Search Results (49)

To address the challenges of low automatic leveling efficiency and insufficient control precision for small tracked operation chassis navigating uneven terrain in hilly and mountainous areas, this study proposes a leveling control system that integrates a dual-layer threshold strategy with a BP neural network algorithm. The system is developed based on a four-point lifting leveling mechanism. Building upon this foundation, the conventional single-threshold angle error compensation control strategy was optimized to meet the specific leveling demands of chassis operating in such complex environments. A co-simulation platform was established using Matlab/Simulink-AMEsim for subsequent simulation and comparative analysis. Simulation results demonstrate that the proposed method achieves a 15.6% improvement in leveling response speed and a 21.3% enhancement in leveling accuracy compared to the classical single-threshold PID control algorithm. Static test results reveal a smooth leveling process devoid of significant overshoot or hysteresis, with the leveling error consistently maintained within 0.5°. Field tests further indicate that at a travel speed of 3 km/h under a 50 kg load, the platform stabilization time is reduced by an average of 1.3 s, while the leveling angle error remains within 0.5°. The proposed system not only improves leveling response speed and precision but also effectively enhances the overall leveling efficiency of the tracked chassis system. Full article

Aiming at the problem of a full-width horizontal-axis roadheader being prone to diverge from the preset trajectory of the tunnel, a deviation correction control method based on particle swarm optimization–backpropagation (PSO-BP) neural network proportional–integral–derivative (PID) control is proposed. The track error model of the walking system and the transfer function model of the deviation correction control are established. The PSO-BP PID controller is designed; the beginning weights of BP are enhanced by the PSO, and the BP receives the optimal weights to instinctively adapt the PID parameters. An experiment on deviation correction control of the roadheader was carried out. The experimental results indicate that the maximum steady-state error of PSO-BP PID for deflection angle and angular velocity is reduced by 41.03% and 44.93%, respectively, compared with BP PID, and the average rise time for deflection angle and angular velocity is reduced by 75.76%. Full article

This study focuses on a blood pump system equipped with four radial active magnetic bearings (RAMBs). The finite element method (FEM) was employed to optimize the physical parameters of the system. Based on this optimization, two intelligent PID tuning strategies—particle swarm optimization (PSO) and backpropagation (BP) neural networks—were compared. First, a differential control model of a single-degree-of-freedom active magnetic bearing was developed, based on the topology and operating principles of the radial magnetic bearings. Then, magnetic circuit parameters were precisely identified through finite element simulation, enabling accurate optimization of the physical model. To enhance control accuracy, intelligent tuning strategies based on PSO and BP neural networks were applied, effectively addressing the limitations of conventional PID controllers, which often rely on empirical tuning and lack precision. Finally, simulation experiments were conducted to evaluate the optimization performance of PSO and BP neural networks in the magnetic bearing control system. The results demonstrate that the improved PSO algorithm offers significant advantages over both the BP neural network and traditional manual PID tuning. Specifically, it achieved a rise time of 0.0049 s, a settling time of 0.0079 s, and a steady-state error of 0.0013 mm. The improved PSO algorithm ensures system stability while delivering faster dynamic response and superior control accuracy. Full article

In order to solve the problems of the poor adaptability to nonlinear systems, cumbersome parameter adjustment, and sensing-execution delay facing PID control for trawl winch tension control on fishing vessels, a prediction model for trawl winch cable tension was developed using a NARX neural network. The network was trained using historical data to achieve the accurate prediction of the trawl winch cable tension value in the future moment. The predicted value of the NARX neural network was introduced into the BP-PID controller as a feedforward quantity, and a BP-PID feedforward control strategy based on the prediction of the NARX neural network was designed. The simulation results in MATLAB software version: 9.13.0 (R2022b) show that, in comparison with the conventional PID control method, the BP-PID feedforward control strategy based on NARX neural network prediction substantially minimizes the fluctuation in trawl winch tension, enhances the control accuracy and robustness, and demonstrates excellent control performance under various sea states and load conditions. Full article

