Neural Network PID Controller Research Articles

Face recognition and real-time tracking system based on convolutional neural network and parallel-cascade PID controller

The purpose of this research is to develop a high-efficiency, low-cost, and easy-to-use tracking system for vehicles, and it is expected that the system can be extended to areas such as service robots, autonomous driving, and manufacturing. In this paper, we introduced an object detection algorithm based on convolutional neural networks to realize face recognition, which has better efficiency and robustness than traditional machine learning methods. With the concept of edge computing, we deployed the model on the local embedded system to improve the information transmission and security issues of cloud computing. In order to realize the tracking system, this paper builds a mecanum-wheel vehicle with omnidirectional mobility, and proposes a parallel-cascade PID controller architecture based on the mecanum-wheel vehicle. The fixed distance linear tracking control can be realized through the dual-loop feedback control of distance and yaw angle; moreover, the vehicle slipping which is caused by difference rotation speed can be improved. Finally, through algorithm optimization, controller parameter adjustment, and system integration, an omnidirectional mobile vehicle with recognition and tracking functions is realized. The experiment results indicate that the system is stable and robust during actual operation.

Design of Distributed Drive Control Strategy for Wheel Side Rear Drive Electric Bus Based on Neural Network Algorithm.

The electric bus driven by the wheel-side motor has a natural advantage for the driving stability control of the vehicle because the torque of each driving wheel is independently controllable. Based on the advantages of the artificial neural network control algorithm, this paper designs a direct yaw torque control strategy based on neural network PID control, and combines the steering characteristics of the vehicle to distribute the driving torque. The co-simulation results of Matlab/Simulink and TruckSim show that the designed control strategy can effectively reduce the vehicle’s center of mass slip angle and lateral acceleration under medium and high speed conditions, and ensure the stable driving of the bus.

SUBSTANTIATION OF NEUROCONTROLER PARAMETERS FOR THE CONTROL SYSTEM OF THE VIBRATOR DRIVE OF ADAPTIVE VIBRATION TECHNOLOGICAL MACHINES

The article proposes and analyzes the structure of the neurocontroller for controlling the vibratory drive of adaptive vibrating technological machines (AVTM), which allows to implement a two-circuit neurocontrol system of energy and technological parameters of AVTM that its each circuit takes into account inertial and dissipative characteristics. By monitoring the phase shift between the frequency of forced oscillations of the AVTM working body and the frequency of cyclic forcing force of the vibrating drive and using predictive model neurocontrol technology to correct the frequency of cyclic forcing force of the vibrating drive, the neurocontroller provides mini resonant operation. The second circuit of neurocontrol provides on the resonant frequency tracking and stabilization of the specific work of the vibration field of AVTM during the vibration cycle using to adjust the amplitude of oscillations of the working body hybrid neuro-PID control system with self-tuning based on neuromodel AVTM system. The proposed design of the neurocontroller to control the vibratory drive of adaptive vibrating technological machines can improve the quality characteristics of the control of dynamic parameters of both electromagnetic and unbalanced vibratory drive. The use of neural network technologies in the design solution allows the introduction of neurocontrol algorithms that change the parameters of the process of vibration processing implemented by AVTM, or change the load mass of the working body AVTM, through the use of predictive model neurocontrol based on the simplex method or quasi-Newtonian algorithm that will optimally choose a strategy for correcting the frequency of cyclic forcing force of the vibrator to ensure and maintain a constant resonant mode of AVTM and at the resonant frequency AVTM will stabilize the specific operation of the neural network PID-controller with self-adjustment on the basis of the direct AVTM neuroemulator.

