Traditional PID Control Algorithm Research Articles

Research on Real-time Simulation and Control of Linear 1-stage Inverted Pendulum

Inverted pendulum system is a complex, unstable and nonlinear system.In order to make it become an adaptive and robust stable system in control strategy, the mathematical model of linear l-stage inverted pendulum has been established by means of Newton mechanics. The control strategies including classic control methods and modern control methods, such as the PID control algorithm, the pole assignment algorithm and the T-S fuzzy control algorithm.The real-time control results show that the PID control algorithm and the pole assignment algorithm based on the dynamic model have different qualities and characteristic. The structure of PID control is smiple, but the PID controller parameters is more difficult to select, and the overshoot of the system is easy to be increase because of the system strong instability. In addition, the traditional PID control algorithm can only control the pendulum’s angle, and can’t control displacement. Pole placement method has the better robustness and transient characteristics, but it mainly relies on the experience of engineers to select the desired pole, so it does not have the convenience and simplicity of the PID. The T-S fuzzy controller doesn’t need to build an accurate mathematical model of the object, the nonlinear system can be fuzzified into local linera model based on the empirical knowledge. The T-S fuzzy controller reduce the dimension of fuzzy controller and simplify the rules. The real-time control results analysis shows the pendulum and cart position can quickly be stabilized with strong robustness. In real-time control of the inverted pendulum system, car position, car speed and the pendulum angle and angular velocity, etc not only to be considered, but also the state variables, measurement accuracy and sensitivity of the sensor have to consider. So there are a lot of work to do in the real-time control of inverted pendulum system.

Design and Research of Energy Saving Control System for Heating Network

In the central heating system, because the controlled object has the characteristics of time-varying, nonlinear, strong coupling and big lag. The traditional single loop model and the conventional PID control algorithm are difficult to react the mechanism and meet the control requirements. Therefore a model of multiplex heating network is established. By comparing the single neuron PID control algorithm with the traditional PID control algorithm, the results show that the single neuron PID control algorithm has a better control effect.

Design of a New Suspension System Controlled by Fuzzy-PID with Wheelbase Preview

This paper studies a new active vehicle suspension controlled by Fuzzy-PID controller with wheel base preview. By this new algorithm, the fuzzy controller controls the parameters of the PID in time .Then the wheelbase preview is integrated to ensure the future road information is combined with the current state of the vehicle effectively. A sensor is placed on the front suspension collects and feeds forward the preview information as an input to the rear suspension system . MATLAB simulations show that using such control strategy can obtain a low noise and better robustness performance than the traditional PID control algorithm.

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.

Improved Modeling and PID Control Algorithm for Three-Tank Liquid Level System

Three-tank liquid level system is a more typical non-linear and delay object with strong representation and background of industry. The part or whole of many objects can be abstracted into a mathematical model of three-tank liquid level system. Research on modeling and control of the system has important theoretical significance and practical value. This article describes how to use Simulink function to establish the mechanism of three-tank liquid level system model. And at the time, an improved PID control algorithm is proposed. The results show that it has more superior performance for three-tank liquid level system simulation control compared with the traditional PID control algorithm.

The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor

Based on analyzing mathematical mode of the permanent magnet linear synchronous motor (PMLSM), three-closed-loop control system is presented in this paper. Combined the advantages of traditional PID control algorithm and fuzzy control algorithm, according to the characteristics of linear motor and the possible factors of uncertainty, a set of adaptive fuzzy PID control system is designed for the speed loop of the proposed control system, moreover, fuzzy inference rules is established to realize the Fuzzy PID controlling of the speed loop. In the end, the simulation models of the motor and the whole control system are built on Matlab/Simulink platform to compare and analyze the fuzzy PID control and conventional PID control. Simulation results show that the designed fuzzy PID speed loop controller can significantly improve the response performance of linear motor.

Research of Intelligent Control Policy on Main Steam Temperature of Thermal Power Plant

In view of the nonlinear, time-variable, long delay, large inertia character of main steam temperature system, the difficult point of control is summarized. The status quo of application study on main steam temperature by fuzzy control, neural network and fuzzy neural network control, genetic algorithms is introduced. And taking "the 600 MW concurrent boiler load in 100%" as an example, carry on the main steam temperature control simulation by means of Matlab/Simulink software. In this simulation, four control strategies are taking for the simulation experiment, and comparing with the traditional PID control algorithm. The simulation results prove that fuzzy neural network control strategies have good robustness, fast response, short setting time, and great potential for the control of main steam temperature.