The aim of this study is to solve the problem of it being difficult to obtain quantitative step signals when testing the time constant of thermocouples using the laser excitation method, thereby restricting the accuracy and repeatability of the test of the time constant of thermocouples. This paper designs a thermocouple time constant testing system in which laser power can be adjusted in real time. The thermocouple to be tested and a colorimetric thermometer with a faster response speed are placed on a pair of conjugate focal points of an elliptic mirror. By taking advantage of the aberration-free imaging characteristic of the conjugate focus, the temperature measured by the colorimetric thermometer is taken as the true value on the surface of the thermocouple so as to adjust the output power of the laser in real time, make the output curve of the thermocouple reach a steady state, and calculate the time constant of the thermocouple. This paper simulates and analyzes the effects of adjusting PID parameters using quantum neural networks. By comparing this with the method of optimizing PID parameters with BP neural networks, the superiority of the designed QNN-PID controller is proven. The designed controller was applied to the test system, and the dynamic response curves of the thermocouple reaching equilibrium at the expected temperatures of 800 °C, 900 °C, 1000 °C, 1050 °C, and 1100 °C were obtained. Through calculation, it was obtained that the time constants of the tested thermocouples were all within 150 ms, proving that this system can be used for the time constant test of rapid thermocouples. This also provides a basis for the selection of thermocouples in other subsequent temperature tests. Meanwhile, repeated experiments were conducted on the thermocouple test system at 1000 °C, once again verifying the feasibility of the test system and the repeatability of the experiment. Full article

In the context of the global energy transition, thermal management of electric vehicle batteries faces severe challenges due to temperature rise and energy consumption under dynamic operating conditions. Traditional strategies rely on real-time feedback and suffer from response lag and energy efficiency imbalance. In this study, we propose a neural network-based synergistic optimization method for driving conditions prediction and dynamic thermal management, which collects multi-scenario real-vehicle data (358 60-s condition segments) by naturalistic driving data collection method, extracts four typical conditions (congestion, highway, urban, and suburbia) by combining with K-means clustering, and constructs a BP (backpropagation neural network) model (20 neurons in the input layer and 60 neurons in the output layer) to predict the vehicle speed in the next 60 s. Based on the prediction results, the coupled PID control and temperature feedback mechanism dynamically adjusts the coolant flow rate (maximum reduction of 17.6%), which reduces the maximum temperature of the battery by 3.8 °C, the maximum temperature difference by 0.3 °C, and the standard deviation of temperature fluctuation at ambient temperatures of 25~40 °C is 0.2 °C in AMESim simulation and experimental validation. The results show that the strategy significantly improves battery safety and system economy under complex working conditions by prospectively optimizing heat dissipation and energy consumption, providing an efficient solution for intelligent thermal management. Full article

The electrolytic capacitor-less PMSM drive system presents complex nonlinear characteristics. Since electrolytic capacitor-less systems exhibit low inertia due to the absence of energy storage components, traditional controllers struggle to achieve the dynamic optimization of phase and amplitude margins, resulting in power transmission mismatches that trigger DC bus voltage surges. This severely limits the dynamic response capability and reliable operation of the system across full operating conditions, leading to an insufficient wide-speed-range performance and disturbance rejection. This study investigates the stable operation mechanism under intermittent working conditions by analyzing DC bus voltage transient characteristics. It optimizes control parameters for stable intermittent operations and establishes a neural network-based adaptive controller model. By modeling the correlation between hardware parameters and control parameters in drive systems under frequent start–stop conditions, this research achieves dynamic controllability of the controller during intermittent operations. This approach enhances the computational accuracy of the drive system control model, ultimately improving system-wide operational reliability and adaptability. Experimental validation confirms the effectiveness of this approach, showing significant reliability improvements in capacitor-less variable-frequency speed-control systems. Key innovations include: (1) BP neural network integration for dynamic parameter optimization, (2) impulse voltage suppression through adaptive control matching, and (3) enhanced transient response via machine learning-enhanced speed regulation. The test results demonstrate a 63% reduction in bus voltage fluctuations and 35% improvement in load transition responses compared to conventional PID-based systems, proving the strategy’s practical viability for industrial drive applications. Full article

