PID Parameters Research Articles

Survey on the quality of automatic parameter detection of PID controller for DC motor using Genetic Algorithm and Particle Swarm Optimization

This article presents the results of a study on selecting optimal PID parameters tuned by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) used for a DC motor. The simulating controller response results show that the PID - GA and PID - PSO combination algorithms are superior to traditional methods. The result also allows for the selection of the optimal algorithm - combining the PSO - PID to design a controller that has smaller settling error but larger overshoot and settling time compared to GA-PID method. The simulation was taken in Matlab environments

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID

Brushless DC (BLDC) motors, which have small volumes, are widely used in many areas from the aviation industry to industrial applications due to their high efficiency and torque. In parallel with the development of technology, the field of use continues to expand with the development of BLDC engine (BLDCM) control strategies and the decrease in control costs. In this thesis study, it is aimed to minimize the observed changes in rotor speed compared to the reference speed. To achieve this, PID parameters were tried to be changed simultaneously with fuzzy control techniques, taking the error value as a reference. The control system of the BLDC engine was designed in the MATLAB/Simulink environment. In the simulation, the operating stability of classical PID and PID with updated fuzzy-based parameters on two engines with the same features was compared at different speeds. As a result of the research, it was concluded that the correction of the speed observed in the rotor of the PID-controlled motor, whose fuzzy logic-based coefficients were updated, based on the reference speed was more stable and the percentage of exceedance for the reference value was lower, compared to the classical PID controlled motor.

Hybrid Sine Cosine and Particle Swarm Optimization Algorithm for High-Dimensional Global Optimization Problem and Its Application

Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way.

Domain Invariant Learning for Gaussian Processes and Bayesian Exploration

Out-of-distribution (OOD) generalization has long been a challenging problem that remains largely unsolved. Gaussian processes (GP), as popular probabilistic model classes, especially in the small data regime, presume strong OOD generalization abilities. Surprisingly, their OOD generalization abilities have been under-explored before compared with other lines of GP research. In this paper, we identify that GP is not free from the problem and propose a domain invariant learning algorithm for Gaussian processes (DIL-GP) with a min-max optimization on the likelihood. DIL-GP discovers the heterogeneity in the data and forces invariance across partitioned subsets of data. We further extend the DIL-GP to improve Bayesian optimization's adaptability on changing environments. Numerical experiments demonstrate the superiority of DIL-GP for predictions on several synthetic and real-world datasets. We further demonstrate the effectiveness of the DIL-GP Bayesian optimization method on a PID parameters tuning experiment for a quadrotor. The full version and source code are available at: https://github.com/Billzxl/DIL-GP.

Design and Implementation of an Optimized PID Controller for Two-Limb Robot Arm Control

With developing technology, robot arms are used in more areas, and this is directly proportional to the work done for its development. Studies on robot arms are generally focused on control. The controllability of robot arms generally provides speed and precision.
 Within the scope of this study, the control optimization of a two-arm robot arm with an optimized proportional integral differential controller (PID) was carried out using a microcontroller. The kinematic operations required to control a two-arm robot arm have been developed by the MATLAB Support Package for Arduino Hardware. The transfer function required for the control system was used for a direct current (DC) brushed motor, using the values given in the motor data sheet. Feedback is provided for the control system thanks to the Hall effect encoder.
 The gripper end of the two-limbed robot arm follows the specified square-shaped reference. In this study, where PID controller was used, controller parameters were obtained with particle swarm, artificial bee colony and chaos game metaheuristic optimization algorithms for square orbit and these parameters were used on the produced robot arm.
 Many methods have been used in the literature to determine PID parameters. In this study, the chaos game metaheuristic optimization algorithm, which has become popular in recent years, was used to determine the parameters of the PID controller.

Current study on PID control method with optimized BP neural network based on particle swarm optimization

At present, robots show great application value in various industries, and the progress of the industry constantly puts forward higher requirements for the performance of robots, and robot control is an important part of robot application. It is difficult for the traditional PID controller to implement online tuning when it is faced with actual objects. Consequently, the neural network algorithm has been included into robot control methods in recent years; the PID controller based on BP neural network is the subject of this research. This study presents the current state of research, the basic concept behind BP neural networks, how they are used in controller design, and how to optimize the PID parameters of BP neural networks using a particle swarm optimization method. To increase system stability, the neural network is coupled with a PID controller. The adaptive learning capability of the neural network is utilized to modify PID control parameters online in real time. Targeting the trouble that it slips into a local minimum easily during the BP-PID self-learning process and the refined PSO algorithm is used to improve it. It makes sure that the BP-PID system converges to the global optimal solution. The proposed method can effectively improve the system control accuracy and control stability.

