Neural Network PID Research Articles

Design of a BP neural network PID controller for an air suspension system by considering the stiffness of rubber bellows

A rubber bellows has certain creep characteristics, and its role has often been ignored in previous air suspension research. To describe the motion state of a vehicle more accurately during driving, this paper uses amplitude correlation and frequency correlation to describe the mechanical characteristics of the rubber bellows in air spring mechanical characteristics research, establishes an air suspension simulation, and explores the role of the rubber bellows in the suspension. In addition, an adaptive neural network controller is designed to simulate and analyze the Body Acceleration (BA), Sprung Mass Displacement (SMD), Unsprung Mass Displacement (UMD) and Dynamic Wheel Load (DWL) of the suspension. Simulation results show that the rubber bellows has a certain impact on suspension SMD and has a great impact on the performance of the active suspension. The root mean square (RMS) can be increased by 4.55 % but little impact is found on other performance indicators. In addition, compared with the conventional PID control strategy, the adaptive neural network control strategy designed in this paper has better control performance, reducing the RMS of BA and SMD by 27.58 % and 16.67 %, respectively, and improving the control effect by about 4.48 % and 4.17 %, respectively compared with PID. Thus, it can better maintain the stability of the car during driving.

Intelligent Temperature Control of Pressure Gauge Calibration Based on ANT-LION Neural Network

Aiming at the actual industrial requirements of high accuracy and high efficiency in the industrial process of pressure gauge calibration, this paper studies the intelligent temperature control of the pressure gauge calibration system. Based on the physical mechanism of the calibration system, the controlled object model is established. According to the characteristics of large time delay and nonlinearity of the calibration industrial process, a neural network PID control method based on ANT-LION optimization algorithm is proposed. The experimental results show that the method is effective and reliable, which improves the speed and stability of the temperature control of the pressure gauge calibration system, and realizes the intelligent control of the temperature of the calibration system.

Modulation of motor responses and neural activities with transcranial ultrasound stimulation based on closed-loop control

Closed-loop transcranial ultrasound stimulation technology is based on real-time feedback signals, and has the potential for precise regulation of neural activity. In this paper, firstly the local field potential (LFP) and electromyogram (EMG) signals of mice under different intensities of ultrasound stimulation were recorded, then the mathematical model of ultrasound intensity and mouse LFP peak/EMG mean was established offline based on the data, and the closed-loop control system of LFP peak and EMG mean based on PID neural network control algorithm was simulated and built to realize closed-loop control of LFP peak and EMG mean of mice. In addition, using the generalized minimum variance control algorithm, the closed-loop control of theta oscillation power was realized. There was no significant difference between the LFP peak, EMG mean and theta power under closed-loop ultrasound control and the given value, indicating a significant control effect on the LFP peak, EMG mean and theta power of mice. Transcranial ultrasound stimulation based on closed-loop control algorithms provides a direct tool for precise modulation of electrophysiological signals in mice.

Optimal temperature and humidity control for autonomous control system based on PSO‐BP neural networks

AbstractIn order to solve the problems of difficult control, poor stability, and low control precision in complex autonomous non‐linear systems, and some sensors have non‐linear errors in special environments. Based on the PSO (Particle Swarm Optimization) algorithm, an PSO‐BP‐PID (Particle Swarm Optimization Back Propagation neural network PID) control method and a sensor error compensation algorithm based on BP (Back Propagation) neural network are designed for optimal temperature and humidity control and sensor error compensation in the autonomous greenhouse system. The error between the average temperature value and the target value after steady state is 0.5°C, and the error between the average humidity value and the target value is 1% RH. The results show that the control method can effectively compensate the non‐linear error of the sensor and improve the performance of the control system in a complex environment, which is suitable for the stable and control of actuators in autonomous systems. The error of temperature and humidity sensor is compensated by BP neural network; PSO (Particle Swarm Optimization) was used to optimize the BP‐PID parameters of the automatic greenhouse system.

Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures

Low temperatures induce limited charging ability and lifespan in lithium-ion batteries, and may even cause accidents. Therefore, a reliable preheating strategy is needed to address this issue. This study proposes a low-temperature preheating strategy based on neural network PID control, considering temperature increase rate and consistency. In this strategy, electrothermal films are placed between cells for preheating; battery module areas are differentiated according to the convective heat transfer rate; a controller regulates heating power to control the maximum temperature difference during the preheating process; and a co-simulation model is established to verify the proposed warm-up strategy. The numerical calculation results indicate that the battery module can be preheated to the target temperature under different ambient temperatures and control targets. The coupling relationship between the preheating time and the maximum temperature difference during the preheating process is studied and multi-objective optimization is carried out based on the temperature increase rate and thermal uniformity. The optimal preheating strategy is proven to ensure the temperature increase rate and effectively suppress temperature inconsistency of the module during the preheating process. Although preheating time is extended by 17%, the temperature difference remains within the safety threshold, and the maximum temperature difference is reduced by 49.6%.