Simulation and Experimental Study of Nonlinear Characteristics for Multi-mode Driving of Intelligent Vehicles

To study the nonlinear characteristics simulation and experiment of multimode driving of intelligent vehicle, an improved neural network PID controller is proposed to realize the safe and active braking requirements of cruise vehicles, according to the study of vehicle dynamics of different active braking forms and the joint simulation of road condition, driving condition, and braking mechanical structure. On the basis of the current research, the four-layer neural network combined with PID control is proposed in this paper, which can better control the braking compared with the traditional PID controller and BP neural network, safety, efficiency and stability have been improved in the following cars. According to the animation simulation demonstration of CAR SIM software, it can be determined that the improved neural network PID controller in the mechanical system can realize the safety and active braking requirements of cruise vehicle, and it is more accurate and efficient than the traditional BP neural network PID controller. Relevant parameters of the existing millimeter-wave radars are set by the radar sensors, and the long-distance horizontal detection range is plus or minus 10 degrees. The radar is installed on the longitudinal axis of the front bumper and has a height of 442[Formula: see text]mm. According to Car SIM’s preset radar sensor module parameters, the sensors can independently collect and transmit relevant data, it is also possible to cooperate with other sensors for the same variable parameter to increase the accuracy and avoid abnormal errors caused by the failure of a certain sensor. Compared with the traditional PID controller and BP neural network, the combination of four-layer neural network and PID control can carry out better braking control and improve the safety, efficiency, and stability of the following cars.

A Design of FPGA-Based Neural Network PID Controller for Motion Control System.

In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.

Neural network PID control for combustion instability

Considerable research studies have been reported on developing effective active control means to mitigate combustion instability. In this work, a neural network PID (NN-PID) controller is proposed and demonstrated to suppress the oscillating pressure in a cylindrical Rijke tube, for which the combustion instability is modelled by a 1D acoustic network model together with linear and nonlinear flame models. The flame model is based on the classical n − τ model filtered by a first-order low pass filter. The nonlinearity of the flame model is realised using a gain function saturated with velocity perturbation. A fuel valve and a loudspeaker are adopted as two independent control actuators. Results show that system oscillation begins to attenuate when the NN-PID control starts. The attenuation rate using a fuel valve as the actuator is faster than that using a loudspeaker. For nonlinear combustion oscillations, the NN-PID controller could inhibit the reference pressure oscillation in the linear growth and saturation processes. A maximum overshoot of 2.3% could occur when the loudspeaker is used as the actuator to suppress system oscillation. The NN-PID controller is not affected by noise with a fuel valve or the loudspeaker and can effectively suppress and eliminate the system pressure oscillation in different oscillating stages with different flame models.

Deep PID Neural Network Controller for Precise Temperature Control in Plastic Injection-moulding Heating System

In this paper, deep PID neural network (PIDNN) controller is used in a nonlinear thermal regulatory control problem. We have a hot runner system and a nozzle with round tip as controlled plants. The control goal is to regulate their temperature responses for tracking a constant set point stably and precisely without exactly knowing mathematic models of plants during entire process by using deep PIDNN controller. The parameters (or weights) of controller are updated on-line based on gradient descent rule with Adam optimizer. Comparing to the results with PID control, deep PIDNN controller reduces overshoot, saves much power and enhance the control performance that temperatures almost fluctuate within ±0.2°C tolerance of set point in steady state.

Research on DC Electronic Load System Based on BP Neural Network PID Control

DC electronic load is a kind of instrument to replace the traditional dissipative resistance, which is widely used in the discharge performance test of DC power supply. However, it is difficult to achieve better control effect through analog circuit control in some test environments with high speed requirements for discharge test, and it is easy to produce overshoot. In digital circuits, however, in the actual control of DC electronic load, there are many uncertainties in the control system, such as interference and delay, which makes it difficult for the system to achieve the best control effect. This paper takes constant current mode as an example, in order to improve the effect of PID control, BP (Back Propagation) neural network is used to dynamically adjust the PID parameters online. After simulation analysis, under the ideal control environment, the adjustment time of BP-PID is only 0.0002s, which is 0.0005s higher than the traditional PID of 0.0007s. It can also reach a stable value within 0.0003s in the interference environment, with almost no overshoot. Therefore, compared with traditional PID control or analog circuit control, BP neural network PID control has better control effect on DC electronic load system.