Towards an optimized heat transfer process in vaporizing liquid microthrusters using pulsed heating control

The Institute for Microelectronics and Microsystems of the Italian National Research Council (Italy) has designed and fabricated a water-fed MEMS vaporizing liquid microthruster (VLM) in collaboration with the University of Salento (Italy) and KU Leuven (Belgium). Previous studies have demonstrated the device’s functioning and highlighted the presence of a strong thermoelectric-hydraulic coupling that severely affects the heating and propulsive efficiencies and reliability of the device. In this regard, the current work provides a preliminary experimental investigation of a pulsed heating system actively controlled by temperature using PI-D logic (proportional and integrative actions applied to the error; derivative action applied to the output). The overall performance assessment focuses on evaluating power consumption during steady-state operation and analyzing the dynamic response of the VLM undergoing such an actively controlled pulsed heating, which has been built using both manual and data-driven offline adaptive tuning of the PID parameters. Concerning the steady-state operation, results highlight a promising enhancement of the heating efficiency to values above 0.9. The dynamic response analysis shows that operating with a single set of fixed PID parameters is not feasible and that the manual tuning is a trial-and-error approach highly dependent on the operator’s experience, operating conditions, and reference temperature profile. Furthermore, shorter rise and response times require a higher proportional gain, and the overshoot experienced when crossing the saturation temperature cannot be avoided. In this paper, we show that the response of the data-driven adaptive controller solves these issues, exhibiting a reduced maximum settling time from around 59 s to less than 10 s, while the overshoot is avoided thanks to the temporal adjustment of the PID parameters.

Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor

Exoskeleton-aided active rehabilitation is a process that requires sensing and acting upon the motion intentions of the user. Typically, force sensors are used for this. However, they increase the weight and cost of these wearable devices. This paper presents the methodology for detecting users’ intentions only with encoders integrated with the drives. It is unique compared to other algorithms, as enables active kinesiotherapy while adding no sensory systems. The method is based on comparing the measured motion with the one computed with the idealised model of the multibody system. The investigation assesses the method’s performance and its robustness to model and measurement inaccuracies, as well as patients’ unintended motions. Moreover, the PID parameters are selected to provide the optimal regulation based on the dynamics requirements. The research proves the presented concept of the control approach. For all the tests with the final settings, the system reacts to a change in the user’s intention below one second and minimises the changes in proportion between the system’s acceleration and the generated user’s joint torque. The results are comparable to those obtained by EMG-based systems and significantly better than low-cost force sensors.

Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Stable trajectory tracking of unmanned aerial vehicles (UAVs) in complex environments is of paramount importance for achieving high precision and robustness in flight missions. In this paper, we address the attitude control problem of quadrotor UAVs and propose an optimization method based on adaptive PID control strategy. This text first presents an overview of the current status of UAVs both domestically and internationally, followed by the establishment of a mathematical model for quadrotor UAVs. Next, analysing the application of the traditional PID algorithm in UAV attitude control and provide a detailed description of the principles behind genetic algorithms and simulated annealing algorithms, along with their application in optimizing PID parameters. Through simulation experiments conducted in strong wind conditions, we compare the performance of the traditional PID algorithm with the optimization algorithm in stable trajectory tracking tasks. The experimental results demonstrate that the optimization algorithm significantly enhances the flight stability and accuracy of UAVs. Finally, this text summarizes the research findings and provide an outlook on future development directions.

Optimization of basic PID control algorithm based on genetic algorithm and Matlab

This paper investigates how to optimize the basic PID algorithm by using population genetic forms. "In traditional PID control, the tuning of PID parameters mostly relies on the experience of the tuner. The main purpose of this paper is to use an algorithm that can automatically tune its parameters to self-tune a PID controller for an actual model." Based on the principle of genetic algorithm, this paper compares the traditional PID controller tuning methods to study the trend of the PID parameters during the iteration process as well as the trend of the PID controller adaptability. Through the principle of the algorithm and the iterative output, as well as in the code set the weights occupied by different performance indicators, it can not only flexibly adjust the degree of adaptation and make the algorithm more flexible, but also prove that the use of genetic algorithms for the rectification of the PID is more adaptable and convenient, with promising research prospects

Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator

Aiming at the current complex problem of the mechanized high-quality picking of tender tea buds, this paper designs a tender tea bud-picking manipulator. In the picking process, the quality of the petiole and leaf blade of the tender tea bud is crucial, as the traditional cutting picking method destroys the cell structure of the tender tea buds, resulting in rapid oxidation of the cuts, thus losing the bright green appearance and pure taste. For this reason, this paper draws on the quality requirements of tender tea buds and traditional manual picking technology, simulating the process of the manual picking action, putting forward a ‘rotary pull-up’ clamping and ripping picking method, and designing the corresponding actuating structure. Using PVDF material piezoelectric thin-film sensors to detect the clamping force of the tender tea bud picking, the corresponding sensor hardware circuit is designed. In addition, the finite element analysis method is also used to carry out stress analysis on the mechanical fingers to verify the rationality of the automatic mechanism to ensure the high-quality picking of tender tea buds. In terms of the control of the manipulator, an SMC-PID control method is designed by using MATLAB/Simulink 2021 and Adam 2020 software for joint simulation. The way to control the closed-loop system angle and angular velocity error feedback is by adjusting the PID parameters, which quickly converts the sliding mode control to the sliding mode surface. The simulation results show that the SMC-PID control method proposed in this paper can meet the demand in tender tea bud picking and simultaneously has high control accuracy, response speed, and stability.

BP-Adaptive PID Regulation for Constant Current and Voltage Control in WPT Systems

To enhance the stability and disturbance rejection of wireless charging systems for electric vehicles, we designed a bilateral collaborative control strategy based on BP neural networks, achieving closed-loop constant voltage control for the secondary rectification circuit. Integrating BP neural network adaptive PID parameters with dual-phase-shift control, this strategy outperforms conventional incremental PID controllers in terms of response time and overshoot. Validated on an 11 kW experimental platform, our approach demonstrated efficient response under disturbances; with a load switch from 10 Ω to 12 Ω, the system exhibited a mere 5% fluctuation rate and an impressive efficiency of up to 92.96%.

Utopian robust efficiency indicator for robust PID multi-objective tunning

This paper describes the robust design of a multi-loop PID temperature control of a PEMFC stack. A multi-objective optimization approach is used, considering uncertainties in the dynamic model parameters. A new quality indicator in the optimization process, utopian robust efficiency, is defined for tuning PID parameters. To compare this approach, the alternative designs based on optimizing the worst case and the system performance in the nominal model are aborded. The results show that the controllers tuned based on the utopian robust efficiency exhibit a better trade-of between optimal performance in the nominal model and robustness in the presence of uncertainty.

Modeling study of boiler using oil waste as an energy source

Used oil waste is one type of waste that can be used as a source of energy. Waste oil that is used in this research is used as energy source for plasma burner and boiler plants. This final project is aimed at modeling a boiler with 3 input variables and 3 output variables (MIMO - Multiple Input Multiple Output). It's known that this MIMO system still exhibits interactions among the variables. To mitigate these interactions, a decoupler is designed. Subsequently, PID controllers are designed for this MIMO system. The PID parameter values are obtained through modeling using the Ziegler-Nichols method. The modeling is carried out based on the values obtained from the Ziegler-Nichols calculations, which are then implemented in Simulink within MATLAB. The test results indicate that employing a decoupler reduces the interactions between the systems. Therefore, Ziegler-Nichols and heuristic method is needed to find the PID parameter values. By utilizing the heuristic method, the system achieves stability with the value of Kp = 2,1, Ki = 0,005, dan Kd = 0 for the first variable, Kp = 0,0315 Ki = 0, dan Kd = 0 for the second variable, and Kp = 0,007 Ki = 0, dan Kd = 0 for the third variable..

A PID Control Architecture Based on IEC 61499

This work presents an approach for PID control and optimization in a distributed system, using the IEC 61499 control standard. This standard enables communication among different PLCs, which are used to develop a three-layered event-driven control of a SISO loop. The lowest layer is in charge of cyclical data acquisition. The second layer carries out an event-based PID control. The highest layer runs a control optimization algorithm, specifically a simple tuning approach based on Ziegler-Nichols that is used to determine the PID parameters for different operating points. The proposed approach is assessed in a tank level SISO control problem, whose behavior can be modeled as a first-order plus dead time system in each operating point. For that purpose, it has been implemented using two PLCs and a software PLC running on an industrial computer. The experimental results on the SISO level control loop show the feasibility of the proposed approach for event-driven control in a distributed system and open interesting research questions in the intersection of controller design and distributed automation.

PID control of data-driven patient response with fixed ratio co-administration of drugs for depth of hypnosis

In this work we make use of knowledge from clinical practice to deliver a trusted environment for feedback control of co-administration of two drugs to induce depth of hypnosis during general anesthesia. The clinical data we have provides valuable insight into the ratio used to determine the Propofol and Remifentanil infusion rates by means of specifying their corresponding effect site concentrations. A feedback closed loop with a fixed parameter PID controller tuned on population dynamics is used. To account the strong variations in gain of the system (patient) we propose an online identification of the dose-effect response from clinical data available during clinical protocol. Theoretical analysis is provided to justify the approach and simulations with real data from patient are given to support the theoretical insight. Limitations of this approach are discussed as to justify why ratio based co-administration is used in practice solely during the induction phase of general anesthesia.

