Tuning Of Proportional Integral Derivative Parameters Research Articles

A Temperature Control Method of Lysozyme Fermentation Based on LRWOA-LSTM-PID

In order to overcome the difficulty of parameter tuning caused by the large lag and time-varying nonlinearity of the tank for lysozyme fermentation, a temperature control method based on LRWOA-LSTM-PID is proposed in this paper. Firstly, according to the intrinsic mechanism of the fermenter, a temperature mechanism model based on a dynamic equation is designed, which can better reflect the temperature changes in the fermenter. Secondly, a Proportional Integral Derivative (PID) parameter tuning method based on a Long-Short Term Memory Network (LSTM) is proposed, which takes advantage of the ability of LSTM to learn time sequence information and obtains the variation trend between error sequences under continuous time sampling, thereby adjusting network weights more reasonably and accelerating PID parameter tuning. Finally, a Whale Optimization Algorithm (WOA) based on the Lévy flight and random walk strategy (LRWOA) is proposed for the initialization of LSTM parameters; this algorithm has excellent optimization capabilities and overcomes the problem of LSTM falling into local optimal solutions prematurely during parameter randomization. The results show that the method proposed in this paper can achieve rapid tuning of PID parameters, thereby improving the convergence speed of the system and reducing system overshoot.

Review Work on Modelling & Simulation of Car Cruise Control System using PID Controller

This work focuses on the modeling and simulation of car cruise control systems with the implementation of a Proportional-Integral-Derivative (PID) controller. The primary objective is to develop a comprehensive model that integrates a PID controller for maintaining car speed under varying conditions. The methodology involves detailed modeling techniques to simulate car dynamics and the tuning of PID parameters for optimal control

Ant colony optimization-based adjusted PID parameters: a proposed method

The ant colony algorithm (ACA) is a heuristic algorithm that resolves the optimality problem by simulating an ant’s foraging process, which finds the shortest path. The connotation of the ACA is to find the optimal solution. The Proportional Integral Derivative (PID) parameter tuning is an essential tool in the control field and includes three parameters, Kp, Ki, and Kd, to achieve the best control effect. Besides, tuning the PID parameters is closely related to finding the “optimal” solution that can be attained based on the feasible combination of the two. This article transforms the PID parameter tuning problem into an ACA that finds the optimal solution called ACA-based PID parameters tuning. Furthermore, PID control is simulated by setting the parameters of ACA, such as ant colony size, iteration times, nodes, paths, path evaluation criteria, pheromone concentration, heuristic function, weight factor, and decision function. Eventually, the two PID controller parameter tuning strategies are compared and analyzed, and the advantages and disadvantages of each are obtained. Compared with the 4:1 attenuation curve method, the proposed method can significantly reduce the MP score of the overshoot of the system, increase the time, and improve the dynamic and steady-state performance of the system, but reduce the steady-state error of the system. Therefore, the feasibility and effectiveness of the proposed method is verified.

Development of a Matlab-Based Fuzzy PID Controller and The Simulation

Fuzzy PID control is a process control technique combining the advantages of fuzzy logic and PID control. It maintains precise control over processes by combining fuzzy logic and proportional-integral-derivative (PID) controllers. With this method, it is possible to achieve higher levels of accuracy and stability in controlling process variables such as temperature, pressure, flow rate, and position. This makes Fuzzy PID control an ideal cordial application requiring precise process control. Fuzzy PID control uses the PID parameter tuning experience to make the fuzzy controller automatically tune its parameters, giving PID control a better control effect. In this paper, a fuzzy PID controller is designed to control the temperature of an electric boiler. The control effects of PID control, fuzzy control, and fuzzy PID control are compared by simulation analysis. The simulation results show that the unclear PID control effect is ideal and has a good application prospect.

Design Decoupling Controller based on Feedforward and Improved PID for Tension System

It is difficult to precisely control the substrate tension due to the tension system of roll-to-roll coating machine with strong coupling and time-varying parameters. Therefore, a feedforward improved proportional integral derivative (PID) parameters self-tuning decoupling controller of tension system is proposed in this paper. According to the components and working principle of the coating machine, the nonlinear coupling mechanism model of the global tension system is established, and the model is linearized by using the Taylor series. Based on the linear model, a feedforward improved PID parameters self-tuning decoupling controller is designed for tension system. Through the feedforward control compensates the modeled tension and speed interference, a tracking differentiator (TD) is used to improve the performance of PID, and the online self-tuning of PID parameters can be realized by an Radial Basis Function (RBF) identification network. The simulation results show that the proposed decoupling control strategy of tension system can well suppress the coupling interference between the units, and realize the real-time tuning of PID parameters.