Fuzzy Neural Network PID Control of Valve-controlled Hydraulic Motor for Camellia Fruit Picking Machine

Fuzzy Neural Network PID Control of Valve-controlled Hydraulic Motor for Camellia Fruit Picking Machine

Application of Fuzzy Neural Network PID In Laser-guided Mortar Projectile

Based on the high control performance requirement of laser-guided mortar control system, the permanent magnet synchronous motor (PMSM) is adopted in this paper as the electromechanical actuator of the system, the mathematical model of the motor is analyzed, and the vector control technology is adopted to achieve precise control of position, speed and torque of the electromechanical actuator. Aiming at the characteristics of non-linearity, strong coupling and large parameter changes of the system in flight, an improved fuzzy neural network PID control method is proposed by combining the classical PID control algorithm with fuzzy control and neural network control algorithm to realize the real-time tuning and optimization of PID parameters. The mathematical model of the electromechanical actuator control system is established and simulated. The results show that the fuzzy neural network PID control has good tracking performance, small amplitude error, and strong adaptability to load changes.

Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations

Recently, solar power generation systems are more and more popular and widely used in grid connected power generation, intelligent buildings, and power supply in remote areas. For photovoltaic panels, due to the influence of factors such as light intensity and ambient temperature, their output voltage and current become uns, and the output voltage of a single photovoltaic panel is considerably low. As a result, Boost circuits are needed for voltage boosting. PID controller is commonly used for Boost converter because it can effectively control the controlled object according to the characteristics of the controlled object. However, when the controlled object is complex and variable, the appropriate parameters are hardly to be selected by experience, and the fixed controller parameters may lead to unexpected performances under different working conditions. Here, a genetic algorithm combined with BP neural network PID control (GA-BPPID) is proposed to improve both dynamic and anti-interference performances of Boost circuit by introducing the global optimization ability of genetic algorithm and the adaptive adjustment characteristics of BP neural network. System modelling and detailed controller design procedures are provided. Finally, the theoretical analysis and controller design are validated by simulation results.

Lateral Stability Control of Four-Wheel-Drive Electric Vehicle Based on Coordinated Control of Torque Distribution and ESP Differential Braking

This research focuses on four-wheel-drive electric vehicles. On the basis of the hierarchical coordinated control strategy, the coordinated control system of driving force distribution regulation and differential braking regulation was designed to increase the electric vehicles steering stability under special road working conditions. A seven-degree-of-freedom model of an electric vehicle was established in MATLAB/Simulink, and then a hierarchical coordination control model of the Electronic stability program and dynamic torque distribution control system was established. Adaptive fuzzy control was applied to ESP and, based on the neural network PID control, a torque distribution control system was designed. On the basis of the proposed coordinated control model, a performance simulation and a hardware-in-the-loop test of the control system under the typical working condition of single line shift were carried out. From the final results, it can be seen that the proposed control strategy can greatly improve the safety of the vehicle after serious side slip, increase the stability of the whole vehicle, and effectively increase the vehicle lateral stability.

Data Collection of Digital Monitoring System for Agricultural Facilities Environment

In order to realize the intelligent control of the agricultural facility greenhouse environment cluster, the paper designs and implements an agricultural facility greenhouse environment cluster monitoring system based on RS485 bus and Modbus protocol. The system consists of on-site data acquisition and control part, remote monitoring centre monitoring software part and communication protocol part. We use the RS485 bus with Modbus protocol to realize the communication between the monitoring centre host and the microcontrollers of each greenhouse. The paper uses the BP neural network PID control algorithm to achieve the intelligent control of the agricultural greenhouse environment. Use Qt software to design a human-computer interaction interface to realize remote control of the greenhouse cluster. The experimental results show that the system can correctly collect the temperature, humidity and light intensity of the agricultural greenhouse environment, and can effectively control the environmental parameters of each greenhouse through the action of the control relay.