Control of High-altitude Power Grid Inspection UAV Using Hierarchical PID Based on Reinforcement Learning

Unmanned aerial vehicles (UAVs) are now being utilized in a wide range of work scenarios to replace humans in performing dangerous tasks and jobs. In the context of high-altitude power grid inspections, UAV inspections have significant advantages. However, the complex environment often poses challenges for controlling UAVs. Building upon traditional PID control, this paper uses MATLAB software to construct the dynamic model and controller of a quadcopter UAV. A cascaded loop is established by selecting the control objects for the inner and outer loops. The stabilization of the UAV is achieved by setting the PID parameters for the inner and outer loops. Additionally, the deep reinforcement learning algorithm is employed to fine-tune the PID parameters based on the Zigler-Nicholes method. The adjustment is performed by considering the reward function values for the UAV under different disturbances. Finally, response curves and dynamic response parameter indicators are obtained. Compared to traditional cascaded PID control, the quadcopter UAV system tuned using this method exhibits significant improvements in both dynamic response parameters and steady-state responses.

Design a Controller Based on Smith Predictor by Direct Synthesis for Speed Control DC Motor

The modified Smith predictor is designed for a speed control DC motor. A speed controller of a DC motor by selection of PID parameters using a direct synthesis method. Here, the model of a DC motor is considered as a second-order system for speed control. The PID and PD controller structure reported recently decouples set-point tracking from disturbance rejection. The aim of this work is to design a speed controller of a DC motor by selection of proper PID. Simulation examples show that improved servo and regulatory performances are achieved by the proposed method as compared to the normal tune PID method, and also check by perturbed performance. When used for regulatory/servo purposes, a controller optimized for servo/regulatory application significantly degrades performance.

Path Planning and Trajectory Tracking Control for Two-Wheel Mobile Robot

The mobile robot is a system that can work in various environments. This means that the robot must be able to navigate without delay and avoid any obstacles placed within the boundaries of its movement. Designing mobile robots that can be intelligently managed and operate autonomously when traveling from one place to another requires at least two steps. To start with, path planning is required to prevent motion collisions. Tracking the robot's trajectory is a crucial second task. The primary goal of this study is to find the quickest and safest path between the two positions. In this work, we investigated the path planning of a mobile robot with dynamic, and dynamic obstacles with moving goal environments using RRT, BiRRT, and HA* algorithms. These algorithms are easy, computationally inexpensive, and simple to use. They have been chosen for numerous real-time path-planning applications. The DDMR's kinematic model has been utilized in this paper to control path tracking, and a PID controller has been proposed to reduce tracking deviations between the robot's actual route and the reference trajectory. This work introduced the PSO, FPA, CSA, SSA, BWOA, and proposed HBPO optimization techniques for obtaining PID parameters (k_p,k_i,k_d) for improved mobile robot trajectory tracking. The simulation results have been examined using three trajectory shapes: step, circular, and infinite. The simulation findings reveal that HA* outperforms the other algorithms by generating collision-free pathways that are smoother and shorter than their RRT and BiRRT equivalents. On the other hand, the proposed HBPO outperforms the other methods. The HBPO method converges quicker than the other proposed algorithms.

Optimal PID control with anti-windup in neutralization process

PID control, which is a type of automation, was used to ensure that neutralization takes place in a controlled manner. To determine the PID parameters of the system with the Cohen-Coon tuning method, two different dynamic experiments were carried out for pH and temperature in the first stage, and the transfer function and model parameters were found. In the experiment carried out for the pH variable; time constant (τ) is 59 s, dead time (Ɵ) is 261 s and steady state gain (K) is read from the graph as 14,72, while PID parameters are calculated as KC =0.0375, τI =315.759 s and τD =52.601 s. Likewise, while τ=1402 s., Ɵ= 88 s, and K=-6 were read for the temperature variable, the PID parameters were calculated as KC =6.196, τI =47.23 s, and τD =-19.20 s. The determined controller parameters were used as initial parameters and simulated using the S-function block via MATLAB (2007b). The pH set range was coded as 6.5-8.5 and Tset for temperature was coded as 22 °C. As a result of the oscillation observed due to the nature of the PID control parameters that are intended to be controlled, the safe operation of the process and the desired set values are ensured. When the obtained PID controller parameters were applied to the neutralization reaction, the PID control successfully controlled the reactor temperature and pH and eliminated possible hazards in operation. Anti-windup provides better control rather than traditional PID control method.