Proportional-integral-derivative Parameters Research Articles

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.

A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant

Solid oxide fuel cells (SOFCs) are promising electrochemical devices which translate chemical energy directly into electric energy with high efficiency and low pollution. However, the control of the output voltage of SOFCs is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. Nowadays, proportional-integral-derivative (PID) controllers are commonly utilized in industrial control systems for their high reliability and simplicity. However, it will lead to overshoot and windup issues when used in the wide-range operation of SOFCs. This paper aims to improve the PID controller performance based on fuzzy logic by (1) identifying a linear model based on the least squares method; (2) optimizing the PID parameters based on the generated linear model; and (3) designing a fuzzy adaptive PID controller based on the optimized parameters. The simulation results of the conventional PID controller and the fuzzy adaptive PID controller are compared, demonstrating that the proposed controller can achieve satisfactory control performance for SOFCs in terms of anti-windup, overshoot reduction, and tracking acceleration. The main contribution of this paper can be summarized as: (1) this paper identifies the SOFC model and uses the identified model as a control object to optimize conventional PID controllers; (2) this paper combines a fuzzy logic control scheme and PID control scheme to design our proposed fuzzy adaptive PID controller; and (3) this paper develops an anti-windup structure based on a back-calculation method to reduce saturation time and overshoot.

Tree physiology optimization on SISO and MIMO PID control tuning

The tuning of proportional–integral–derivative (PID) controller is essential for any control application in order to ensure the best performance by step change or disturbance. This paper presents the tuning of PID controller for single-input single-output (SISO) and multiple-input multiple-output (MIMO) control systems using tree physiology optimization (TPO). TPO is a metaheuristic algorithm inspired from a plant growth system derived based on the idea of plant architecture and Thornley model (TM). The basic principle of TM simplifies the plant growth into shoots and roots part. The plant shoots grow towards sunlight with the help of nutrients supplied by the root system in order to undergo photosynthesis process, a process of converting light photon into carbon. The carbon gain from the shoots extension will be supplied to the root system in order for the root to grow and search for water plus nutrients. As a result, the nutrients are supplied upwards towards shoot system for further extension. This concept runs iteratively in order to ensure optimum plant growth. The iterative search of shoot towards better light supported by the root counterparts leads to an optimization idea of TPO algorithm. TPO also has a unique exploration strategy due to its multiple branches and shoots that can be defined by user. This concept may improve the search mechanism with a better trade-off between diversification and intensification search. A simulation of SISO control system and an industrial application of MIMO control are applied to demonstrate the effectiveness of the proposed algorithm and compared with other optimization methods such as particle swarm optimization, Ziegler–Nichols, Tyreus–Luyben and Chien–Hrones–Reswick methods. The results clearly exhibit the capability of TPO algorithm towards finding the optimum PID parameters for SISO and MIMO process with faster settling time and better performance with respect to other methods.

A model predictive functional control based on proportional-integral-derivative (PID) and proportional-integral-proportional-derivative (PIPD) using extended non-minimal state space: Application to a molten carbonate fuel cell process

The performance of most controllers, including proportional-integral-derivative (PID) and proportional-integral-proportional-derivative (PIPD) controllers, depends upon tuning of control parameters. In this study, we propose a novel tuning strategy for PID and PIPD controllers whose control parameters are tuned using the extended non-minimal state space model predictive functional control (ENMSSPFC) scheme based on the auto-regressive moving average (ARMA) model. The proposed control method is applied numerically in the operation of the MCFC process with the parameters of PID and PIPD controllers being optimized by ENMSSPFC based on the ARMA model for the MCFC process. Numerical simulations were carried out to assess the set-point tracking performance and disturbance rejection performance both for the perfect plant model, which represents the ideal case, and for the imperfect plant model, which is usual in practical applications. When there exists uncertainty in the plant model, the PIPD controller exhibits better overall control performance compared to the PID controller.

Time delay control of cable-driven manipulators with artificial bee colony algorithm

Cable-driven manipulators (CDM) are widely-used for their unique advantages such as light weight, low moving mass, high payload-to-weight ratio, and large reachable workspace. However, their complex dynamic character and low stiffness with flexible joints make the control design much more difficult than for traditional robot manipulators. In this paper, time delay control (TDC), which combines the proportional-integral–derivative (PID) control method and time delay estimation (TDE) technology, will be investigated to build a model-free controller for CDM. PID parameters are reduced dramatically as TDE compensates for a large proportion of unknown dynamics. To handle the problem in tuning parameters of this controller, artificial bee colony (ABC) algorithm is utilized to obtain optimal parameters of PID. Finally, simulations are conducted to verify the effectiveness of the propose controller and the tuning method.