The research object of this paper is a new type of multi-functional, air-gap-type, vehicle-mounted magnetic suspension flywheel battery. It is a new energy storage technology with a long working life, high energy conversion efficiency, multiple charging and discharging times, low carbon and environmental protection. However, when the vehicle-mounted flywheel battery is operating, it will inevitably be disturbed by road conditions, resulting in loose sensors and feedback errors, thereby reducing the control accuracy and reliability of the system. To solve these problems, a sensorless control system came into being. It samples the current of the magnetic bearing coil through the hardware circuit and converts it into displacement for real-time control, eliminating the risk of sensor failure. However, the control accuracy of the traditional sensorless system is relatively low. Therefore, this paper adopts a BP (backpropagation) neural network PID controller based on genetic algorithm optimization on the basis of the sensorless control system. Through the joint simulation of the dynamic simulation software ADAMS/VIEW2018 and MATLAB2022b, the optimal PID control parameter database for complex road conditions is established. Through sensorless technology, the current of the flywheel battery is converted into the position error for extensive training so that the genetic BP neural network PID controller can accurately identify the current complex road conditions according to the position error, so as to provide the optimal PID control parameters corresponding to the road conditions to carry out accurate real-time stability control of the flywheel rotor. The experimental results show that the method can effectively reduce feedback errors, improve the control accuracy, and output optimal control parameters in real time under complex road conditions, which significantly improves the reliability and control performance of the vehicle flywheel battery system. Full article

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. Full article

Hydrogen energy generation plays a crucial role in enhancing the utilization of clean energy at coastal stations with abundant wind and solar resources in Antarctica. In response to the reliable demand for the application of hydrogen fuel cells in standalone observation systems in Antarctica, in this research, a power supply scheme based on a proton exchange membrane fuel cell (PEMFC) is introduced. Transient models of the PEMFC are developed, and the optimum operational and environmental conditions are determined through experimental investigations conducted at low temperatures. Based on the findings, a PEMFC-based power supply system is designed, encompassing a fuel cell stack, a measurement and control system, and an operation cabin. A temperature-coordinated control system leveraging a BP neural network, fuzzy logic rules, and the fuzzy-based active disturbance rejection control (Fuzzy-ADRC) strategy are proposed to ensure that the temperature of the PEMFC and cabin can reach the optimal state rapidly and that the output voltage is stable. The results indicate that the stack temperature reaches the specified value more rapidly than with PID and ADRC control methods when the current loading and changes in the ambient temperature are considered, and the output voltage oscillation amplitude can be more effectively minimized. This research provides preliminary guidance for a reliable energy supply scheme for PEMFCs, especially in standalone observation systems in coastal locales. Full article

In order to carry out offshore operations smoothly in severe sea conditions, a shipborne Stewart platform for wave compensation is required. Due to the random characteristics of waves, traditional control algorithms cannot accurately compensate for the motion caused by a wave. For the electric shipborne Stewart platform, this paper proposes a backpropagation (BP)-neural-network-based proportional–integral–derivative (PID) control algorithm where the PID parameters are adaptively adjusted by a BP neural network. The control algorithm can improve the robustness and wave compensation precision of the wave compensation system. First, a numerical system model of the shipborne Stewart platform was established according to the classical kinematic model and dynamic model. Then, the BP-PID control algorithm was designed based on the joint space control. In order to reduce the network’s sensitivity to local details and quickly find the global minimum, the gradient descent method with the momentum term is used in the neural network. At last, the availability and rationality of the new method were substantiated through a simulation comparison under various sea conditions. The simulation results indicate that the proposed control method achieves a higher compensation accuracy in three directions under various sea states, compared with traditional PID control algorithm. Under the irregular wave disturbance, the new control method can reduce the position deviation by about 6.56 times compared with a traditional PID control algorithm. The new control algorithm will play an active role in the control of the shipborne Stewart platform. Full article

This study explored the optimization of control systems for atmospheric pipeline air-floating vehicles traveling at ground level by introducing a novel composite wheel-fan system that integrates both wheels and fans. To evaluate the control impedance, the system simulates road conditions like inclines, uneven surfaces, and obstacles by using fixed, random, and high torque settings. The hub motor of the wheel fan is managed through three distinct algorithms: PID, fuzzy PID, and the backpropagation neural network (BP). Each algorithm’s control strategy is outlined, and tracking experiments were conducted across straight, circular, and curved trajectories. Analysis of these experiments supports a hybrid control approach: initiating with fuzzy PID, employing the PID algorithm on straight paths, and utilizing the BP neural network for sinusoidal and circular paths. The adaptive capacity of the BP neural network suggests its potential to eventually supplant the PID algorithm in straight path scenarios over extended testing and operation, ensuring improved control performance. Full article

