BP Neural Network PID Controller Research Articles

Modeling and Simulation of Energy‐Regenerative Active Suspension Based on BP Neural Network PID Control

In this paper, an electromagnetic energy‐regenerative suspension system is proposed to achieve active control and vibration energy harvesting. For this system, a PID controller based on BP neural network algorithm is designed and vehicle dynamic performances are studied. Based on the traditional energy‐regenerative efficiency calculation, a novel self‐supply energy efficiency concept is proposed to evaluate the utilization effect of the recycled energy for this dual‐functional suspension. Simulations are carried out, and the results show that the vehicle dynamic performances are effectively improved under different input conditions, including road surfaces and vehicle speeds. Furthermore, the energy‐regenerative suspension can recover part of vibration energy, where the self‐supply energy efficiency is about 55% and the energy‐regenerative efficiency is about 16%. Meanwhile, the BP‐PID algorithm also enables the suspension system’s self‐adaptability and stability characteristics on its energy recovery capability.

Design and Experimental Analysis on Autonomous Control System of Wave Glider

In order to realize the unattended and autonomous navigation of Wave Glider in the ocean, an autonomous control system is designed, which mainly includes the control circuit part, motor driver part and control algorithm part. System integrates solar energy charging module, wireless communication module, satellite communication module, rudder motor module and data acquisition module etc. In order to improve the intelligence and autonomy, the algorithm of Gaussian earth algorithm, visual tracking algorithm and BP neural network PID control algorithm are used to ensure the navigation accuracy of Wave Glider. To verify the reliability of the system, Wave Glider is tested in the ocean in the aspects of long time running, target points tracking, velocity keeping, and data acquisition. The experiment shows that under normal ocean conditions, Wave Glider can realize the long term autonomous navigation tracking in the ocean, but the speed fluctuates significantly by the current ocean condition; The acquisition of sensor data is normal, stable and reliable, which meets the requirement of long term using in the ocean.

Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

Effect of downward depth and inflation pressure on contact force of gasbag polishing

Gasbag polishing is a kind of ultra-precision machining technology by means of flexible contact, while how to control the polishing contact force online is one of the key issues. In this paper, by analyzing the effect of downward depth and inflation pressure on the contact force experimentally, and then the coupling contact force model is developed. Thus, the predictions of polishing contact force and inflation pressure in terms of the nonlinear composite material of rubber gasbag can be obtained, which can be used to get the optional combination and controllable range of polishing contact force, and to construct the control system of coupling contact force as well. Experimental study shows that applying coupling contact force model to the control system of gasbag polishing contact force with BP neural network PID control strategy is a proper method, which realizes the polishing contact force controllable online and uniform surface quality of mold.

Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

Control System Design of Anti-rolling Tank Swing Bench Using BP Neural Network PID Based on LabVIEW

The anti-rolling tank swing bench is a typical hydraulic position servo system. Its function is to simulate the ship roll motion and to verify the performance of the anti-rolling tank. Generally, the PID control method can achieve an ideal control effect. But the uncertainties, such as the couplings and the nonlinearty of the swing bench, make it difficult to establish the precise mathematical model of system. During experiments, the different disturbance torques are generated according to the different dimension scales of the anti-rolling tanks. Changes of the natural characteristics of the system are caused by changes of load, and the fixed parameters of PID controller are difficult to continue to ensure a good control effect. In order to reduce the influences caused by the factors mentioned above, a BP neural network PID control system based on virtual instrument LabVIEW is designed. By simulating the responses of the system in real time control, the verification tests using actual system are avoided. Finally, the experiments of swing bench are carried out. The results show that the controller can ensure the control accuracy and stability. The parameters tuning process efficiency is improved. The operation is convenient. The data acquisition process is simple and practical.

Plastic Tank Temperature Control System Based on OPC Technology

This paper analyzes the characteristics of the PLC and MATLAB, describes the OPC technology and introduces the system structures and procedures which uses the OPC technology to achieve the exchange of data between PLC and MATLAB system. For example, in the plastic tank temperature control system, it mentions the BP neural network PID control algorithm which is applied to the PLC control system by using the MATLAB intelligent controller. The results show that this control method is simple and the control effect is good, much better than the traditional PID control.