Novel PID-Fuzzy Video Rate Controller for High-Delay Applications of the HEVC Standard

The new High Efficiency Video Coding (HEVC) standard is targeted for high-resolution video contents that are suitable for many high-delay video communications applications, such as video streaming. A few-rate control algorithms (RCAs) have been proposed for the HEVC in the literature. Almost all state-of-the-art RCAs proposed for the HEVC are optimized for constant bit rate low-delay applications. In order to reach a higher visual quality in high-delay applications, such as video streaming, a proportional integral derivative (PID)-fuzzy RCA with the buffering constraint is proposed for the HEVC in this paper. The proposed RCA is designed to minimize fluctuations of quantization parameter (QP) while the buffering constraints are fully obeyed. Unlike conventional fuzzy-PID algorithms that use a PID controller for control and a fuzzy system for tuning the PID parameters, the proposed algorithm uses a fuzzy controller with proportional, integral, and derivative inputs. A base QP is calculated for each group of picture (GOP) by the PID-fuzzy controller and determined QP offsets are added to the base QP to obtain the QP of frames at different temporal layers in the GOP. Experimental results indicate that high rate-distortion performance, fulfilled buffering constraint, and the high visual quality of compressed video are gained simultaneously by the proposed algorithm with low computational complexity.

Frog-inspired jumping robot actuated by pneumatic muscle actuators

Jumping robots can be widely applied in archeology, interstellar probe, antiterrorism, and resource exploration. This article adopts the frog as the bionic object and briefly analyzes biological features and jumping movements in a complete jumping sequence of the frog. Then an equivalent six-bar mechanism model depicting frog jumping movement is built up, and the optimizing simulation of the six-bar mechanism model is performed. On the basis of the optimized results, the robot is designed and prototype is manufactured. An adaptive cascade controller is proposed to control the robot. Proportional–integral–derivative parameters of the controller are tuned online using radial basis function neural network to deal with nonlinearities of the robot. Jumping experiment demonstrates the jumping capacity of the robot and the ability of the proposed controller in tracking the desired trajectories.

Air Condition’s PID Controller Fine-Tuning Using Artificial Neural Networks and Genetic Algorithms

In this paper, a Proportional–Integral–Derivative (PID) controller is fine-tuned through the use of artificial neural networks and evolutionary algorithms. In particular, PID’s coefficients are adjusted on line using a multi-layer. In this paper, we used a feed forward multi-layer perceptron. There was one hidden layer, activation functions were sigmoid functions and weights of network were optimized using a genetic algorithm. The data for validation was derived from a desired results of system. In this paper, we used genetic algorithm, which is one type of evolutionary algorithm. The proposed methodology was evaluated against other well-known techniques of PID parameter tuning.

Robust Performance of PID Controller by using Artificial Intelligence

This paper is meant to design method for determining the optimal proportional-integral-derivative (PID) controller parameters of plant system using the particle swarm optimization (PSO) algorithm and bacterial Foraging Optimization (BFO). There are several methods which are used to tune the controller parameters. They are categorized into two types known as classical methods and modern methods. In this paper the use of PSO method tuned the PID parameter to make them more general and to achieve the steady state error limit, also to improve the dynamic behaviour of the system. The performance and design criteria of automatic selection of controller constants are discussed below.

