Proportion Integration Differentiation Parameters Research Articles

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson’s disease

Objective. Beta triggered closed–loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson’s disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia–thalamus–cortical circuit. Approach. A dynamic basal ganglia–thalamus–cortical mean–field model was established to emulate the medication rhythm. Then, a dynamic target model was designed to embody the multi-timescale dynamic of beta power with milliseconds, seconds and minutes. Moreover, we proposed a closed-loop DBS strategy based on a proportional–integral–differential (PID) controller with the dynamic control target. In addition, the bounds of stimulation amplitude increments and different parameters of the dynamic target were considered to meet the clinical constraints. The performance of the proposed closed-loop strategy, including beta power modulation accuracy, mean stimulation amplitude, and stimulation variation were calculated to determine the PID parameters and evaluate neuromodulation performance in the computational dynamic mean–field model. Main results. The Results show that the dynamic basal ganglia–thalamus–cortical mean–field model simulated the medication rhythm with the fasted and the slowest rate. The dynamic control target reflected the temporal variation in beta power from milliseconds to minutes. With the proposed closed-loop strategy, the beta power tracked the dynamic target with a smoother stimulation sequence compared with closed-loop DBS with the constant target. Furthermore, the beta power could be modulated to track the control target under different long-term targets, modulation strengths, and bounds of the stimulation increment. Significance. This work provides a new method of closed–loop DBS for multi-timescale beta power modulation with clinical constraints.

EASS: An automatic steering system for agricultural wheeled vehicles using fuzzy control

Automatic steering is a crucial technology for achieving automatic navigation and unmanned driving of agricultural machinery. In this research, agricultural wheeled vehicles with the HPAS (Hydraulic Power Assistant Steering) system were selected as the research object and the EASS (Electric Automatic Steering System) was proposed by using fuzzy control to realize effective steering instead of human drivers. The PID (Proportional Integral Differential) algorithm was used to process the difference between the desired and actual steering angles and to realize automatic control of rotation of directional wheels. Fuzzy control was adopted to achieve self-adjusting of PID parameters and to optimize working state of the steering motor in real-time for different agricultural wheeled vehicles under various operating conditions. Fuzzy control rules were explored and formulated by analyzing variation of the steering angle under different resistive torque values. For evaluation of its feasibility, the newly developed EASS was applied in automatic navigation of a rice transplanter, a tractor, and a high-clearance sprayer. Field testing results showed that the maximum path tracking lateral error and heading error were 4.39 cm and 2.13°, respectively. And the maximum RMS (Root Mean Square) errors were 3.75 cm and 1.02°, respectively. Therefore, the fuzzy control-based the EASS in this research was applicable to various agricultural wheeled vehicles.

Study of an improved single neuron PID control algorithm in the Tokamak plasma density control system

Tokamak is an important device for controlled nuclear fusion research. The plasma electronic density control system (PEDCS) is an important system for controlling the Tokamak discharge process, which should be of high stability, rapidity, and accuracy. Gas seeding systems are widely used in many Tokamak devices to achieve plasma electronic density control. According to the mechanism model analysis for the plasma electronic density object, an adapted single neuron proportion integration differentiation (PID) control algorithm with the radial basis function (RBF) neural network tuning is studied. The principle and the implementation of the intelligent control algorithm are described in detail in this paper. The intelligent controller enables the system to optimize the PID parameters online according to the density state in the discharge process. The experimental results show that the adapted algorithm achieves a good control effect and also improves the control performance. The proposed method provides a useful reference for Tokamak devices and other similar control systems.

Design and Optimization of a New Index Finger Exoskeleton Robot with Underactuated Mechanism

Spinal cord injury and stroke are the main diseases that lead to loss or decline of hand function, and hand rehabilitation exoskeletons can be used to help restore hand function. Due to the problems of traditional hand exoskeletons such as heavier weight, higher cost and limited mobility, this project aims to improve an index finger exoskeleton robot with an underactuated mechanism, improve its bending and extension angles, and optimize the robot to a certain extent. flexibility. At the same time, the genetic algorithm is combined in the control system to make all the control indexes reach excellent. This project establishes a 3D model through Solidworks, and at the same time proves the feasibility of improvement by establishing a kinematic model. In terms of design, the mechanism is adjusted according to the range of activities of MCP(metacarpophalangian joint) and PIP(proximal interphalangeal joint) to optimize the flexibility of the design model. In the aspect of control system, the genetic algorithm is combined with system identification and PID(Proportion Integration Differentiation)control, in order to reach the objective of establishing a more precise training model and finding appropriate PID parameters. This project has a positive effect on the medical efficiency of the underactuated hand exoskeleton.

Design and implementation of Internet-of-Things software monitoring and early warning system based on nonlinear technology

Abstract In order to realize and design a software monitoring and early warning system for the Internet of Things (IoT), this paper establishes a “trinity” control platform integrating PLC, WINCC, and MATLAB based on nonlinear technology and realizes the proportion integration differentiation (PID) control based on the RBF neural network tuning on this platform. Based on the framework of the trinity control platform, the PID control system set by the radial basis function (RBF) neural network and the STEP7 virtual object programming of the control platform are designed and realized. The experimental data update cycle is 0.5 s to record 1,000 data item objects, U is the control quantity, which is associated with the U communication driver variable in WINCC, and the corresponding storage address in the PLC is MD200; Yout is the controlled quantity, which is related to the Yout communication driver variable in WINCC, and the corresponding storage address in the PLC is MD100; start is the control switch, associated with the start communication driver variable in WINCC, corresponding to the storage address in the PLC of M0.1; reset is the reset control switch, It is associated with the reset communication driver variable in WINCC, and corresponds to the storage address in the PLC as M0.0. KP, KI, KD, and TIME correspond to three real-time PID parameters and are the cycle time in MATLAB (used for the X-axis of trend graphing), and are the variables of the communication driver. The addresses in the PLC are MD20, MD24, MD28, and MD32. It shows that for these three software programs, the update cycle of the data in the respective storage areas must be consistent, the program control cycles in MATLAB and PLC need to be consistent, and the transmission of parameters must be correctly implemented in a control cycle according to the programming logic sequence, in order to realize the design of an IoT software monitoring and early warning system.

Research on Fuzzy Control Method of Automobile Antilock Braking System

The automobile Antilock Braking System (ABS) can prevent the wheel from locking by automatically adjusting the brake pressure at a high speed during emergency braking. While improving the braking effect of the car, it can also keep the steering ability of the car and ensure the safety of passengers. The automobile braking process has a strong nonlinearity and uncertainty, so exploring a simple and reliable control strategy is the focus of current research. Based on this, this paper proposes a variable-domain fuzzy proportional integral differential (PID) control strategy using a particle swarm optimization (PSO) algorithm to iteratively find the optimal theoretical domain. First, the PSO strategy is used to obtain the optimal regulation parameters. Then, the dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control, and the PID parameters and variable theory domain expansion factor are optimized by the PSO to increase the utilization degree of fuzzy rules initially set. Finally, compared with the traditional control strategy, the simulation and real vehicle test prove that the proposed system can significantly improve the control accuracy of abs. The proposed system has the advantages of small overshoot, short adjustment time, strong antiinterference ability, and practicability, which greatly improves the tracking performance of the ABS.

PID-Based Design of DC Motor Speed Control

DC motor is an easy-to-apply motor but has inconsistent speed due to the existing load. PID (Proportional Integral Differential) is one of the standard controllers of DC motors. This study aimed to know the PID controller's performance in controlling the speed of a DC motor. The results showed that the PID controller could improve the error and transient response of the system response generated from DC motor speed control. Based on the obtained system response data from testing and tuning the PID parameters in controlling the speed of a DC motor, the PID controller parameters can affect the rate of a DC motor on the setpoint of 500, 1000, 1500: Kp = 0.05, Ki = 0.0198, Kd = 0.05.

Design and experiment of multi-mode steering system of agricultural machinery for hilly and mountainous area

In order to improve the steering flexibility of agricultural machinery in hilly and mountainous areas, a multi-mode steering system with front wheel steering, rear wheel steering, and four-wheel steering has been developed. The hydraulic steering system based on load sensitivity principle and proportion-integration-differentiation (PID) controlling algorithm was designed, which overcomes the negative impact of external load changes on flow control accuracy. The mechanical-hydraulic-controlling coupling model established in the AMESim and the sequential quadratic combinatorial optimization algorithm (SQCOA) was adopted to obtain the optimal combination of PID parameters. The simulation results demonstrate that the parameters such as pressure, speed, displacement of hydraulic cylinders, etc. in different steering modes meet the design requirements. To examine and verify the system performance, the test platform was researched and developed for conducting steering radius and displacement measurement. The experimental data illustrated that the front and rear hydraulic cylinders have good synchronization accuracy in four-wheel steering mode, and the fast switch of steering mode can be realized. The maximum error rate of is steering radius 4.21% and 3.77%, respectively, in two-wheel steering and four-wheel steering modes. The research methods and conclusions can provide a theoretical basis and reference for the other steering system development.

PID self-tuning method based on deep belief network and improved firefly algorithm

In order to overcome the difficulty of tuning the proportion integration differentiation (PID) parameters, a PID parameter self-tuning method based on the firefly algorithm improved by Newton's law of universal gravitation (LOGFA) and deep belief network (DBN) is proposed. Compared with the FA, LOGFA cannot only maintain the evolutionary advantage of the original algorithm but also can effectively improve the accuracy and convergence ability of the algorithm. The advantage of DBN is to train each layer of neural network separately, which greatly improves the training efficiency and accuracy. The closed-loop PID speed control system of a three-phase asynchronous motor is used as the simulation object for PID parameter self-tuning. The proposed LOGFA-DBN is compared with other three algorithms. Simulation results show that the algorithm combining LOGFA and DBN can realise the off-line parameter tuning which is not subject to the controlled object, and speed up the parameter tuning.

Promoting the co-utilisation of glucose and xylose in lignocellulosic ethanol fermentations using a data-driven feed-back controller

BackgroundThe diauxic growth of Saccharomyces cerevisiae on glucose and xylose during cellulose-to-ethanol processes extends the duration of the fermentation and reduces productivity. Despite the remarkable advances in strain engineering, the co-consumption of glucose and xylose is still limited due to catabolite repression. This work addresses this challenge by developing a closed-loop controller that is capable of maintaining the glucose concentration at a steady set-point during fed-batch fermentation. The suggested controller uses a data-driven model to measure the concentration of glucose from ‘real-time’ spectroscopic data. The concentration of glucose is then automatically controlled using a control scheme that consists of a proportional, integral, differential (PID) algorithm and a supervisory layer that manipulates the feed-rates to the reactor accounting for the changing dynamics of fermentation.ResultsThe PID parameters and the supervisory layer were progressively improved throughout four fed-batch lignocellulosic-to-ethanol fermentations to attain a robust controller able of maintaining the glucose concentration at the pre-defined set-points. The results showed an increased co-consumption of glucose and xylose that resulted in volumetric productivities that are 20–33% higher than the reference batch processes. It was also observed that fermentations operated at a glucose concentration of 10 g/L were faster than those operated at 4 g/L, indicating that there is an optimal glucose concentration that maximises the overall productivity.ConclusionsPromoting the simultaneous consumption of glucose and xylose in S. cerevisiae is critical to increase the productivity of lignocellulosic ethanol processes, but also challenging due to the strong catabolite repression of glucose on the uptake of xylose. Operating the fermentation at low concentrations of glucose allows reducing the effects of the catabolite repression to promote the co-consumption of the two carbon sources. However, S. cerevisiae is very sensitive to changes in the glucose concentration and deviations from a set-point result in notable productivity losses. The controller structure developed and implemented in this work illustrates how combining data-driven measurements of the glucose concentration and a robust yet effective PID-based supervisory control allowed tight control of the concentration of glucose to adjust it to the metabolic requirements of the cell culture that can unlock tangible gains in productivities.

An intelligent controller design based on the neuroendocrine algorithm for the plasma density control system on Tokamak devices

Abstract Realization of real-time and accurate control of plasma electron density is one of the key points for the long-time steady-state operation of Tokamak. Based on the mechanism model of the neuroendocrine regulatory principle, this paper designs an intelligent controller and studies its application on the plasma density control system (PDCS) for the J-TEXT Tokamak. This PDCS uses the HCN laser interferometer to obtain the density signal, and controls the gas puffing valve through the proposed intelligent control algorithm to achieve accurate density control. The controller mainly includes a hypothalamic regulation module, a single neuron proportional-integral-differential (PID) control module and an ultrashort feedback module. It is designed and referenced to the long feedback, short feedback, and ultra-short feedback loop mechanisms of the human neuroendocrine hormone regulation. The proposed controller makes the PID parameters be adjusted on-line in real-time and achieves rapid and stable elimination of errors. The simulations and experimental results show that the proposed method, compared with the conventional PID control method, can synthetically improve the performance of the control system.

Improved Decoupling Control for a Powershift Automatic Mechanical Transmission Employing a Model-Based PID Parameter Autotuning Method

Automatic mechanical transmission (AMT) with a gearshift assistant mechanism is a novel transmission architect concept aiming to improve the torque interruption and driveline jerk of AMT. During the shifting process, the shifting performance deteriorates as the varying road gradient and the friction coefficient worsen the coupling effect between the motor torque and the clutch friction torque. This paper focuses on improving the controller’s robustness of AMT with a gearshift assistant mechanism against the perturbed parameters during the stage of torque gap filling. In this paper, a detailed powertrain simulation model was presented. Based on a decoupling controller and a disturbance compensator, proportional-integral-differential (PID) controllers are applied to enhance the robustness and the decoupling effect. The PID parameters are automatically tuned by employing the Nelder-Mead method. In the tuning process, a cost function was established to demonstrate the outputs’ reference tracking performance, and the PID parameters are tuned by minimizing the cost function. Finally, the tuned parameters are stored in PID maps to make them adjustable online. Simulation results show that with the perturbed parameters well estimated, the upshift process was successful and the torque filling effect was also acceptable. The proposed transmission is a promising structure for industry applications.

Application of PID optimization control strategy based on particle swarm optimization (PSO) for battery charging system

Abstract The battery charging process has nonlinear and hysteresis properties. PID (Proportion Integration Differentiation) control is a conventional control method used in the battery charging process. The control effect is determined by the PID control parameters ${K}_p$, ${K}_i$ and ${K}_d$. The traditional PID parameter setting method is difficult to give the appropriate parameters, which affects the battery charging efficiency. In this paper, the particle swarm optimization (PSO) is used to optimize the PID parameters. Aiming at the defects of basic PSO, such as slow convergence speed, low convergence precision and easy to be premature, a modified particle swarm optimization algorithm is proposed, and the optimized PID parameters are applied to the battery charging control system. Also, the experimental results show that the battery charging process possesses better dynamic performance and the charging efficiency of the battery has increased from 86.44% to 91.47%, and the charging temperature rise has dropped by 1°C.

Speed Control of DC Motor Using PID and Arduino

This paper is aim to research the Proportional Integral Differential (PID) controller design and selection of various Proportional, Integral and Differential control parameters. MATLAB is allows matrix manipulations, plotting of functions and data, implementation of algorithms. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems. MATLAB is used to implement the Proportional Integral Differential (PID) controller, it is used to control the speed of DC motor and bring it at the desired speed. PID parameters Kp , Ki and Kd are finely tuned to optimal values. And then a ARDUINO UNO microcontroller is programmed by adding the finely tuned PID algorithm to control the speed of DC motor.

Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

This paper aims to improve the control performance for a magnetorheological damper (MRD) based semi-active seat suspension system. The vibration of the suspension is isolated by controlling the stiffness of the MRD using a proportion integration differentiation (PID) controller. A new intelligent method is proposed to optimize the PID parameters in this work. This new method appropriately incorporates the particle swarm optimization (PSO) into the PID-parameter searching processing of an improved fruit fly optimization algorithm (IFOA). Thus the PSO-IFOA method possesses better optimization ability than IFOA and is able to find a globally optimal PID-parameter set. The PID controller optimized by the proposed PSO-IFOA was evaluated for attenuating the vibration of the MRD-suspension using a numerical model and an experimental platform, respectively. Both the simulation and experimental analysis results demonstrate that the proposed PSO-IFOA is able to optimize the PID parameters in controlling the MRD semi-active seat suspension. The control performance of the PSO-IFOA based PID is superior to that of individual PSO, FOA or IFOA based methods.

Dynamic load frequency control for high-penetration wind power considering wind turbine fatigue load

The frequency stability of power systems can be improved by the participation of high-penetration wind power in grid frequency regulation. However, the wind turbine (WT) fatigue load will be increased after the wind power participates in the load frequency control (LFC). This paper proposes a control method that takes into account the WT fatigue load and the frequency response of the power system. First, wind turbines are added to the two-areas LFC model including thermal power and hydropower models to participate in frequency regulation. And considering the fatigue load experienced by the tower bending moment and the low-speed shaft torque of the WT, a WT model capable of reflecting the main dynamics is established. Then, the model identification adaptive Proportional-Integral-Differential (PID) controller is designed to be applied to LFC of wind power participating in the grid considering both frequency response and fatigue load. In order to achieve better control effects, the differential evolution (DE) algorithm is used to optimize the PID parameters based on multi-cost functions. The controller parameters are optimized by considering both the fatigue load and frequency deviation. To verify the effectiveness of the proposed method, the controller is applied to the established model and the simulation results are compared.

Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization

The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learning algorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.

Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

The quantum bacterial foraging optimization (QBFO) algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant, which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO (IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity. The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integer-order PID controller, the fractional-order proportion integration differentiation (PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

Optimization Design by Genetic Algorithm Controller for Trajectory Control of a 3-RRR Parallel Robot

In order to improve the control precision and robustness of the existing proportion integration differentiation (PID) controller of a 3-Revolute–Revolute–Revolute (3-RRR) parallel robot, a variable PID parameter controller optimized by a genetic algorithm controller is proposed in this paper. Firstly, the inverse kinematics model of the 3-RRR parallel robot was established according to the vector method, and the motor conversion matrix was deduced. Then, the error square integral was chosen as the fitness function, and the genetic algorithm controller was designed. Finally, the control precision of the new controller was verified through the simulation model of the 3-RRR planar parallel robot—built in SimMechanics—and the robustness of the new controller was verified by adding interference. The results show that compared with the traditional PID controller, the new controller designed in this paper has better control precision and robustness, which provides the basis for practical application.

Decentralized PID Control Design for Magnetic Levitation Systems Using Extremum Seeking

A decentralized proportional–integral–differential (PID) control structure has been always the first option for magnetic levitation systems due to its model-free nature as well as the simplicity. It is very challenging to tune PID parameters for each suspension point to ensure that the closed-loop system is less sensitive to uncertainties/disturbances. Even though, by a large number of experiments, a reasonably good PID control parameter set can be obtained to levitate a single suspension point for a given set point, these parameters are very sensitive when the set point changes or the decentralized PID controller is applied to the half bogie where coupling exists, strong oscillations will be observed and retuning of parameters is needed. This paper utilizes the extremum seeking (ES) method to tune PID parameters on-line to improve the steady-state performance of the system with few oscillations. A “sampled-data” structure is used to obtain a moving window approximation of the steady-state behavior, leading to a discrete-time ES tuning for the decentralized PID parameters. By using projection method, the tuning parameters are constrained in a compact set, in which the stability can be guaranteed and the optimal performance can be achieved. Experiments have shown the effectiveness of the proposed method.