Design and Simulation of PEM Control System for Reactive Sputtering Coating

During the reactive sputtering process, due to the hysteresis effect, the sputtering state should be maintained in the transition region of the hysteresis curve which can used to obtain stoichiometric compound films at a high deposition rate. If sputtering state changes, it is impossible to make the sputtering state step back to the original point by manually control the process parameters, because the hysteresis is irreversible. Thus it requires a method of fast feedback to control the sputtering power and the reaction gas flow rate into the chamber. In this paper the PEM (plasma emission monitor) control system and the single neuron self-adaptive PID algorithm have been designed to maintain the sputtering state in proper condition, namely preventing the target from poisoned in the reactive sputtering. The signal acquisition and the controller design were the major parts of the PEM system. The signal acquisition was realized by the optical emission spectrometer. And the single neuron self-adaptive PID controller has been designed in the paper. Using the MATLAB software, the simulation experiments have been done. The output waveforms showed that using traditional non-adaptive PID control algorithm, the overshoot is over 6% and the regulation time is over 1.8s, but using single neuron self-adaptive PID algorithm the overshoot 0 and regulation time 0.5s. Monitoring the target spectral intensity at various reaction gas flow rate, several conclusions could be obtained. The overshoot 6% indicated that the reactive gas flow into the chamber was excessive, the target was poisoned and the sputtering state in chemical mode. And while the overshoot was zero which indicated that the target poisoned was avoided and the reaction ran in defined condition. The PEM using the single neuron self-adaptive PID algorithm responded faster than that using the traditional PID algorithm. The PEM system designed in the paper can effectively avoid the target poisoned and make the reactive sputtering maintain at an ideal state.

Research on Signal of Branch Junction Based on Adaptive Fuzzy-PID Control

With the rapid development of the urbanization, the problem of city transportation has been becoming the same issue to each big city in China. As the converge points of urban traffic, the branch junctions are playing important role in the road net and traffic flow. It is true that traditional PID control algorithm is widely used in various control systems. Because its principle is simple and easy to build and has no error in the steady state, etc. However, it does not have a good adaptability towards the change of parameters. It is inferior towards interference, etc. According to the above situation, a adaptive Fuzzy-PID control algorithm is proposed which has returned the characters of regular PID control algorithm. The fuzzy control algorithm is used to realize the on-line regulating relative PID parameters. The simulation analysis indicates that the presented method is effective to improve the main road control.

The Research for Electro-Hydraulic Servo Control System Based on Fuzzy PID

Electric-hydraulic servo system is a nonlinear and uncertain system with time-varying parameters and interference. The fuzzy PID control algorithm is used,which not only keep the flexible characteristic of fuzzy control algorithm but also inherit the traditional PID control algorithm advantages of high control precision. Research shows that fuzzy PID control has the character of fast response speed, rising fast, small lag, small overshoot, anti-interference, do not depend on the accurate model.

Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation

In the process of recycling chemical product in coking object, ammonia and tar were indispensable both metallurgy and agriculture, so the control of separation process for tar-ammonia was one of the most important control problems. Due to the density difference between the tar and ammonia was greater, easier to separate, the control method based on PID was used in field at present. But the control effect of traditional PID was not good because of environment change and fluctuation in material composition. Separation process for tar-ammonia was analyzed firstly, in view of the shortcoming of traditional PID control algorithm, single neuron PID control algorithm based on variable scale method was adopted through using optimization method. Detailed algorithm steps were designed and applied to tar-ammonia separation system. Simulation results show that by comparison with traditional PID algorithm, the algorithm have the following advantages: faster learning speed, shorter adjusted time and good convergence performance.

Design of Step Motor Fuzzy Control System and its Application

The classical PID controller is based on the concrete model of the object and is only suitable for the design of linear control system. Step servo system is a typical nonlinear system, so the intelligent control strategies should be adopted. In this paper an approximate model of the step motor is presented, a compound fuzzy PI controller is designed to fulfill the control of the step motor, and is successfully applied to the automatic control the scanning device of eddy current nondestructive testing (ECNDT). The proposed controller is robust to load torques, cogging forces, and other disturbances. The fuzzy PI controller also guarantees asymptotic stabilization and possesses better dynamic performance compared with the traditional PID control algorithm. The simulation study and an experimental validation carried out on the automatic control system of the scanning device confirmed the suitability of the proposed approach.

The Key Technology Research for Vision Inspecting Instrument of Steel Ball Surface Defect

It has been necessary to adopting automatic detection of steel ball based on machine vision in industrial processing. The detecting instrument for surface quality of steel ball based on machine vision and embedded control technique and is applied to detecting external bug region of steel ball in bearing. Its image detection and control system require excellent real-time character and high control accuracy. This paper put forward a new design for image processing and control system of detecting instrument. Firstly, we adopted OTSU segmentation arithmetic based on image enhancement and median filtering. As a result, steel ball was detected by area character parameter. Secondly, we adopted TMS320LF2407A as main processor and integrated CPLD to develop an embedded controller. It presents a novel optimal design method for PID controller based on the ant colony optimization (ACO) algorithm to optimize traditional PID control algorithm. At last, a performance study of experimental system is conducted and it shows that the proposed method can be used in online steel ball detection applications.