control precision and tracking performance of DC-DC converters.

Research on the Application of Stewart Platform for Wave Compensation

In the field of ocean engineering, the efficiency and safety of ship operation are significantly affected by waves. Faced with the instability and safety risks of ship crane operation under complex sea conditions, a design and research method of efficient wave compensation device based on Stewart platform is proposed in this paper, aiming at developing a compensation system that can respond in real time and effectively counteract the effects of waves, so as to improve the safety and efficiency of offshore operations. Aiming at the limitation of traditional wave compensation technology, this paper adopts the BP neural network PID control algorithm combined with Stewart platform. Utilizing the Stewart platform's six-degree-of-freedom motion capability, fast compensation of wave motion is achieved. On this basis, the BP neural network PID control strategy is introduced to optimize the response speed and compensation accuracy of the system, and realize the efficient and accurate compensation of wave motion under complex sea conditions.

Research on the simulation method of a BP neural network PID control for stellar spectrum.

This study investigated the multiple correlations among spectral simulation units based on digital micromirror device (DMD) spectral simulation, which leads to the problem that conventional spectral simulation methods such as PID control exhibit a low fitting accuracy or long fitting time in the spectral simulation of various targets. In this paper, a method of stellar spectrum simulation based on back propagation neural network-based PID (BP-PID) control is proposed to achieve high efficiency and high precision simulation of various spectral targets. The topology of the BP neural network was constructed based on the spectral modulation model of a DMD stellar spectrum simulation system, and the algorithm of the BP-PID control was designed. Finally, an experimental platform was built to verify the performance and spectral simulation accuracy of the BP-PID control algorithm. The results show that the overshoot and response time of the BP-PID control algorithm decreased by 79.01% and 30%, respectively compared with those of the PID control algorithm. The maximum spectral simulation accuracies of 2000K, 7000K, and 12000K color temperature increased by a factor of 2.311, 1.871, and 2.254, respectively, and the standard deviations of the spectral simulation error decreased by 56%, 41%, and 54%, respectively. In the range of 2000-12000K color temperature, the spectral simulation error of the BP-PID control algorithm is better than ±3.495%, and the standard deviation of the spectral simulation error is between 1.8255 and 2.2358. The proposed method can improve the spectral simulation accuracy and simulation efficiency of a star simulator, reduce the magnitude and spectrum calibration errors caused by the differential response, improve the star feature recognition accuracy of the orbiting star sensor, and hence, provide a theoretical and technical basis for the development of high-precision star sensors.

Modelling And Research on Water Level Control of Great Lakes Based on Neural Network PID Algorithm

The Great Lakes are situated in the border region between the United States and Canada, which has a significant impact on the climate and the lives of those living in the surrounding areas. The objective of this paper is to establish a network of the Great Lakes through Pearson's correlation coefficient analysis and to construct a two-tank water level model based on a PID control system in order to effectively manage the dynamics of the Great Lakes. Firstly, the strength and direction of the linear relationship between two variables is quantified through Pearson's correlation coefficient analysis, which involves the collection of observational data and the calculation of mean values. This analysis serves as a fundamental basis for predictive modelling and hypothesis testing. Secondly, based on the flow balance principle, mathematical expressions are constructed to simulate the water flow, and a PID control system is constructed to achieve optimal water level maintenance. By analysing the Pearson's correlation coefficient, the interrelationships among the variables in the Great Lakes network can be understood, thereby providing guidance for scientific research and decision-making. The results demonstrate that the constructed two-tank water level model combined with the PID control system and SHAP algotithm can effectively manage the water level of the Great Lakes and achieve optimal water level regulation.

Research on Dual-Actuator Shift Control of Dual-Mode Coupling Drive Electric Vehicles

Dual-mode coupling drive system can improve the dynamic performance of electric vehicles through mode switching, and the quality of mode switching directly affects the comfort of drivers and passengers. Mechanical coupling on the left and right sides of the single actuator causes mutual interference during shifting, resulting in prolonged power interruption time and shifting shock. Therefore, this paper analyzes the mutual interference mechanism of single-actuator shifting, designs a new dual actuator, and proposes a staged fuzzy PID controller. Finally, the effectiveness of dual-actuator shifting is proven through simulations and real vehicle testing. Compared with conventional PID control and BP neural network PID control, the shock degree is reduced by 64% and 50%, which improves the mode-switching quality of the dual-mode coupling drive system.

Comparison Between Conventional PID Controller and Neural Network PID Controller Based on DC Motor Speed Control

DC motor is a multivariable, strong coupling and nonlinear controlled system, and is difficult to setting up the accurate mathematical model. So, it cannot achieve good control effect using traditional PID control method. To tackle this problem ANN based PID controller is proposed in this paper. The objective here is to compare the response of DC motor controlled by two different PID controllers.

Structure modification based PID neural network decoupling control for nonlinear multivariable systems

Structure modification based PID neural network decoupling control for nonlinear multivariable systems

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors.

Research on the Application of Neural Network PID in Quadcopter Aircraft

As a highly maneuverable and flexible unmanned aerial vehicle, quadcopters have broad application prospects in both civilian and military fields. However, due to their complex dynamic characteristics, nonlinearity, and strong coupling, achieving stable and precise control of quadcopters is a challenging task. Traditional control methods often fail to meet the control performance requirements for such complex nonlinear systems. Neural networks provide a new approach to solving control problems in complex systems due to their powerful learning and adaptive capabilities. Neural networks can improve the performance of control systems by learning from large amounts of data, capturing the dynamic characteristics of the system, and adjusting control strategies online. Combining neural networks with PID control is expected to fully leverage the advantages of both. Neural networks can adjust the parameters of PID controllers in real-time, enabling them to better adapt to the complex dynamic changes and external disturbances of quadcopter aircraft. This fusion control method brings new possibilities for improving the control performance of quadcopter aircraft. This study explores the application of neural network PID in quadcopter aircraft to achieve stable and precise control, laying the foundation for its widespread promotion in practical applications.