Adaptive PID control system with assigned robust stability

This paper presents a proportional‐integral‐derivative (PID) control system design method for discrete‐time first‐order plus dead‐time plants. Because the relation between the tracking performance and the robust stability is the trade‐off, the PID parameters of the PID control system are decided such that the tracking performance is optimized subject to the assigned robust stability, where the reference or disturbance response is selected as the tracking performance. In the proposed method, the PID parameters decision method is designed using a neural network with an extreme learning machine. Hence, the optimal PID parameters are obtained based on the trade‐off between the tracking performance and the robust stability. Furthermore, the proposed design method is extended as the adaptive control, and hence an unknown or time‐varying plant, is well controlled using the proposed method. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Stability Analysis of a Force-Aided Lever Actuation System for Dry Clutches with Negative Stiffness Element

A force-aided lever with a preload spring is not only force-saving but also energy-saving. Therefore, it has great potential to be applied to dry clutch actuations. However, the negative stiffness of the clutch diaphragm spring introduces unstable dynamics which becomes more intensive due to the preload spring. In order to explore the intensified unstability, this paper builds dynamic models for the rotating lever coupling a negative stiffness diaphragm spring and a preload spring. The stability analysis using the Routh-Huiwitz criterion shows that the open-loop system can never be stable due to the negative stiffness. Even if the diaphragm spring stiffness is positive, the system is still unstable when the preload of the spring exceeds an upper limit. A proportionalintegral- derivative (PID) closed-loop scheme addressing this problem is designed to stabilize the system. The stability analysis for the closed-loop system shows that stable region emerges in spite of the negative stiffness; the more the negative stiffness is, the less the allowed preload is. Further, the influences of the dimensions and PID parameters on the stability condition are investigated. Finally, the transient dynamic responses of the system subjected to disturbance are compared between the unstable open-loop and stabilized closed-loop systems.

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.

Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm

The concentration of dissolved oxygen (DO) in the aeration tank(s) of an activated sludge system is one of the most important process control parameters. The DO concentration in the aeration tank(s) is maintained at a desired level by using a Proportional-Integral-Derivative (PID) controller. Since the traditional PID parameter adjustment is not adaptive, the unknown disturbances make it difficult to adjust the DO concentration rapidly and precisely to maintain at a desired level. A Radial Basis Function (RBF) neural network (NN)-based adaptive PID (RBFNNPID) algorithm is proposed and simulated in this paper for better control of DO in an activated sludge process-based wastewater treatment. The powerful learning and adaptive ability of the RBF neural network makes the adaptive adjustment of the PID parameters to be realized. Hence, when the wastewater quality and quantity fluctuate, adjustments to some parameters online can be made by RBFNNPID algorithm to improve the performance of the controller. The RBFNNPID algorithm is based on the gradient descent method. Simulation results comparing the performance of traditional PID and RBFNNPID in maintaining the DO concentration show that the RBFNNPID control algorithm can achieve better control performances. The RBFNNPID control algorithm has good tracking, anti-disturbance and strong robustness performances.

Incorporation of stochastic fractal search algorithm into efficient design of PID controller for an automatic voltage regulator system

Although most accepted control algorithm is of proportional + integral + derivative (PID) structure in automatic voltage regulator (AVR) systems due to its effectiveness and simplicity, it is still a challenging task for researchers to tune its parameters efficiently. It is therefore of great interest, and constructs the motivation of this paper to undertake this task by incorporating the recently introduced, powerful optimization technique named as stochastic fractal search (SFS), for the first time in literature, which has the feature of increased accuracy and reduced convergence time. After optimizing the controller gains of Kp, Ki, and Kd, their performance is checked by using transient response analysis and then compared with those offered by recently published studies that use six competitive algorithms for achieving an efficient AVR system. To appraise the stability of the concerned power system tuned by SFS algorithm, root locus analysis and bode analysis are conducted along with the robustness analysis, which examines the system response to variations in the system parameters. Obtained comparative results affirm the excellence of SFS algorithm in achieving better PID parameters, and accordingly this yields an improved voltage response and minimized integral of time squared error in the output voltage profile.

Speed control of switched reluctance motor using genetic algorithm and ant colony based on optimizing PID controller

Proportional-Integral-Derivative control is the most used kind of control which provides the simplest and most effective solution to different kinds of control engineering applications. But until now PID controller is poorly tuned in real life and online applications. While most of PID tuning is done manually. Switched reluctance motor (SRM) has highly nonlinear characteristics since the developed/produced torque of the motor has a nonlinear function on both phase current and rotor position. These nonlinearities of the SRM drives make the conventional PID (proportional + integral + Derivative) controller a poor choice for application where high dynamic performance is desired under all motor operating conditions. research paper comes up with two artificial and hybrid techniques involving Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Those techniques where used to tune the PID parameters for the switched reluctance motor (SRM) and its performance were compared with the conventional method of “Ziegler Nichols. The results obtained reflects that, the use of those algorithms based controller improves the performance of the whole process in terms of a fast set point tracking and regulatory changes and also provides an optimum stability for the system itself with a minimum overshoot on the output signal.