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

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

Overview of Key Technologies for Torque Ripple Suppression in Four- Wheel In-Wheel Motor Drive Electric Vehicles

Aims and Objectives: The stability control of the four-wheel hub motor-driven electric vehicle is of great significance to the safety of the driver during the driving process, and the torque fluctuation is an important factor that affects the stability control of the vehicle. Therefore, this paper reviews the key technologies and difficulties of torque ripple suppression for in-wheel motor-driven electric vehicles from two aspects: rational design of the in-wheel motor structure to suppress the torque fluctuation of the motor and the in-wheel motor torque control distribution strategy. Methods: Through the analysis of the structural characteristics of the motor, the structural optimization design of the pole slot, cogging, core shape, and magnetic pole shape is used to suppress the torque fluctuation of the motor, and the methods of previous scholars are unified and explained; From the motor control strategy, summed up the scholars in the motor torque ripple suppression method strategy; From the perspective of motor torque control allocation strategy, a series of related strategies are elaborated. The previous researchers proposed the neural network PID electronic differential speed torque comprehensive control strategies, electronic differential control algorithms based on sliding mode control, automotive electronic stability program control algorithms based on hierarchical coordinated control strategies, and other control strategies and algorithms. Results: Based on the theoretical model, the theoretical model is verified and tested through software simulation or test platform. The errors of all simulation and test results in the literature and the theoretical model are within the acceptable range. Conclusion: The theoretical model has been verified on the software simulation or test platform, which proves the feasibility and effectiveness of the theoretical model, thereby suppressing torque fluctuations and improving the stability of the vehicle. Finally, the development direction of the key technology of torque ripple suppression for four-wheel in-wheel motor-driven electric vehicles has been prospected.

PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot

AbstractThe demand for automation using mobile robots has been increased dramatically in the last decade. Nowadays, mobile robots are used for various applications that are not attainable to humans. Omnidirectional mobile robots are one particular type of these mobile robots, which has been the center of attention for their maneuverability and ability to track complex trajectories with ease, unlike their differential type counterparts. However, one of the disadvantages of these robots is their complex dynamical model, which poses several challenges to their control approach. In this work, the modeling of a four-wheeled omnidirectional mobile robot is developed. Moreover, an intelligent Proportional Integral Derivative (PID) neural network control methodology is developed for trajectory tracking tasks, and Particle Swarm Optimization (PSO) algorithm is utilized to find optimized controller's weights. The simulation study is conducted using Simulink and Matlab package, and the results confirmed the accuracy of the proposed intelligent control method to perform trajectory tracking tasks.

Improving Power Quality by DSTATCOM Based DQ Theory with Soft Computing Techniques

The development of non-linear loads at consumers has significantly impacted power supply systems. Since, the poor power quality has been found in the three-phase distribution system due to unbalanced loads, harmonic current, undesired voltage regulation, and extreme reactive power demand. To overcome this issue, Distributed STATicCOMpensator (DSTATCOM) is implemented. DSTATCOM is a shunt-connected Voltage Source Converter (VSC) that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor. DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation. A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply. The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink. The DQ theory is used to produce the reference signal for the Pulse Width Modulation (PWM) generator. In addition, a traditional Proportional Integral Derivative (PID) controller is designed and compared with proposed soft computing approaches such as Fuzzy–PID and Artificial Neural Network (ANN-PID) and compared accurate reference current determination for Direct Current (DC) bus through DC link. An Analytical explores the proposed control strategies given to establish superior outcomes. Finally, total harmonic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.

Improved fuzzy neural network control for the clamping force of Camellia fruit picking manipulator

During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia fruit tree trunk wouldn't loosen or damage, which may affect its later growth, during the picking process. In this regard, this paper derived the state space model of the valve-controlled clamping hydraulic motor system of the push-shaking camellia fruit picking manipulator, and the fuzzy wavelet neural network (FWNN) was designed on the basis of the traditional incremental PID control principle and the parameters of the neural network were optimized by the improved grey wolf optimizer (GWO). And then, the control system was simulated with the MATLAB/Simulink software without and with external interference, and compared and analyzed it with traditional PID controller and fuzzy PID (FPID) controller. The results show that the traditional PID controller and the FPID control have slow response and poor robustness, while the improved fuzzy wavelet neural network PID (IFWNN PID) controller possesses the characteristics of fast response and strong robustness, which can well meet the requirement of the constant clamping force of hydraulic motors. Finally, the field clamping test was carried out on the picking manipulator. The results show that the manipulator controlled by the IFWNN PID controller shortens the clamping time by 20.0% and reduces the clamping damage by 13.6% compared with the PID controller, which is verified that the designed controller can meet the clamping operation requirements of the camellia fruit picking machine.