Neural network PID control of spherical permanent magnet based on external magnetic field driving system

Abstract The neural network PID control method of the external magnetic field driving system solves the shortcomings of the traditional control method. The controlled object is a spherical permanent magnet. Other detections or operation equipment can be attached to the sphere. In the process of external magnetic field driving, the model-based controller is prone to large fluctuations when the environmental parameters change, which reduces the control performance. The neural network algorithm has good control ability for the highly nonlinear magnetic levitation driving system. PID control system combined with a neural network algorithm has high stability and strong adaptability in the driving process. The test results show that the response output overshoot is 9.322%, and the system fluctuation is low. Self-tuning and adaptive environment changes of PID controller parameters can be realized by neural network control.

Overview and development of PID control

Proportional integral differential control, referred to as PID control, is one of the earliest developed control methods, which is widely used in industrial process control because of its simple control algorithm, high reliability and good robustness. The classic PID controller is the simplest control system, but the actual control object usually has the characteristics of strong interference, transient uncertainty, nonlinearity, etc, so it is difficult to achieve the ideal control effect with the classic PID controller. In addition, with complex parameter tuning methods, it is often difficult to find an optimal result for the parameters of the classical PID controller. These factors lead to the fact that classical PID control cannot be applied to complex systems and high-performance systems, so people continue to develop PID control methods, introduce new models, and develop fuzzy PID controllers, expert PID controllers, neural network PID controllers, and PID controllers based on genetic algorithms. This paper will review the development history of PID controllers, and introduce fuzzy PID controllers, expert PID controllers, neural network PID controllers, and PID controllers based on genetic algorithms. The research status and future development prospects of these controllers are also prospected.

Adjusting laser power to control the heat generated by nanoparticles at the site of a patient's cells.

Cancer treatment often involves heat therapy, commonly administered alongside chemotherapy and radiation therapy. The authors address the challenges posed by heat treatment methods and introduce effective control techniques. These approaches enable the precise adjustment of laser radiation over time, ensuring the tumour's core temperature attains an acceptable level with a well-defined transient response. In these control strategies, the input is the actual tumour temperature compared to the desired value, while the output governs laser radiation power. Efficient control methods are explored for regulating tumour temperature in the presence of nanoparticles and laser radiation, validated through simulations on a relevant physiological model. Initially, a Proportional-Integral-Derivative (PID) controller serves as the foundational compensator. The PID controller parameters are optimised using a combination of trial and error and the Imperialist Competitive Algorithm (ICA). ICA, known for its swift convergence and reduced computational complexity, proves instrumental in parameter determination. Furthermore, an intelligent controller based on an artificial neural network is integrated with the PID controller and compared against alternative methods. Simulation results underscore the efficacy of the combined neural network-PID controller in achieving precise temperature control. This comprehensive study illuminates promising avenues for enhancing heat therapy's effectiveness in cancer treatment.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Fiber-end control algorithm based on back-propagation neural network

The evenness of the trajectory of the optical fiber grinding track has a substantial effect on the quality of the transmission of optical signals. To boost this transmission quality, we present a blueprint for the trajectory of the optical fiber grinding track, together with the recently introduced equipment for shaping the end-face of the optical fiber. This model establishes the correlation between the grinding trajectory and the rotational speed of the grinding motor, enabling control of the grinding trajectory of the optical fiber connector by manipulating the motor’s rotation speed. Three-speed control methods, namely the Proportional-Integral-Derivative (PID) controller, fuzzy control algorithm, and Back-Propagation (BP) neural network PID control algorithm, were compared for effectiveness using Matlab/Simulink simulation software. The BP neural network displayed exceptional performance, leading us to implement the PID control algorithm to govern the speed of the grinding motor. Our experimental findings validate the superior efficacy of the BP neural network PID control algorithm in governing the speed of the grinding motor, thereby ensuring a consistent grinding trajectory for optical fibers.

Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control

Introduction: Modern industrial manufacturing often requires the eight-bar stamping mechanism to have high motion accuracy and stability. To meet these stringent requirements, traditional control techniques such as proportional-integral-derivative (PID) control need to be improved.Methods: In this study, radial basis function neural network is introduced to improve the traditional proportional integral derivative control technique. The improved proportional integral derivative technique is applied to the modeling and optimization of eight kinds of bar stamping mechanisms.Results: Comparing the improved control technology, the experiment showed that the peak time and adjustment time of the improved technology were 0.516 s and 1.038 s, respectively, which are better than the comparative control technology. In addition, in the comparative analysis of the eight bar stamping mechanism, the proposed architecture scored 9.3 points in operational efficiency, which is significantly greater than the comparative architecture.Discussion: The results show that the combination of PID control strategy and radial basis function neural network provides a powerful tool for dynamic modeling and optimization of eight-bar stamping mechanism. It not only provides enhanced motion accuracy and stability, but also brings significant practicality to industrial manufacturing. This integration opens up new possibilities for improving the performance of complex mechanical systems to meet the evolving needs of modern manufacturing.

Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System.

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of -2.5 m/s2 within a deviation of ±0.1 m/s2, compared with the deviation of 0.11 m/s2 of fuzzy PID and the deviation of 0.13 m/s2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery.