Proportional Integral Derivative Control Algorithm Research Articles

Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace

An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.

Real-time trajectory tracking of an unmanned aerial vehicle using a self-tuning fuzzy proportional integral derivative controller

In the present study, a desired reference trajectory was autonomously tracked by means of a quadrotor unmanned aerial vehicle with a self-tuning fuzzy proportional integral derivative controller. A proportional integral derivative controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize the quadrotor, to control the attitude and to track the trajectory. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of unmanned aerial vehicle and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which produce pitch, roll, yaw and height values. The fuzzy proportional integral derivative control algorithm is real-time applied to the quadrotor in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and self-tuning fuzzy proportional integral derivative controller have accomplished to track a defined trajectory with the aircraft, the self-tuning fuzzy proportional integral derivative controller has been able to control with less errors than the classical proportional integral derivative controller.

Design and development of a new large-stroke XY compliant micropositioning stage

This paper presents the design and development of a new flexure-based compliant XY micropositioning stage with large stroke. The parallel-kinematic XY compliant stage is designed based on the Roberts mechanisms. Pseudo-rigid-body model is developed to establish the quantitative models of the whole compliant mechanism. Finite element analysis is carried out to validate the performance of the XY stage. A prototype of the XY stage is developed for experimental investigations. Experimental results show that the stage can deliver a working range larger than 12 mm in each of the two working axes. Moreover, a feedback control using the proportional–integral–derivative control algorithm is implemented to demonstrate the positioning performance of the developed XY stage. The reported ideas can also be extended to the design and control of other micro-/nanopositioning systems.

Sensor enabled closed-loop bending control of soft beams

Control of soft-bodied systems is challenging, as the absence of rigidity typically implies distributed deformations and infinite degrees-of-freedom. In this paper, we demonstrate closed-loop control of three elastomer beams that vary in bending stiffness. The most stiff beam is comprised of a single prismatic structure made from a single elastomer. In the next beam, increased flexibility is introduced via an indentation in the elastomer, forming a joint. The most flexible beam uses a softer elastomer in the joint section, along with an indentation. An antagonistic pair of actuators bend the joint while a pair of liquid–metal-embedded strain sensors provide angle feedback to a control loop. We were able to achieve control of the system with a proportional–integral–derivative control algorithm. The procedure we demonstrate in this work is not dependent on actuator and sensor choice and could be applied to to other hardware systems, as well as more complex multi-joint robotic structures in the future.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.

To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions. After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70-180 mg/dL. Mean time in range 70-180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period. This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

Development of photoelectric balanced car based on the linear CCD sensor

The smart car is designed based on Freescale’s MC9S12XS128 and a linear CCD camera. The linear CCD collects the road information and sends it to MCU through the operational amplifier. The PID control algorithm, the proportional–integral–derivative control algorithm, is adopted synthetically to control the smart car. First, the smart car’s inclination and angular velocity are detect through the accelerometers and gyro sensors, then the PD control algorithm, the proportional–derivative control algorithm, is employed to make the smart car have the ability of two-wheeled self-balancing. Second, the speed of wheel obtained by the encoder is fed back to the MCU by way of pulse signal, then the PI control algorithm, the proportional–integral control algorithm, is employed to make the speed of smart car reach the set point in the shortest possible time and stabilize at the set point. Finally, the PD control algorithm is used to regulate the smart car’s turning angle to make the smart car respond quickly while the smart car is passing the curve path. The smart car can realize the self-balancing control of two wheels and track automatically the black and while lines to march.

Proportional-Integral-Derivative (PID) Control of Secreted Factors for Blood Stem Cell Culture.

Clinical use of umbilical cord blood has typically been limited by the need to expand hematopoietic stem and progenitor cells (HSPC) ex vivo. This expansion is challenging due to the accumulation of secreted signaling factors in the culture that have a negative regulatory effect on HSPC output. Strategies for global regulation of these factors through dilution have been developed, but do not accommodate the dynamic nature or inherent variability of hematopoietic cell culture. We have developed a mathematical model to simulate the impact of feedback control on in vitro hematopoiesis, and used it to design a proportional-integral-derivative (PID) control algorithm. This algorithm was implemented with a fed-batch bioreactor to regulate the concentrations of secreted factors. Controlling the concentration of a key target factor, TGF-β1, through dilution limited the negative effect it had on HSPCs, and allowed global control of other similarly-produced inhibitory endogenous factors. The PID control algorithm effectively maintained the target soluble factor at the target concentration. We show that feedback controlled dilution is predicted to be a more cost effective dilution strategy compared to other open-loop strategies, and can enhance HSPC expansion in short term culture. This study demonstrates the utility of secreted factor process control strategies to optimize stem cell culture systems, and motivates the development of multi-analyte protein sensors to automate the manufacturing of cell therapies.

High-speed download of seismographs using private cloud technology and a proportional integral derivative controller

ABSTRACTSeismic exploration is widely used in mining projects. A cable-free self-positioning seismograph has been developed by College of Instrumentation & Electrical Engineering of Jilin University that may collect and store seismic data wirelessly or by Ethernet. When large data sets are produced, data download may affect the schedule of a project. To improve the rate of transfer, a system that allows parallel download of multichannel seismic data is reported here. The system is based on private cloud technology and proportional integral derivative control. A private cloud was employed to provide suitable performance and availability. A proportional integral derivative control algorithm was developed for scheduling to maintain the load balance of the system. The system was employed in a seismic exploration project in Liaoning, China. The average download time of six lines was approximately 1.1 hours, demonstrating that the system reduced download time and enhanced exploration efficiency.

PID 제어를 통한 쿼드콥터 다중목적 근사최적설계

In this study, the nondominated sorting genetic algorithm (NSGA-II) is used to obtain the optimized proportional-integral-derivative (PID) gain value that can quickly recover the motion of a quadcopter after a disturbance. Prior to PID control, the four-rotor quadcopter interval was defined using computational fluid dynamics (CFD). Through the definition of this model, the PID control algorithm was generated. To construct a response surface model, D-optimal programming was used for the generation of experimental points. For this purpose, a gain value that satisfies both the roll and altitude PID gain values is obtained. Using the NSGA-II, the gain value of shorten time of the quadcopter motion control can be optimized.

Comparative Study of Fuzzy PID Control Algorithms for Enhanced Position Control in Laparoscopic Surgery Robot

This paper proposes intelligent fuzzy proportional–integral–derivative (PID) controllers for the position control of a master–slave configuration laparoscopic surgery robot system. For the slave robot controller, two fuzzy PID control algorithms are implemented: a rule-based fuzzy control algorithm for online PID gain tuning and a learning fuzzy controller to tune the rules in the rule buffer automatically online. The two fuzzy controllers are tested using sinusoidal reference motions with various periods and the test results are compared with those of a conventional PID controller in terms of position control performance. Various performance indices are used for the comparison, including root mean square error, steady-state error, integral of average error, integral squared error, integral time-weighted absolute error, and integral of time multiplied by the squared error. The evaluation shows that the learning fuzzy controller yields the best performance among the three algorithms. Furthermore, the relationship between the scaling gains and the performance can be deduced to construct a comparative tuning algorithm, which enables the scaling gains to be optimally tuned with less trial and error. Further refinement of the algorithm for enhancing control performance is under way.

Development of a New Control Algorithm for Automatic Irrigation Scheduling in Soilless Culture

In soilless culture, water and nutrients must be frequently and precisely applied due to the reduced volume and low water holding capacity of the substrate. For this reason, irrigation scheduling is an important and difficult task. The use of an irrigation control tray with two electrodes (level sensor) is a simple way for controlling irrigation. Irrigation operation is triggered when the water level in the tray decreases below a preset level. This is a simple on-off control but inflexible because it requires periodic manually calibrations. This work aims at developing a more efficient control system for the irrigation management of soilless culture. The control system is based on a Proportional Integral Derivative (PID) algorithm and it allows for fully automatic operation with a minimum set of variables in order to reduce the cost of the equipment. The results obtained demonstrate that the PID control algorithm can efficiently be used to control irrigation in soilless culture. The calculated average daily leaching fractions fit reasonably well to the target values. Nevertheless, some improvements of the control algorithm are required to reduce the variability of the calculated leaching fractions during the initial stage of crop development.

Fuzzy Self-Tuning PID Temperature Control Modeling and Simulation System

For the current general heating control system tracks the set temperature hysteresis heating device to automatically adjust the real-time problem of poor design a fuzzy self-tuning proportional-integral-derivative (PID) controller parameters. Scoop out using PID control and fuzzy control algorithm combined method; fuzzy PID control parameters are adjusted. Use Matlab to model in simulink, the aryl the controller simulation analysis. The results show that the fuzzy self-tuning PID controller overshoot σ≈l%, steady-state error Island es=0. This method can improve the performance of the temperature control system..

Intelligent Train Operation Models Based on Ensemble Regression Trees

Traditional control algorithms in Automatic Train Operation (ATO) system have some drawbacks, such as high energy consumption and low riding comfort. Combined with data mining methods and driving experience, two Intelligent Train Operation (ITO) model s for the subway train control are proposed. Firstly the training data set was sorted out and sieved out from the real train operation data set by drivers in Beijing subway line Yizhuang to establish the standard database. By using Classification and Regression Trees (CART) algorithm and Bagging ensemble learning method which base on CART algorithm , two ITO model s are dug out to represent the output of controller with limited speed, running time and gradient. In the train control simulation platform, ITO models were compared with the traditional PID (Proportional Integral Derivative) control algorithm of ATO systems . The simulation results indicate the proposed ITO models are better than PID control in energy consumption, riding comfort and switching times of controller ’s output. Furthermore, the ITO model with bagging ensemble learning method is better especially in energy consumption and riding comfort.

A Hybrid of Fuzzy and PID Controller for EAST AIA CASK Baking System

Based on the real operation requirements of experimental advanced superconducting tokamak (EAST) articulated inspection arm (AIA) CASK baking system, this paper focuses on how to achieve high accuracy of temperature control. Given the complexity of temperature control system, a hybrid of fuzzy and proportional-integral-derivative (PID) controller is designed. According to the conditions that exist, a testing experiment that simulates the CASK baking system is built. Through the analysis of the experimental results, a hybrid of fuzzy and PID control algorithm is designed as the core strategy of the temperature control system.

Design methodology and self-turning velocity control for high-speed slim sensorless brushless direct current motors with self-lubricated bearings

This study presents the motor design methodology and adaptive fuzzy self-tuning proportional–integral–derivative (PID) velocity closed-loop control of slim sensorless brushless direct current (BLDC) motors with self-lubricated bearings that operate at a high speed of 14,000rpm. Moreover, the novel sensorless commutation method includes a mask-based phase shift detector to detect precise optimal commutation points. A prototype of the slim sensorless radial-flux BLDC motor with a self-lubricated bearing for use in the blowers of vacuum cleaners was manufactured and the motor design methodology was verified. The damping interaction caused by the self-lubricating hydrodynamic bearings, shafts, and blades of blowers was also considered, together with a multi-physics analysis. The velocity control experiment used a adaptive fuzzy self-tuning PID control algorithm that has been proven to improve the dynamic performance of the slim sensorless BLDC motors in the blowers of vacuum cleaners. The settling time converged within 1s, that is, the vacuum cleaners rapidly switched to different speeds, and the control approach exhibited robustness and precision in a steady state.

Performance and Robustness Considerations for Tuning of Proportional Integral/Proportional Integral Derivative Controllers with Two Input Filters

The aim of this work is to present an extension of the closed-loop model-reference robust tuning (MoReRT) methodology to proportional integral and proportional integral derivative control algorithms enhanced with two input filters (set-point and feedback signal filters) for control of stable, integrating, inverse response, and unstable controlled processes. The method is based on the use of target models that include two design parameters (closed-loop dominant poles relative speed and damping). The control system performance/robustness trade-off is analyzed to reduce the design parameters to only one directly related with the control system robustness measured with the maximum sensitivity (MS). The proposed design methodology takes into consideration the control system load-disturbance rejection, set-point tracking, control effort smoothness, measurement of high frequency noise attenuation, and robustness to changes on the controlled process dynamics. The incorporation of these two input filters allows ta...

Input-Rate Based Adaptive Fuzzy Neuron PID Control for AQM

Internet routers play an important role during network congestion. All the routers have buffers at input and output ports to hold the packets at congestion. Various congestion control algorithms have been proposed to control the congestion. Recently, some proportional-integral-derivative (PID) controller based algorithms have been proposed as Active Queue Management (AQM) schemes to address performance degradations of end-to-end TCP congestion control. However, most of the proposed PID-controllers for AQM are validated for their performance and stability via intuitive explanation and simulation studies instead of theoretic analysis and performance evaluation. But there are a few drawbacks of PID-controller based AQM algorithms leading to poor performance like causing data retention dropping and oscillation when the time delay is large, which means that the existing PID-controller can not meet the Quality of Service (QoS) requirements. To overcome the drawbacks of traditional PID, we analyze and enhance the PID-controller based AQM algorithm by regarding the TCP congestion control mechanism as an input-rate based Adaptive Fuzzy Neuron PID control algorithm (IRAFNPID) to avoid congestion in TCP/AQM networks. By means of simulations, we evaluate and compare the performance of traditional PID, single neural adaptive PID(SNAPID) and IRAFNPID, simulations with experiment data analysis and find that IRAFNPID has better convergence, stability, robustness, goodput and lower loss ratio.

IMPLEMENTATION OF PID CONTROL TO REDUCE WOBBLING IN A LINE FOLLOWING ROBOT

A Proportional-Integral-Derivative (PID) Control Sy stem provides for controlling the value of a variab le, in this case the position of the robot, so that it is at or near a desired value, called the set-point, in this case on the centre of the line. In contrast to other control systems this provides for the smoothest type of con trol with minor deviations and overshooting if impl emented correctly. This paper discusses, the concept, design and implementation o f a line following robot that uses the PID control algorithm to govern its motion. The control algorithm is implemented in real time b y a pre-programmed microcontroller.

Modeling and Simulation of Hydraulic Roll Bending System Based on CMAC Neural Network and PID Coupling Control Strategy

The hydraulic roll bending control system usually has the dynamic characteristics of nonlinearity, slow time-variance and strong outside interference in the rolling process, so it is difficult to establish a precise mathematical model for control. So, a new method for establishing a hydraulic roll bending control system is put forward by cerebellar model articulation controller (CMAC) neural network and proportional-integral-derivative (PID) coupling control strategy. The non-linear relationship between input and output can be achieved by the concept mapping and the actual mapping of CMAC. The simulation results show that, compared with the conventional PID control algorithm, the parallel control algorithm can overcome the influence of parameter change of roll bending system on the control performance, thus improve the anti-jamming capability of the system greatly, reduce the dependence of control performance on the accuracy of the analytical model, enhance the tracking performance of hydraulic roll bending loop for the hydraulic and roll bending force and increase system response speed. The results indicate that the CMAC-PID coupling control strategy for hydraulic roll bending system is effective.

The Course Control Based on an On-line Self-adjusted PID Control Algorithm for Unmanned Surface Vehicles

To improve the robustness of course control of the studied USV(unmanned surface vehicle) system under diverse sea conditions and settle the coupling between the surge speed and the course,a PID(proportional-integral-derivative) control algorithm whose control parameters are adaptively adjusted on-line is developed.The control parameters of the proposed PID control algorithm are some functions which are adaptively adjusted according to the surge speeds and the state errors of course.It is shown by Lyapunov function method that the proposed algorithm can keep the USV system stable. Some sea trials demonstrate that the control parameters are self-adjusted on-line according to surge speed and the state error of course under different sea conditions,moreover,the robustness and anti-disturbance performance under different sea conditions are improved,and the overshoot is decreased to less than 1.5%without static errors.