Design and Simulation of a PID Neural Network Controller for PMDC Motor Speed and Position Control

Direct current (DC) motors have many difficulties when controlling angular velocity in a variety of applications. The perfect controller cannot be carried out by traditional control alone due to the nonlinear properties of DC motors, design constraints, and mechanical variations caused by the operation conditions. This study proposes a design for an artificial neural network based PID controller (ANNPID) to control the speed of a permanent magnet DC motor (PMDC) in two methods. A detailed analysis is performed based on the simulation results of both methods. The proposed controllers are numerically simulated for various test conditions including; set-point changes, step changes in the load torque, and parameter variations, then the suggested techniques were compared in a comparative study with a traditional PID controller based on the transient response specifications and the performance indices to validate the performance of the controllers. The simulation results demonstrated that the controllers have improved dynamics, static performance, and less overshoot. The methods described here achieve control more effectively than the conventional control approaches under both nominal and disturbed test conditions over different operating ranges.

Retracted: Research on wavelet neural network PID control of maglev linear synchronous motor

AbstractRetraction: [Jun Mao, Jun Ma, Research on wavelet neural network PID control of maglev linear synchronous motor, IET Circuits, Devices & Systems 2022 (https://doi.org/10.1049/cds2.12136)].The above article from IET Circuits, Devices & Systems, published online on 8 December 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Harry E. Ruda, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

WITHDRAWN: Missile Trajectory Defense Planning based on Distributed Neural Network

In this article, the development status, models and learning algorithms of distributed neural networks are investigated, conventional BP algorithms are analyzed, some improved algorithms are proposed, and the principle and structure of neural network PID controllers are discussed. In addition, a distributed neural network is used to predict the trajectory of the missile and develop an appropriate attack prevention plan. At the same time, the construction of the virtual world will be introduced by constructing the virtual sky and terrain in the simulation process, changing the perspective in the virtual world, and completing the ballistic counterattack plan of the missile. The purpose of the anti-attack plan is to provide technical reserves for the further development of the National Missile Defense Program, so that the Ministry of Defense can update the technology demonstration system of the existing bases, reduce risks and accelerate technological updates. In order to solve the problem of peculiar or drastic changes in the existing trajectory tracking deviation, we introduced a new method that takes the trajectory arc as the independent variable, and built a linear variable parameter system for trajectory tracking on this basis, and calculated a new automatic Tracking deviation under variable.

Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID

To improve the comfort and smoothness of vehicle driving and reduce the vehicle vibration caused by uneven road surface. In this paper, a new active suspension control strategy is pro-posed by combining a fuzzy neural network and a proportional-integral-derivative (PID) controller, taking body acceleration as the main optimization target and adjusting the parameters of the PID controller in real time. Meanwhile, a fuzzy neural network parameter optimization algorithm combining a particle swarm optimization algorithm and gradient descent method is proposed to realize offline optimization and online fine-tuning of fuzzy neural network parameters. Finally, the active suspension model of a 2-degree-of-freedom 1/4 vehicle is established using MATLAB/Simulink, and the proposed control scheme is verified through simulation studies. The results show that the active suspension system with a particle swarm-optimized fuzzy neural network control method improves the spring mass acceleration, dynamic deflection of suspension, and dynamic tire deformation by 30.4%, 17.8%, and 15.5%, respectively, compared with the passive suspension. In addition, there are also 14.6%, 12.1%, and 11.2% performance improvements, respectively, compared to the PID-controlled active suspension system. These results indicate that the control strategy proposed in this paper can improve the vehicle driving performance and can support the design and development of active suspension systems.

Design and torque control base on neural network PID of a variable stiffness joint for rehabilitation robot.

Variable stiffness joints have been gradually applied in rehabilitation robots because of their intrinsic compliance and greater ability to adjust mechanical stiffness. This paper designs a variable stiffness joint for upper limb rehabilitation training. The joint adopts the variable stiffness principle based special curved surface. The trapezoidal lead screw in the variable stiffness module has a self-locking function, and the stiffness can be maintained without the continuous output torque of the motor. In the aspect of control, back propagation (BP) neural network PID control strategy is used to control the torque of variable stiffness joint. Experiments show that this control method can effectively improve the torque control performance of variable stiffness joints in the case of low stiffness, and the isotonic centripetal resistance training can be realized by using the joints and control methods designed in this paper.