MEMS-based low-cost strap-down AHRS research

With the rapid development of modern Micro-Electro-Mechanical Systems (MEMS) technology, the requirement of low cost strapdown AHRS based on MEMS sensor is becoming feasible. This paper presents the results of the attitude determination using low-cost MEMS-based sensor. Aiming at the accumulative deviation incurring by low-cost MEMS gyroscope drifting and integration deviation, this study proposes a multi-sensor data fusion algorithm that uses benchmark reference vectors to detect and correct the gyro drift. The proposed algorithm adopts the method of attitude evaluation based on Direction Cosine Matrix (DCM). A BP neural network PID controller is used to compute gyro drift for real-time correction. Numerical simulation shows that the algorithm meets the requirements of the system. In order to test the actual performance of the system, a hardware platform of strap-down AHRS is designed, based on a three-axis gyroscope, a three-axis accelerometer, three-axis magnetic sensors and a Digital Signal Processor (DSP). Finally, the three-axis flight turntable table test results obtained, show that the static accuracy of the attitude angle is better than 0.5° and the dynamic accuracy is better than 3°. Moreover, the AHRS is tested on a micro-Unmanned Aerial Vehicle (UAV), and the practical application feasibility is validated.

The Study of BP Neural Network PID Control Based on the Cutting Chatter Suppression System

The intelligent control strategy of BP neural combined network with classical PID control is mainly studied and simulated. The advantages of the control strategy are discussed. Based on the simulated data, the BP neural network PID control has the stronger adaptive ability.

Design of PID Temperature Control System Based on BP Neural Network

Based on BP neural network PID controller has the ability to approximate any nonlinear function, can achieve real-time online tuning PID controller parameter . Through the system simulation analysis, simulation results show that the BP neural network tuning PID control than traditional PID algorithm and BP network algorithm has a greater degree of improvement, the system has better robustness and adaptability, its output can also achieve the desired control accuracy through online adjustments. Suitable for temperature control system.

Study on BP Neural Network PID Control for Hydro-Viscous Drive System

Establish simulation model based on BP neural network PID control to solve the issue of hydro-viscous drive speed regulating start control strategies, experimental verifications prove its adaptability through AMESim/MATLAB co-simulation, research shows that: BP neural network PID control for hydro-viscous drive has a good self-correcting effect, the output speed adjusts towards the opposite direction according to the error and the error rate, while maintaining the smoothness of the output curve, thereby it can avoid over-large mechanical shocks, it indicates the BP neural network PID control is suitable for speed regulating start.

Research on Hydraulic Control System of Straightening Machine with Nine Rolls for Plates Based on BP Neural Network PID Controller

A mechanical transmission equipment of traditional straightening machine for plates are driven by worm gear and worm, which causes small straightening force, slow pressing speed and low control precision. However, screwdown control system of straightening machine can be driven by hydraulic system, which will lead to large straightening force, rapid pressing speed and high control precision. This system was designed for straightening machine with nine rolls for plates, its transfer function was deduced, and the analysis on its stability and time response was conducted. A BP neural network PID controller was utilized in the system for improving dynamic characteristics. It can be concluded that the system responds rapidly, and stability and control precision are high if BP neural network PID controller is used in the system.

Research on Yaw Stability Control of Active Front Steering Based on BP Neural Network PID

This paper proposes using BP neural network PID to improve the yaw stability of the vehicle with active front steering system. A dynamic model of vehicle with active front steering is built firstly, and then the BP neural network PID controller is designed in detail. The controller generates the suitable steering angle so that the vehicle follows the target value of the yaw rate. The simulation at different conditions is carried out based on the fore established model. The simulation results show the BP neural network PID controller can improve the vehicles yaw stability effectively.

Research on Pressurizer Pressure Control System Based on BP Neural Network Control of Self-Adjusted PID Parameters

The pressurizer is an important device in nuclear reactor system, and the traditional PID regulator is usually used to control pressure system of pressurizer in modern reactors. However, it is difficult to get precise parameters of traditional PID controller, and the PID control method is relied on the precise mathematical model badly. And the response of PID controller is often shown by the large amount of overshoot and long setting time which are not the desired results. For such a large inertia and complex time-varying control system, the tradition PID controller can not obtain the satisfy control results. A controller based on BP neural network in this paper has a simple structure, and the parameters of PID controller can be tuned on-line by the neural network self-learning characteristics. The computer simulation experiment demonstrates that the BP neural network PID controller performs very well when compared with the tradition PID regulator in minimal overshoot and more quick response.