A parallel compact firefly algorithm for the control of variable pitch wind turbine

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by the flickering behavior of fireflies. Due to its excellent performance, it has been widely used in engineering field.​ However, FA is a population-based algorithm, which needs to occupy a lot of running memory. It is adverse for some small wind turbines or scenarios with limited memory Therefore, this paper proposes an improved firefly algorithm, called parallel compact firefly algorithm (PCFA). The compact technique helps FA save operation memory, which is advantageous for some usage scenarios limited memory. And the parallel technique helps compact FA achieve better solutions and faster convergence. The proposed PCFA was tested on 28 benchmark functions and applied to the proportional–integral–derivative (PID) parameter tuning of the variable pitch wind turbine. Results demonstrate that (1) PCFA is superior to common compact optimization algorithms not only on less memory consumption, but also on more competitive solutions and faster convergence. (2) PCFA shows the better applicability in the PID parameter tuning of variable pitch wind turbine. It can availably smooth the power output of wind turbine under less memory consumption.

Parameter optimization of unmanned surface vessel propulsion motor based on BAS-PSO

Despite advances in modern control theory and artificial intelligence technology, current methods for tuning proportional-integral-derivative (PID) controller parameters based on the traditional particle swarm optimization (PSO) algorithm do not meet the requirements for controlling an unmanned surface vessel (USV) propulsion motor. To overcome the disadvantages of the PSO algorithm, such as low precision and easily falling into a local optimum, the beetle antennae search (BAS) algorithm can be introduced into the PSO algorithm by replacing particles with beetles, and effectively prevents the PSO algorithm from easily falling into the local optimum. At the same time, the BAS algorithm will no longer be limited to single objective parameterization. Herein, we propose a PID parameter optimization method based on the hybrid BAS-PSO algorithm for a USV propulsion motor. The PID parameter optimization of propulsion motor effectively becomes a beetle foraging problem with group optimization. Numerical results show that the method can effectively solve the problems of PSO and greatly improve convergence speed. Compared with the genetic algorithm and standard PSO algorithm, the BAS-PSO algorithm is superior for PID parameter tuning and can improve performance of USV propulsion system.

Numerical and experimental verification of the single neural adaptive PID real-time inverse method for solving inverse heat conduction problems

For the problems of difficult on-line tuning of PID parameters, weak anti-interference ability and poor self-adaptive ability in the classical proportional-integral-derivative (PID) inverse method, combining single artificial neuron with PID inverse algorithm, a single neural adaptive PID (SNA-PID) inverse method is established to estimate the thermal boundary condition. Compared with the classical PID inverse method, the main difference is that SNA-PID can adaptively adjust the weights of PID parameters. In the SNA-PID inverse method, firstly, the deviation signal between the output of the direct heat conduction model and the measured temperature and its first-order difference and second-order difference are taken as three inputs of a single neuron (dendritic input signal of neuron), and the connection strength of each input signal is determined according to the updated weight of the adaptive learning algorithm, The excitation value of the boundary heat flux increment of the heat conduction system is generated according to the linear truncation function. Finally, the real-time estimation of boundary heat flux of heat conduction system is realized. The unsteady boundary heat flux of one-dimensional heat conduction object is estimated through numerical and experimental tests, which verifies the effectiveness of the proposed SNA-PID inverse method.

Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.

A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm

The PID (proportional–integral–derivative) controller is the most widely used control method in modern engineering control because it has the characteristics of a simple algorithm structure and easy implementation. The traditional PID controller, in the face of complex control objects, has been unable to meet the expected requirements. The emergence of the intelligent algorithm makes intelligent control widely usable. The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a new evolutionary algorithm. Compared with other intelligent algorithms, the QUATRE algorithm has a strong global search ability. To improve the accuracy of the algorithm, the adaptive mechanism of online adjusting control parameters was introduced and the linear population reduction strategy was adopted to improve the performance of the algorithm. The standard QUATRE algorithm, particle swarm optimization algorithm and improved QUATRE algorithm were tested by the test function. The experimental results verify the advantages of the improved QUATRE algorithm. The improved QUATRE algorithm was combined with PID parameters, and the simulation results were compared with the PID parameter tuning method based on the particle swarm optimization algorithm and standard QUATRE algorithm. From the experimental results, the control effect of the improved QUATRE algorithm is more effective.