IMC based PID Control Applied to the Benchmark PID18

In this paper an Internal Model Control (IMC) based proportional-integral-derivative (PID) control is presented and evaluated on the benchmark system presented at the 3rd IFAC Conference on Advances in Proportional-Integral-Derivative Control (PID 18). The controller is designed based on the model of the benchmark system. Its performance is compared with a computer-aided design tool based on frequency response (FRtool) and against the benchmark reference controller. The results show that the proposed method has a better performance due to the fact that IMC based PID parameters depend totally on the model.

Robotic excavator trajectory control using an improved GA based PID controller

Abstract In order to achieve excellent trajectory tracking performances, an improved genetic algorithm (IGA) is presented to search for the optimal proportional-integral-derivative (PID) controller parameters for the robotic excavator. Firstly, the mathematical model of kinematic and electro-hydraulic proportional control system of the excavator are analyzed based on the mechanism modeling method. On this basis, the actual model of the electro-hydraulic proportional system are established by the identification experiment. Furthermore, the population, the fitness function, the crossover probability and mutation probability of the SGA are improved: the initial PID parameters are calculated by the Ziegler-Nichols (Z-N) tuning method and the initial population is generated near it; the fitness function is transformed to maintain the diversity of the population; the probability of crossover and mutation are adjusted automatically to avoid premature convergence. Moreover, a simulation study is carried out to evaluate the time response performance of the proposed controller, i.e., IGA based PID against the SGA and Z-N based PID controllers with a step signal. It was shown from the simulation study that the proposed controller provides the least rise time and settling time of 1.23 s and 1.81 s, respectively against the other tested controllers. Finally, two types of trajectories are designed to validate the performances of the control algorithms, and experiments are performed on the excavator trajectory control experimental platform. It was demonstrated from the experimental work that the proposed IGA based PID controller improves the trajectory accuracy of the horizontal line and slope line trajectories by 23.98% and 23.64%, respectively in comparison to the SGA tuned PID controller. The results further indicate that the proposed IGA tuning based PID controller is effective for improving the tracking accuracy, which may be employed in the trajectory control of an actual excavator.

Control Design of Robotic Manipulator Based on Quantum Neural Network

In this study, a control system was designed to control the robot's movement (The Mitsubishi RM-501 robot manipulator) based on the quantum neural network (QNN). A proposed method was used to solve the inverse kinematics in order to determine the angles values for the arm's joints when it follows through any path. The suggested method is the QNN algorithm. The forward kinematics was derived according to Devavit–Hartenberg representation. The dynamics model for the arm was modeled based on Lagrange method. The dynamic model is considered to be a very important step in the world of robots. In this study, two methods were used to improve the system response. In the first method, the dynamic model was used with the traditional proportional–integral–derivative (PID) controller to find its parameters (Kp, Ki, Kd) by using Ziegler Nichols method. In the second method, the PID parameters were selected depending on QNN without the need to a mathematical model of the robot manipulator. The results show a better response to the system when replacing the traditional PID controller with the suggested controller.

Control of Single-Axis Rotation INS by Tracking Differentiator Based Fuzzy PID

In order to improve the ability of rotating turntable to resist the external disturbance in the single-axis rotation modulation inertial navigation system (SRMINS) and ensure smooth running of the turntable, we designed a dual closed-loop controller. The conventional proportional-integral-derivative (PID) algorithm was employed in the position loop, and a tracking differentiator based fuzzy PID algorithm was proposed and used in the speed loop, to realize the steady speed control of continuous rotation in the SRMINS. The introduction of the tracking differentiator (TD) guarantees the reliability of the differential signal extraction. Furthermore, the combination of the TD and the fuzzy adaptive PID controller leads to the smooth motion of the turntable without overshoot. The robustness and better stability margin of the system can also be achieved while the static error is eliminated and the performance of fuzzy control is improved. Simulation results demonstrate that the system has many merits such as fast response, no overshoot, and better robustness. It is also verified that regulating the PID parameters in real time by the fuzzy control rules effectively improves the dynamic and static performance of the system, and the requirement on stable rotation speed of the SRMINS is satisfied.

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder–Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.