Application of PID Control Algorithm Based on Improved BP Neural Network to Turntable System

Aiming at the problem that traditional PID control algorithm is difficult to get ideal control effect, a PID control algorithm based on improved BP neural network is proposed to improve the performance of turntable system. According to the structure and characteristic of BP neural network, the construction of PID controller and the description of improved BP neural network algorithm are introduced at first. Then, on the basis of the least square method and neural network prediction model of controlled object, the weight adjustment algorithm of PID is improved by replacing the measured values of BP network with calculated forecast output. A mathematical model of turntable control system is established and simulated. Simulation results show that the improved BP neural network PID controller has good control performance, high tracking accuracy and strong system robustness, which can be better applied to turntable system.

Speed-Regulation System of PMLSM Based on BP Neural Network PID Control

For the characteristics of permanent magnet linear synchronous motor (PMLSM) hoisting system’s nonlinear, time-varying and vulnerable to disturbance, based on the established PMLSM d-q axis dynamic model, designed of an improved BP neural network PID speed controller. Modified the fixed learning rate in BP neural network to adaptive adjustable, and added the momentum to reduce the oscillation tendency in the learning process, greatly improved the convergence speed and avoided the network into a local minimum. Compared with the simulations of traditional PID and the improved BP network PID speed controller, the results showed that the improved BP neural network PID speed controller had the high quality and it can make the system with better dynamic performance and robustness.

Based on the BP Neural Network-PID Series Control Boiler Main-Steam Temperature System Research

Due to the boiler main-steam temperature system exists more serious characteristics,such as much capacitive,nonlinear,time-varying and lag, so adopt cascade control strategy. This paper design a control algorithm which is based on BP neural network, it can accelerate the regulating time, and combined with the conventional PID controller, constitute the BP neural network - PID cascade control strategy. This control strategy not only contain the BP neural network control in real time system strong anti-interference ability characteristic, but also fully utilize the PID controller response speed characteristic. The simulation results show that based on the BP neural network - PID series control boiler main-steam temperature system can achieve satisfactory control effect.

A Variable Speed Control Hydraulic System Based on BP Neural Network PID Controller

The variable speed hydraulic systems have many advantages, and research about this field in recent years has developed rapidly. In this paper, a variable speed hydraulic system is studied using BP Neural Network PID controller. The research results show that using BP Neural Network PID controller can achieve good control effect.

Design of Ball-Beam Control System Based on Machine Vision

The nonlinear Ball-beam system combine with a CCD camera is studied in this paper. The images which include Ball-beam system and a ruler are collected by CCD sensor. The image is segmented using the adaptive image binarization threshold algorithm, and then the ruler, the ball position and the pointer position are extracted from the image. The ruler is scaled and the pointer position is also calculated. Finally, the value of pointer position is input into Ball-beam system as an expected ball balance position. A Fuzzy self-tuning PID controller and a BP neural network PID controller are designed for ball balance stable control. After experimental, the Ball-beam balance control in any position can be fulfilled using both of algorithms.

Study on Improved Neural Network PID Control for Coal Mining Power Grid Arc Suppression Coil Compensating System

The paper analyzed arc suppression coil with magnetic bias compensating system with linear system rules. The nonlinear and time-variable performances are considered during model building process. In order to optimize control effect, the paper adopted improved BP neural network PID controller with closed loop control method. Improve BP neural network with the combination of the two strategies, adding momentum method and adaptive learning rate adjustment, can not only effectively suppress the network appearing local minimum but also good to shorten learning time and improve stability of the network furthermore. The results of simulation and experiments indicate that arc suppression coil based on improved neural network with PID control method can quickly and accurately control the compensating capacitive current to an expected value and it has strong robustness. The paper also provided core controller with software and hardware designing scheme based on STM32 microcontroller.