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control.

This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

Proportional Integral Derivative (PID) controller is one of the most classical controllers, which has a good performance in industrial applications. The traditional PID parameter tuning relies on experience, however, the intelligent algorithm is used to optimize the controller, which makes it more convenient. Fish Migration Optimization (FMO) is an excellent algorithm that mimics the swim and migration behaviors of fish biology. Especially, the formulas for optimization were obtained from biologists. However, the optimization effect of FMO for PID control is not prominent, since it is easy to skip the optimal solution with integer-order velocity. In order to improve the optimization performance of FMO, Fractional-Order Fish Migration Optimization (FOFMO) is proposed based on fractional calculus (FC) theory. In FOFMO, the velocity and position are updated in fractional-order forms. In addition, the fishes should migration back to a position which is more conducive to survival. Therefore, a new strategy based on the global best solution to generate new positions of offsprings is proposed. The experiments are performed on benchmark functions and PID controller. The results show that FOFMO is superior to the original FMO, and the PID controller tuned by FOFMO is more robust and has better performance than other contrast algorithms.

Decentralized PID Controller Tuning Based on Nonlinear Optimization to Minimize the Disturbance Effects in Coupled Loops

Decentralized proportional-integral-derivative (PID) control systems are widely used for multiple-input multiple-output (MIMO) control problems. However, decentralized controllers cannot suppress the plant interactions in multivariable systems, which are addressed in the controller tuning phase. In this paper, a decentralized PID tuning method is proposed in order to minimize the undesirable effects of the coupling between the inputs and outputs of the closed-loop system. For this purpose, the PID parameter tuning method solves a nonlinear optimization problem. This optimization problem is formulated with the criteria of the performance, robustness and multivariable stability of the closed-loop system. A single design parameter is required to specify the trade-off between performance and robustness. Simulation studies are conducted to demonstrate the effectiveness of the proposed method. The performance is compared to that of alternative tuning techniques from the literature. Results show that the proposed approach is a feasible candidate for industrial application, as it is simple to implement and capable of addressing robustness and stability concerns of plant operators.

Design of PI Controller for PMSM using Chaos Particle Swarm Optimization Algorithm

The proportional-integral-derivative (PID) controller parameters tuning is critical for improving the performance of permanent magnet synchronous motor (PMSM) systems. To improve the performance of particle swarm optimization (PSO) in the PID parameters tuning, the chaos particle swarm optimization (CPSO) is adopted to tune PI parameters in this work. The PMSM vector control system is selected as the research object, and its speed loop PI controller using CPSO algorithm is implemented. The optimal PI controller parameters are obtained using the CPSO algorithm with ten iterations. The simulation is conducted using MATLAB/Simulink to evaluate to the PI controller performance. Four performance indicators including overshoot, rise time, peak time and ITAE are used to evaluate the PI controller with CPSO, and the results suggest that the CPSO algorithm has better performance and stability compared with other tuning methods.

DC Motorun Otomatik Ayarlamalı PID ile Hız Kontrolünün Gerçekleştirilmesi

Although advanced controllers are used in control applications, proportional-integral-derivative (PID) controllers are preferred in industry due to their simple structure and ease of application. However, it can difficult to set these controller parameters for the controlled platform. These parameters tuning with trial-error method leads to time and job loss, and the parameters determined in this way cannot provide a sufficiently efficient operating characteristic. In order to overcome this problem related to PID parameter tuning many automatic tuning methods have been developed. In this paper, the automatic tuning method proposed by Aström and Hagglund was applied to a DC motor speed control system. The DC motor speed control system was implemented in an interface designed on Laboratory Virtual Instrument Electronic Workbench (LabVIEW) environment and CompactRIO unit. The PID parameters obtained with trial-error and two types auto-tuning methods were tested in the DC motor control system and achieved results were compered. The results showed that performance of the PID controller tuned with LAbVIEW auto-tuning method is better than others.

Design of a Performance-Adaptive 1-Parameter Tuning PID Controller

Many self-tuning Proportional—Integral—Derivative (PID) controllers have been proposed. However, these methods are rarely employed to actual systems because of low reliability of an on-line PID parameter estimator. In this work, a performance adaptive controller that has two stages of PID parameters tuning mechanisms is proposed. One of the tuners is a 1-parameter tuner using the recursive least squares. The other tunes PID parameters using ordinary least squares. The effectiveness of the proposed method is evaluated by an experimental example.

