Error Of Control System Research Articles

Application of Discrete Variable-Gain-Based Self-Immunity Control to Flywheel Energy Storage Systems

For the study of the trade-off between steady-state error and transient response in control systems for flywheel energy storage, a controller with a discrete variable gain is proposed. This controller aims to adapt to changes in the system state by dynamically adjusting the controller gain to optimize the system’s anti-disturbance performance. Theoretical analysis and mathematical derivation demonstrate that increasing the observer gain can significantly enhance the system’s anti-disturbance capability. However, this increase also results in overshooting, which highlights the limitations of traditional control methods in achieving both system stability and anti-disturbance performance. A discrete variable-gain extended state observer is designed. The gain of this observer can be adaptively adjusted according to a system state value, enabling the effective control of both steady-state error and transient response. Additionally, the stability of the proposed control method was analyzed and verified, ensuring its effectiveness and reliability for practical applications. Finally, the effectiveness of the proposed method in improving system performance is demonstrated by simulation results.

Design and test of reel speed control system for soybean combine harvester in the strip intercropping mode

At present, the speed of the reel of soybean combine harvester is mainly adjusted manually, and basically does not have the ability to automatically adjust the speed of the reel according to changes in crops or working speed. In the maize-soybean strip intercropping mode, soybean plants are densely planted. During harvesting, the feeding rate increases, making it even more necessary to adjust the reel speed according to the growth status of the crops. This article uses PLC as the main control platform to design a reel speed control system for the existing combine harvester. Tests have shown that the control system can meet the requirements for reel speed control. The relative error of the reel control system’s speed test is 2.6%, the relative error of the operating speed is 6.77%, and the maximum relative error of the automatic control of the reel speed is 5.38%. Compared with traditional reel, it reduces header loss by 2.12%. The soybean combine harvester reel speed control system designed in this article can realize automatic adjustment of the reel speed, reduce feeding losses caused by changes in crop height, changes in feed volume, and untimely adjustment of the reel, and can reduce header loss, increased crop yield.

Comparison between Genetic Algorithms of Proportional–Integral–Derivative and Linear Quadratic Regulator Controllers, and Fuzzy Logic Controllers for Cruise Control System

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero.

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

This work investigates the adaptive optimal tracking control problem for networked discrete-time linear systems by directly using the data transmitted via communication networks. It is shown that the concerned problem can be addressed by solving two sub-problems: an adaptive optimal control one and an adaptive output regulation one. Two novel online reinforcement learning based policy iteration and value iteration algorithms are developed, which constitute an integrated framework to learn the optimal feedback control gain and the solutions to regulator equations by directly using the data transmitted via communication networks. Furthermore, it is shown that the tracking error of the closed-loop control system is mean square asymptotically stable even in the case of network packet stochastic dropouts. Simulation results demonstrate the efficacy of the proposed approaches.

Railway Signal Electronic Control System Based on BP Neural Network

Railway signal system is the core system of railway command train operation. After the installation of the system equipment is completed, a comprehensive test shall be carried out through joint debugging and joint testing, and the applicability of the system to the design requirements shall be generally evaluated according to the test results. This paper focuses on the research of railway signal electronic control system. Based on BP neural network, fault information can be quickly and accurately identified in a relatively short time, and accurate electronic signal control can be realized in a complex system environment. The final result of the study shows that the difference in delay error of railway signal transmission under BP neural network technology is small, there are some subtle differences in 5 different modes, but the impact is small, in mode 1, the target time value is 0.236s, The actual reflection time is 0.322s, it can be seen that the error is controlled within 0.2s, which is feasible. Through the analysis of delay error of railway signal electronic control system, BP neural network has certain advantages in railway signal transmission.

Research on Pump-Controlled AGC Micro-Displacement Position Control of Lithium Battery Pole Strip Mill Based on Friction Compensation Control Strategy of Imoroved LuGre Pattern

Accurate mathematical patterning of friction has always been a significant research project in the domains of machinery and control, and has played a crucial role in the analysis, control and compensation of mechanical systems containing friction. For high-property electrohydraulic servo control systems, friction compensation is an urgent problem to be solved. The LuGre friction pattern can represent most frictional behaviors, but the LuGre friction pattern is piecewise-continuous, making it non-differentiable. Therefore, the question of how to combine the LuGre friction pattern, enhancing its tracking capacity and robust performance problem, to friction perturbation in hydraulic backstep devices is an important focus for research. In this study, the conventional LuGre friction pattern was enhanced using the continuous differentiability of a friction of rest pattern that laid the foundation of a smooth tangent function. Laying the foundation of an electrohydraulic servo pump-controlled hydraulic roll-gap thickness automatic control system (pump-controlled AGC) pattern, a self-adapting friction compensation controller laid the foundation of the enhanced LuGre pattern. The gradual tracking capacity was determined academically using conditions of parameter, uncertainty and nonlinear friction, and the position control precision of the pump-controlled AGC system was enhanced. The steady-state error of the self-adapting friction compensation control system, which laid the foundation of the enhanced LuGre pattern, attained ±0.1 μm, and the tracking capacity was better than the LuGre pattern control and the conventional PID control strategy at low input speed.

