PID Controller Tuning Research Articles

Network dynamics of a periodically forced chemical system and its application for tuning PID controller with time-delay systems

Network dynamics of a periodically forced chemical system and its application for tuning PID controller with time-delay systems

Correction to: Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics

Correction to: Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics

IPDT Model-Based Ziegler-Nichols Tuning Generalized to Controllers with Higher-Order Derivatives.

The paper extends the earlier work entitled "Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable", to higher-order controllers and a broader range of experiments. The original series PI and PID controllers, based on automatic reset calculated by filtered controller outputs, are now augmented by higher-order output derivatives. This increases the number of degrees of freedom that can be used to modify the resulting dynamics, accelerates transient responses, and increases robustness to unmodeled dynamics and uncertainties. The fourth order noise attenuation filter used in the original work allows for the addition of an acceleration feedback signal, thus resulting in a series PIDA controller or even a jerk feedback that leads to a PIDAJ series controller. Such a design can further use the original process and filter approximation of the step responses through the integral-plus-dead-time (IPDT) model, while allowing experimentation with disturbance and setpoint step responses of the series PI, PID, PIDA and PIDAJ controllers, and thus, evaluating the role of output derivatives and noise attenuation from a broader perspective. All controllers considered are tuned using the Multiple Real Dominant Pole (MRDP) method, which is complemented by a factorization of the controller transfer functions to achieve the smallest possible time constant for automatic reset. The smallest time constant is chosen to improve the constrained transient response of the considered controller types. The obtained excellent performance and robustness allow the proposed controllers to be applied to a wider range of systems with dominant first-order dynamics. The proposed design is illustrated on a real-time speed control of a stable direct-current (DC) motor, which is approximated (together with a noise attenuation filter) by an IPDT model. The transient responses obtained are nearly time-optimal, with control signal limitations active for most setpoint step responses. Four controllers with different degrees of derivative with generalized automatic reset were used for comparison. It was found that controllers with higher-order derivatives may significantly improve the disturbance performance and virtually eliminate overshoots in the setpoint step responses in constrained velocity control.

Self-Tuning Pid (STPID) Control of pH in Recovering Process of Magnesium Hydroxide (Mg(OH)2) by Coagulation From Salt

The salt obtained from the salt sources are not desirable purity level and contains some impurities. In the scope of this study, coagulation of magnesium hydroxide (Mg(OH)2) in pH control has been carried out by adding sodium hydroxide (NaOH) to magnesium chloride (MgCl2) and by this way recovery process has been achieved by Self Tuning PID Control. To provide the best coagulation pH 11 has been obtained as optimum pH value in consideration of the performed calculations.

Solar PV Combined Efficient Torque Control of BLDC Motor Using Salp Swarm Optimization

This study provides an efficient torque and speed control method for brushless DC (BLDC) motor utilizing a fractional order proportional integral derivative (FOPID) controller based on the multiverse optimization algorithm and salp swarm optimization (SSO). When combined with a solar photovoltaic (PV) system and a voltage boosting architecture, standard PID controllers regulated the BLDC drive parameters due to their simplicity and improved steady-state performances. A solar PV system with a specific power range is chosen for the best output solution on the output side. The MPPT method employed is the incremental conductance approach for tracking maximum power from the source. However, it has a problem with unpredictability owing to load changes. The PID controller tuning also relates to the structure’s parametric uncertainties. Therefore, precise control methods may be offered with the aid of the FOPID controller to address the aforementioned issues. Simulation results of the proposed MVO and SSO-based FOPID controller for BLDC speeds run in the Matlab Simulink platform. To confirm the adaptability of the suggested control scheme for BLDC motor, the outcomes are interrelated using genetic algorithm, gray wolf, and PSO-based FOPID controllers.