Study on PID tuning strategy based on dynamic stiffness for radial active magnetic bearing

The development of industry technology requires magnetic bearings to work in high speed conditions. However, the current stiffness and displacement stiffness of the magnetic bearing will decrease significantly due to the consequent eddy current effect, and that decrease will make the system unstable and even result in the rotor drop and instrument damage. Therefore, the traditional Proportional-Integral-Derivative (PID) method based on constant stiffness is not adaptable for high speed conditions. This paper proposes a PID parameters tuning strategy based on dynamic stiffness for the radial active magnetic bearing (RAMB). The dynamic stiffness model under eddy current effect is established by analyzing the equivalent magnetic circuit model in which parameters are frequency-dependent. The PID parameters tuning method for RAMB control system including dynamic stiffness model is put forward according to the characteristic equation and Routh-Hurwitz criterion. Different PID parameters are set in simulations and several corresponding experiments are conducted. Satisfactory control effects consistent with the theoretical analysis are obtained and thus the proposed PID tuning strategy is verified to be good. Simulations and experiments in this paper provide theoretical guidance for the design of controller parameters and have research significance for structural optimization of RAMB.

The Method for Restraining Laser Drift Based on Controlling Mirror

Based on piezoelectric ceramics (PZT), a feedback control method for compensating beam drift is proposed in this paper. The actuator can control the pitch and yaw angle of the light simultaneously, and its structure is very simple. The angular drift of laser is detected and separated in the designed optical path. The influence of interference noise is suppressed by moving average filter, and then, the beam drift is compensated through controlling mirror by PZT to suppress its influence on the stability of alignment measurement. The real-time control is realized by using proportional–integral–derivative (PID). The method of relay feedback is used to realize automatic tuning of PID parameters. Compensated by this method, the drift of quadrant photodiode detector (QPD1) in X and Y directions was reduced by 87 and 44%, respectively; that of QPD2 was reduced by 29.7 and 28.6%, respectively; and that of QPD3 was reduced by 78 and 70.3%, respectively. So the laser angular drift in the measuring optical path is well suppressed.

A contemporary review of the applications of nature-inspired algorithms for optimal design of automatic generation control for multi-area power systems

The modern electric grid is one the most complex man-made control systems. Proportional–integral–derivative (PID) controllers are widely used in a variety of applications including automatic generation control (AGC), automatic voltage regulators, power system stabilizers and flexible AC transmission system devices. Automatic generation control plays an important role in power system operation to maintain the frequency within an acceptable range and to properly respond to load changes under normal conditions. Using the PIDs, AGC keeps the balance between generation and load demand in order to minimize frequency deviations. Furthermore, the AGC regulates the tie-line power exchange and facilitates bilateral contracts spanning over several control areas, thus ensuring reliable operation of the interconnected transmission system. Since the power system load variations occur continually, generation control is set to automatic to restore the frequency after disturbances. The PID controllers have the advantage of simple structure, good stability, and high reliability. However, a robust and efficient tuning of PID parameters are still being investigated using different techniques. One of the recent areas of such studies is nature-inspired algorithms. The main objective of utilizing nature-inspired algorithms is to optimize parameters of several controllers simultaneously. This paper reviews the latest applications of various nature-inspired algorithms for optimal design of AGC control in power systems. Different algorithms, proposed in the recent literature, are classified based on the type of controller, objective function and test systems.

Advanced Fireworks Algorithm and Its Application Research in PID Parameters Tuning

Proportional-Integral-Derivative (PID) controller is one of the most widely used controllers for its property of simplicity and practicability. In order to design high-quality performances PID controllers, an Advanced Fireworks (AFW) algorithm based on self-adaption principle and bimodal Gaussian function is proposed, which is built to optimize the PID controller by parameters tuning. Firstly, a compound index of optimization performance is formulated, and then the extremal optimization method of PID control system is proposed. Secondly, a PID parameters tuning model combined with AFW is built. At last, 5 typical transfer functions are simulated to obtain optimal parameters by AFW and contrast tuning method, such as Ziegler-Nichols method, Enhanced Fireworks (EFW) algorithm, and Particle Swarm Optimization (PSO). Simulation results show that AFW are effective and are easily implemented methods to solve PID control problems of different transfer functions.