Numerical simulation and experimental data blade investigation of horizontal wind turbine

The article presents the results of numerical simulation of a wind turbine blade. The fundamental formulas of the theory of aerodynamics, on which the calculations are based, are presented. Computer models of air movement near the blade are shown. Data on the distribution of speed have been obtained. The coefficient of wind energy utilization depends on the parameters of both the oncoming flow and the blade profile. This coefficient is different for vertical and horizontal wind turbines. To improve the operation of the wind turbine, it is proposed to use non-network algorithms. The neural network should collect data on the received errors in the system of mechanization and automatic control. Errors in control systems are analyzed, individual parameters are weighted to stabilize the operation of the wind turbine as a whole.

Dynamics and Event-Triggered Impulsive Control of a Fractional-Order Epidemic Model with Time Delay

Due to the lack of timely protection measures against infectious diseases, or based on the particularity of the transmission of some infectious diseases and the time-varying connections between people, the transmission dynamics of infectious diseases in the information society are becoming more and more complex and changeable. A fractional-order epidemic mathematical model with network weighting and latency is proposed in this paper, and the stability near the disease-free equilibrium point and endemic equilibrium point are discussed separately. Subsequently, an event-triggered impulsive control strategy based on an infection rate threshold is put forward. By selecting the appropriate control gain, the Zeno phenomenon can be eliminated on the premise of ensuring the stability of the control error system. Finally, the theoretical results were validated numerically and some conclusions are presented. These findings contribute to future research on the limited-time event-triggered impulsive control of infectious diseases.

Comprehensive stiffness regulation on multi-section snake robot with considering the parasite motion and friction effects

Snake robots have been widely used in challenging environments, such as confined spaces. However, most existing snake robots with large length/diameter ratios have low stiffness, and this limits their accuracy and utility. To remedy this, a novel ‘macro-micro’ structure aided by a new comprehensive stiffness regulation strategy is proposed in this paper. This improves the positional accuracy when operating in deep and confined spaces. Subsequently, a comprehensive strategy for regulating the stiffness of the system is then developed, along with a kinetostatic model for error prediction. The internal friction, variation of cable stiffness as a function of tension, and their effects on the structural stiffness of the snake arm under different configurations have been incorporated into the model to increase the modelling accuracy. Finally, the proposed models were validated experimentally on a physical prototype and control system (error: 4.3% and 2.5% for straight and curved configurations, respectively). The improvement in stiffness due to the adjustment of the tension in the driving cables (i.e. average 183.4%) of the snake arm is shown.

An active noise control algorithm based on an improved convex combination loop

The conflict between a faster convergence using a Filtered-X Least Mean Square (FXLMS) algorithm and the requirement of a small steady-state error of an adaptive active control (ANC) system which limits its performance in terms of computation efficiency and the control error. The problem can be solved by using two paralleling adaptive filters to form a convex combination loop in the control system. However, the exponential weight function used in the original convex combination loop design can consume a large computer power which has limited its application. As a result, a new weight function is proposed to replace the original exponential function in the original convex combination loop to enhance the convergence rate of the adaptive ANC system. Furthermore, a MRFXLMS algorithm is used to replace the FXLMS algorithm in the ANC system so that it can be used for impulse noise control. The result shows that the proposed algorithm performs well in the control of input impulse noise with different intensity.

Common errors in machine safety-related control systems and methods of their elimination

The article focuses on the description of non-conformities occurring in safety-related control systems of machines collected by the author during safety audits of machines and production lines. The aim of the article is to familiarise machine designers with safety requirements and ways of eliminating common mistakes made during design, while at the same time indicating to investors buying machines which requirements to take into account when placing an order.