Uncertainty analysis of reactivity feedback coefficient for LFR core power control system during reactivity insertion accident

To study the influence of the reactivity feedback coefficient uncertainty on reactor core power control system, taking a lead-cooled fast reactor as the object, the core power control system with a BP neural network PID controller is designed. Drawing the input samples with Latin hypercube sampling method, the uncertainty quantitative analysis consists of the Chebyshev inequality and relative standard deviation calculation. Based on the method in this paper, the dynamic simulation of reactor core is performed. The results show that the uncertainties of the system output control performance parameters are less than 1% when introducing 50pcm and 100 pcm step reactivity. The uncertainty of the system output parameters changes and returns to zero with the action of the controller.

A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future.

Research on Seeker Servo Platform Based on BP neural network controller

Seeker is the core of precision guided weapon. The performance of its servo mechanism has a decisive impact on the accuracy of guidance weapon. The controller design of seeker servo system is of great significance to improve the function of seeker servo system. The mathematical model of the seeker servo system in the form of transfer function is built. The traditional PID control and BP neural network PID control methods are used to design the controller respectively. Aiming at the limitation of BP neural network, the BP neural network is improved. Finally, the simulation experiment is carried out in Matlab/Simulink. Simulation results show that the neural network PID controller has better performance than the traditional PID controller, so that the system has better adaptability and robustness.

Comparative study on control effect of permanent magnet synchronous motor based on Fuzzy PID control and BP neural network PID control

Aiming at the problems of low control precision, poor anti-interference ability and poor stability of traditional PID controller, the improvement effect of fuzzy control and BP neural network in closed-loop control of PMSM is compared. In the case of variable speed and variable load, the stability, real-time, anti disturbance and robustness of PMSM system with traditional PID controller, fuzzy self-tuning PID controller and BP neural network PID controller as speed regulator are simulated and analyzed. The simulation results show that the fuzzy self-tuning PID controller has more advantages in the response speed of the system, and can make the system quickly recover to the initial state in case of sudden change. The BP neural network PID controller can ensure the relative stability of the system is better, and can restrain the sudden fluctuation.

Design of a New Catheter Operating System for the Surgical Robot.

In interventional surgery, the manual operation of the catheter is not accurate. It is necessary to operate the catheter skillfully and effectively to protect the surgeon from radiation injury. The purpose of this paper is to design a new robot catheter operating system, which can help surgeons to complete the operation with high mechanical precision. On the basis of the original mechanical structure—real catheter, the operation information of the main end operator is collected. After the information is collected, the control algorithm of the system is improved, and the BP neural network is combined with the traditional PID controller to adjust the PID control parameters more effectively and intelligently so that the motor can reflect the output of the controller better and faster. The feasibility and superiority of the BP neural network PID controller are verified by simulation experiments.

AI-Based Proton Exchange Membrane Fuel Cell Inlet Relative Humidity Control

Humidity is a key factor affecting proton exchange membrane fuel cell (PEMFC) efficiency and output performance. Different working conditions have different requirements for humidity. Too high or too low water content inside the PEMFC will damage the output performance and even affect the PEMFC life. Therefore, how to control humidity appropriately is a crucial subject. This paper establishes a PEMFC internal water management and inlet humidity model, and analyzes the influence of the anode inlet humidity change on the performance of the PEMFC and the water content of the membrane through computational fluid dynamics (CFD) simulation. The direct control of the inlet humidity, which is difficult to be accurately measured, is converted to the temperature control of the bubble humidifier according to the proposed model, and a back propagation neural network proportion integration differentiation (BPPID) controller is proposed, which combines artificial neural network and digital PID control to adjust PID parameters in real time. The controller is applied to the temperature control of the bubble humidifier and compared with the traditional PID controller and the fuzzy PID controller. It is found that the performance of the BPPID controller is better through the comparison with the experimental results.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].