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

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

Swing Steadiness Regulation of Electric Vehicles with Improved Neural Network PID Algorithm

With the intensification of global environmental pollution and the energy crisis, the new energy EV industry is developing rapidly, and FWID-EV is a popular direction for future vehicle development. For the sake of improving the swing regulate steadiness and safety of EV, the study uses a particle swarm algorithm to optimize and improve the BP neural network PID, and designs an EV steering regulator to regulate the transverse swing torque and slip rate of EV to improve the safety and steadiness of EV steering. The research results display that the maximum value of the transverse swing angular velocity of the regulation algorithm is 0.156 rad/s, that the car slip rate is controlled within 0.046, and the steadiness is high, and that the maximum values of the car torque under the double shift line and snake conditions are 100 N-m and 179.4 N-m, respectively, which can effectively avoid the danger caused by steering. This demonstrates that the improved neural network PID regulator can effectively distribute the steering torque of the EV and improve the steering steadiness and safety of the EV while maintaining the driving dynamics. The use of the improved neural network PID algorithm to achieve the steering steadiness regulation of EV is of great significance to improve the safety of new energy EV, and helps to promote the widespread use of new energy EV.

A Study on Trajectory Control System of Hydraulic Excavators Based on Multi-Domain Physical Model*

Hydraulic excavators are complex mechatronics construction machinery with characteristics of multidiscipline intersection and multi-domain close coupling. To analyze the comprehensive property of its trajectory control system, a method of multi-system hybrid modeling and simulation based on MATLAB is proposed and described. In this paper, the multi-domain physical model of a medium-sized hydraulic excavator is established. The considered model is mainly comprised of four other subsystems: a machine system, a hydraulic system, a trajectory control system and a sensor system. Moreover, a fuzzy neural network (FNN) PID strategy is introduced to the trajectory control system to guarantee the accuracy of automatic operation. On the basis of the multi-domain physical model, typical simulation experiments for working patterns were performed to validate the performance of the FNNPID controller. Comparison results demonstrate that the precision and velocity response of the FNNPID controller is better than that of the PID with traditional algorithm. The tracking errors of the boom, the arm, the bucket and the swing are decreased by 3∘, 3.2∘, 5.5∘ and 7.5∘, respectively. Establishment of the multi-domain physical model offers technical means for optimization design and rapid modeling of the complex electromechanical system.

Research and Design of Precision Fertilizer Application Control System Based on PSO-BP-PID Algorithm

China has had the highest fertilizer use rate in the world for years, but today a large number of farmlands still use traditional manual fertilizer application methods, which rely entirely on personal experience and not only cause the waste of fertilizer and water resources but also make the local ecological environment polluted. This paper researches and designs a BP neural network PID controller based on PSO optimization to address the above problems. The PSO algorithm is used to optimize the initial weights of the BP neural network, and then optimize the control parameters of the PID to achieve accurate control of the liquid fertilizer flow. A precision fertilizer control system based on the STM32 microcontroller was also developed, and the performance of this controller was verified in tests. The results showed that compared with the conventional PID controller and BP neural network-based PID controller, this controller had good control accuracy and robustness, the average maximum overshoot was 6.35%, and the average regulation time was 41.17 s; when the fertilizer application flow rate was 0.6 m3/h, the shortest adjustment time is 30.85 s, which achieves the effect of precise fertilizer application.

Adaptive PI and RBFNN PID Current Decoupling Controller for Permanent Magnet Synchronous Motor Drives: Hardware-Validated Results

This study presents an adaptive proportional-integral (PI) and radial basis function neural network proportional-integral-derivative (PID) current control solution for permanent magnet synchronous motor (PMSM) drives. The proposed controller includes four controls: a decoupling term, a PI term, a supervision term, and a radial basis neural network-PID (RBFNN-PID) term. The first control term makes up the nonlinear factors, the second automatically adjusts the control gains, the third guarantees the system stability, and the fourth optimizes the PID parameters to achieve optimal system performance. Unlike off-line tuned PID controllers, the adaptive controller includes an adaptive tuning method for the on-line adjustment of control gain based on the gradient descent strategy. Therefore, it can be adjusted and handle the uncertainty of any system parameters. The program is not only simple and easy to be implemented but also ensures the accuracy and rapidity of the tracking speed. The control system has proven to be asymptotically stable. To verify the theory and application of the algorithm, a comparative experiment between the adaptive PI + RBFNN-PID controller and traditional PI + PID controller was conducted, demonstrating good robustness, which can effectively improve the PMSM. The results confirm that the proposed design achieves excellent control stability (i.e., quicker transient responding and smaller steady state error) in the presence of parametric uncertainty compared to conventional PID methods.