The transient stability control for disturbances in microgrids based on a lithium-ion battery–supercapacitor hybrid energy storage system (HESS) is a challenging problem, which not only involves needing to maintain stability under a dynamic load and changing external conditions but also involves dealing with the energy exchange between the battery and the supercapacitor, the dynamic change of the charging and discharging process and other factors. This paper focuses on the bus voltage control of HESS under load mutations and system uncertainty disturbances. A BP Neural Network-based Active Disturbance Rejection Controller (BP-ADRC) is proposed within the traditional voltage-current dual-loop control framework, leveraging uncertainty quantification. Firstly, system uncertainties are quantified using system-identification tools based on measurable information. Subsequently, an Extended State Observer (ESO) is designed to estimate the total system disturbance based on the quantified information. Thirdly, an adaptive BP Neural Network-based Active Disturbance Rejection Controller is studied to achieve transient stability control of disturbances. Robust controllers, PID controllers and second-order linear Active Disturbance Rejection Controllers are employed as benchmark strategies to design simulation experiments. Simulation results indicate that, compared to other benchmark strategies, the BP-ADRC controller based on uncertainty quantification exhibits superior tracking and disturbance-rejection performance in transient stability control within microgrids of hybrid energy storage systems. Full article

Large-scale explosive ordnance disposal (EOD) robotic manipulators can replace manual EOD tasks, offering higher efficiency and better safety. This study focuses on the control strategies and response speeds of EOD robotic manipulators. Using Adams to establish the dynamic model of an EOD robotic manipulator and constructing a hydraulic system model in AMEsim, a co-simulation model is integrated. This study proposes a PID control strategy optimized by the particle swarm optimization (PSO) algorithm for a backpropagation (BP) neural network and simulates the system’s step response for analysis. To address the vibration issues arising during the manipulator’s motion, B-spline curves are used for trajectory optimization to reduce vibrations. The PSO algorithm optimizes the connection weight matrix of the BP neural network, solving the potential problem of local minima during the training process of the BP neural network, thereby enhancing the global search capability, learning efficiency, and network performance. Simulation results indicate that compared to traditional BP+PID control, genetic algorithm (GA)+PID control, and whale optimization algorithm (WOA)-BP+PID control, the PSO-BP+PID algorithm control rapidly tunes the PID control parameters K_p, K_i, and K_d. Under the same step function conditions, the overshoot is only 1.37%, significantly lower than other methods, and the settling time is only 14 s. After stabilization, there is almost no error, demonstrating faster response speed, higher control accuracy, and stronger robustness. This research has theoretical value and reference significance for the control methods and improvements in EOD robotic manipulators. Full article

Aiming at the requirements of the marine work platform for real-time control, time-varying speed and the time-varying torque control of the motor-driven active heave compensation device, this paper introduces a composite control strategy based on the switched reluctance motor (SRM)-driven active heave compensation device. As for the compensation error caused by the time lag in the real-time system, the model prediction trajectory algorithm is used to predict the compensation displacement obtained using the dynamic model. The next time, the control parameters are then provided for the SRM control system in advance to reduce the compensation error. The SRM control strategy selects a double closed-loop compound control strategy of Back Propagation (BP) fuzzy neural network Proportion Integration Differentiation (PID) control. Its outer speed loop uses a fuzzy controller to quickly track a wide range of speed changes. The torque inner loop uses BP neural network adaptive PID control. This helps to reduce torque ripple and to ensure that the electromagnetic torque output of the SRM remains stable. Finally, the system feasibility is verified by setting different wave parameters. The simulation results show that the simulation conditions can reach 97.5% and 96.4% under the 3 and 4 wave levels, respectively. The simulation effect is satisfying, which verifies the feasibility of the proposed scheme. Full article