基于PID-模糊控制的单目视觉农田机器人视觉导航系统设计

This study proposes a monocular vision navigation control system based on PID-fuzzy control, which travels along the edge of the path. It collects path image information through monocular vision, identifies the path edge through image processing to determine the preview point, and uses a combination of PID and fuzzy control to design a controller to track the preview point for path navigation. Firstly, coordinate calibration and conversion were performed on the monocular camera, achieving coordinate conversion from the image coordinate system to the world coordinate system. The accuracy of the calibration results was verified through experiments. According to the navigation strategy of driving along the edge of the path, the world coordinate equation of the path edge is obtained through image processing technology, and the preview point tracked by the navigation system is determined. The navigation parameters are determined based on the position of the preview point. The PID fuzzy controller system designed in this study can switch different control methods based on the position of the preview point. Finally, an experimental verification was conducted on the monocular visual navigation system of the control system. The verification results showed that the average error of the navigation control system in tracking the path when driving in a straight line was 0.039 m, the average error when turning left was 0.079 m, and the average error when turning right was 0.121 m. The error range can meet the basic requirements of agricultural robot farmland operations. Research has shown that the navigation strategy based on PID-fuzzy joint controller to track the preview point along the path edge has a good effect on the visual navigation control system of agricultural robots. This study provides important reference value for the research and development of monocular visual navigation systems of agricultural robots.

Five-axis parallel mechanism system (PMS) CNC partial link control system based on modified inverse kinematic of 6-DOF UPS parallel manipulator

This paper presents a control system algorithm for a five-axis parallel mechanism system (PMS) CNC milling machine based on a 6-DOF Stewart platform parallel manipulator with a universal-prismatic-spherical (UPS) configuration. The control system reads the G-Code commands as standard CNC machine language, then extract data points and interpolates them to generate the robot trajectory patterns as motion references. Then, the control system uses the modified inverse kinematic equation to determine the length of each link to move the end effector to track the trajectory patterns from the previous G-code extraction process. The inverse kinematic equation is modified especially for the five-axis PMS CNC milling machine by including machine-offset and tools-offset parameters so it will be easier for the control system to implement the kinematic equation. As expected, the system simulation results successfully followed the G-Code program moving commands. The average error of the length control system is 0,1 mm, while the average error of the length change rate control system is 1,8 mm/s. The maximum error is 26.9 mm was caused by the system's inability to follow the motion profile in transient. It can be concluded that 6-DOF Stewart platform parallel structures,which provide better performance than serial structures, can be implemented as a new concept for the motion mechanism of five-axis CNC milling machines. The five-axis PMS CNC milling machine also promises better performance than conventional five-axis gantry structures CNC.

Multi-motor position synchronization control method based on non-singular fast terminal sliding mode control.

In order to improve the position high-precision synchronization performance of multi-motor synchronous control, a multi-motor position synchronization control method based on non-singular fast terminal sliding mode control (NFTSMC) combined with an improved deviation coupling control structure (Improved Deviation Coupling Control(IDCC), NFTSMC+IDCC). Firstly, this paper designs a sliding mode controller using a non-singular fast terminal sliding mode surface with a Permanent Magnet Synchronous Motor (PMSM) as the control object. Secondly, the deviation coupling is improved to enhance the coupling between multiple motors and achieve position synchronization. Finally, the simulation results show that the total error of multi-motor position synchronization under NFTSMC control is 0.553r in the simulation of multi-motor synchronization control under the same working conditions, which is 2.873r and 1.772r less than that of SMC and FTSMC in terms of speed error, and the anti-disturbance performance is 83.68% and 76.22% higher than that of both of them, respectively. In the subsequent simulation of the improved multi-motor position synchronization structure, the total error of the multi-motor position is in the range of 0.56r-0.58r at three speeds, which is much smaller than the synchronization error under the Ring Coupling Control (RCC) structure and Deviation Coupling Control (DCC) structure, showing a better The synchronization error is much smaller than that of the RCC structure and DCC structure, which shows better position synchronization performance. Therefore, the multi-motor position synchronization control method proposed in this paper has a good position synchronization effect and achieves the control effect of small displacement error and fast convergence of the multi-motor position synchronization control system after being disturbed, the control performance is significantly improved.

Dynamic Model of a Robotic Manipulator with One Degree of Freedom with Friction Component

This research aims to develop a dynamic model of a robotic manipulator with one degree of freedom by incorporating the LuGre friction model. The study combines a mathematical model with experimental data analysis, using the Stribeck curve and Non-linear Least Square method for Parameter Identification. The purpose of the study is to improve the accuracy of the model and enhance the performance of robotic manipulators. The LuGre model is chosen for its ability to capture the nonlinear behavior of friction, which is a significant source of error in robot control systems. The effectiveness of the proposed representation is evaluated by comparing the simulation results of the dynamic model with experimental data obtained from a prototype. The results indicate that the model accurately captures the nonlinear behavior of friction, and the proposed approach can be used to develop more accurate models for control purposes.

МЕТОДИКА ОПРЕДЕЛЕНИЯ ПАРАМЕТРОВ СЛЕДЯЩЕЙ СИСТЕМЫ УПРАВЛЕНИЯ ДВИЖЕНИЕМ РТК ВН В РЕЖИМЕ АВТОНОМНОГО НАВЕДЕНИЯ

The aim of the study is to improve the accuracy of the motion control system of ground-based roboticsystems for military purposes (RTK VN) of tracked type based on the application of the method ofconstructing two-circuit automatic control systems equivalent to combined systems. The use of automaticcontrol systems equivalent to combined systems makes it possible to increase the accuracy of automaticcontrol systems by reducing the value of the dynamic error, that is, achieving error invariance,without violating the stability of the system. The objective of the study is the possibility of achieving zeroerror in single-circuit and double-circuit automatic motion control systems RTK. To solve this problem,it is necessary to determine the structure of the ACS and draw up block diagrams of automatic trafficcontrol systems of the RTK VN along the angle of the course. This task can be solved in stages. Duringthe first stage, the connection of control errors in single-circuit automatic control systems with a constantinput effect is considered. The next stage is the justification of the construction of two-circuit systemstaking into account the linear input effect. Next, it is necessary to determine the parameters of thesecond circuit of the two-circuit ACS by the movement of the RTK. The problem considers the relationshipof the dynamic control error in dual-circuit ACS by the movement of the RTK along the angle of thecourse with the linear input effect. The method used in the article allows us to solve the problem ofachieving the invariance of the error in the ACS by the movement of the RTK VN along the angle of thecourse. The paper presents a methodology for determining the parameters and structure of the ACS inorder to achieve zero error, which, in turn, leads to increased accuracy while meeting the requirementsfor the stability of the system. The calculation results confirm the operability of the proposed methodologyand show that with various input effects (constant and linear) in single-circuit and double-circuitACS, the RTK movement along the course can achieve independence of reducing the dynamic errorfrom the stability of the ACS (i.e., achieving error invariance without loss of stability of the system).

Online sequential fuzzy dropout extreme learning machine compensate for sliding-mode control system errors of uncertain robot manipulator

Online sequential fuzzy dropout extreme learning machine compensate for sliding-mode control system errors of uncertain robot manipulator

Assessing Surface Texture Features of Asphalt Pavement Based on Three-Dimensional Laser Scanning Technology

Pavement surface texture features are one of key factors affecting the skid resistance of pavement. In this study, a set of stable and reliable texture measurement equipment was firstly assembled by using the linear laser ranging sensor, control system and data acquisition system. Secondly, the equipment was calibrated, and the superposition error of sensor and control system was tested by making a standard gauge block. Thirdly, four different kinds of asphalt mixture were designed, and their surface texture features were obtained by leveraging a three-dimensional laser scanner. Therefore, the surface texture features were characterized as one-dimensional profile features and three-dimensional surface features. At the end of this study, a multi-scale texture feature characterization method was proposed. Results demonstrate that the measurement accuracy of the laser scanning system in the x-axis direction can be controlled ranging from −0.01 mm to 0.01 mm, the resolution in the XY plane is 0.05 mm, and the reconstructed surface model of surface texture features can achieve a good visualization effect. They also show that the root mean square deviation of surface profiles of different asphalt pavements fluctuates greatly, which is mainly affected by the nominal particle size of asphalt mixture and the proportion of coarse aggregate, and the non-uniformity of pavement texture distribution makes it difficult to characterize the roughness of asphalt pavement effectively by a single pavement surface profile. This study proposed a texture section method to describe the 3D distribution of road surface texture at different depths. The macrotexture of the road surface gradually changes from sparse to dense starting from the shallow layer. The actual asphalt pavement texture can be characterized by a simplified combination model of “cone + sphere + column”. By calculating the surface area distribution of macro and microtextures of different asphalt pavements, it was concluded that the surface area of asphalt pavement under micro scale is about 1.8–2.2 times of the cutting area, and the surface area of macrotexture is about 1.4 times of the cutting area. Moreover, this study proposed texture distribution density to characterize the roughness of asphalt pavement texture at different scales. The SMA index can represent the macroscopic structure level of different asphalt pavements to a certain extent, and the SMI index can well represent the friction level of different asphalt pavements.

Integrated Optimization of Structure and Control Parameters for the Height Control System of a Vertical Spindle Cotton Picker

Vertical picking method is a predominate method used to harvest cotton crop. However, a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field. Thus, how to realize a precise height control of the cotton picker is a crucial issue to be solved. The objective of this study is to design a height control system to avoid the collision. To design it, the mathematical models are established first. Then a multi-objective optimization model represented by structure parameters and control parameters is proposed to take the pressure of chamber without piston, response time and displacement error of the height control system as the optimization objectives. An integrated optimization approach that combines optimization via simulation, particle swarm optimization and simulated annealing is proposed to solve the model. Simulation and experimental test results show that the proposed integrated optimization approach can not only reduce the pressure of chamber without piston, but also decrease the response time and displacement error of the height control system.