A Cascade BP Neural Network Tuned PID Controller for a High-Voltage Cable-Stripping Robot

A 10 kV distribution network is a crucial piece of infrastructure to guarantee enterprises' and households' access to electricity. Stripping cables is one of many power grid maintenance procedures that are now quickly expanding. However, typical cable-stripping procedures are manual and harmful to workers. Although numerous automated solutions for grid maintenance have been created, none of them focus on cable stripping, and most of them have large dimensions to guarantee multi-functions. In this paper, a new cable-stripping robot for the 10 kV power system is introduced. The design of a live working cable-stripping robot that is appropriate for installing insulating rods is introduced, taking into account the working environment of 10 kV overhead lines and the structural characteristics of overhead cables. The robot is managed by an auxiliary remote control device. A cascade PID control technology based on the back propagation neural network (BPNN) method was developed, as the stripper robot's whole system is nonlinear and the traditional PID controller lacked robustness and adaptability in complex circumstances. To validate the structural feasibility of the cable-stripping robot, as well as the working stability and adaptability of the BPNN-PID controller, a 95 mm2 cable-stripping experiment are carried out. A comparison of the BPNN-PID controller with the traditional PID method revealed that the BPNN-PID controller has a greater capacity for speed tracking and system stability. This robot demonstrated its ability to replace manual stripping procedures and will be used for practical routine power maintenance.

A novel sequential switching quadratic particle swarm optimization scheme with applications to fast tuning of PID controllers

In this work, a sequential switching quadratic particle swarm optimization (SSQPSO) scheme is investigated, where the velocity update mechanism is improved to enhance the convergence performance. Considering the sequential characteristics (related to evolution factors) of the evolution process, a Markov chain with special probability transition matrix is employed to characterize the switching of evolution state. With the help of the mean distance, the concept of population density is first put forward in the dynamic search region enclosed by all particles. Then, taking into account the change of the population density in different generations, two quadratic acceleration terms are introduced into the velocity update model based on the Hadamard product, where four evolution-state-dependent acceleration coefficients are also adopted. The positivity or negativity of the quadratic acceleration terms is retained by resorting to the matrix sign functions. Several widely utilized benchmark functions (including two unimodal and multimodal functions) are employed to evaluate the search capability of the studied SSQPSO scheme. The experimental consequences illustrate that the performance of the developed SSQPSO scheme is superior to that of some popular particle swarm optimization (PSO) schemes. To further demonstrate the effectiveness in practical engineering, the addressed SSQPSO scheme is successfully applied to achieve the fast parameter tuning of the proportional-integral-derivative controller in a spring-mass-damper system.

Bayesian Optimization for automatic tuning of digital multi-loop PID controllers

In recent years, the use of Bayesian optimization (BO) for efficient automatic tuning of general controller structures through iterative closed-loop experiments, has been attracting increasing interest. However, its potential for tuning interactive multi-loop PID controllers in Multi Input Multi Output (MIMO) processes remains largely unexplored. Even though the optimization domain greatly affects closed-loop performance and safety, it is usually defined manually, through expert knowledge or experimentation. This paper presents a novel systematic methodology for defining the optimization domain for automatic multi-loop PID tuning using BO. Sequential loop closing, system identification and tuning relations are used to constrain the bounds on controller parameters to meaningful ranges, including gains and sampling times. This provides an effective way to improve the convergence of BO and secure process safety during closed-loop experiments, without requiring a MIMO process model or extensive prior knowledge. The methodology can be applied ”as is” to single-loop PID controllers.

Input-output scaling factors tuning of type-2 fuzzy PID controller using multi-objective optimization technique

<abstract><p>The PID controller is a popular controller that is widely used in various industrial applications. On the other hand, the control problems in microgrids (MGs) are so challenging, because of natural disturbances such as wind speed changes, load variation, and changes in other sources. This paper proposes an input-output scaling factor tuning of interval type-2 fuzzy (IT2F) PID controller using a multi-objective optimization technique. The suggested controller is applied to an MG frequency regulation problem. In the introduced controller the effect of variations of renewable energies (REs) and other disturbances are taken into account, and the robustness is investigated. In the multi-objective scheme, some factors such as least overshoot, and minimum settling/rising time are considered. The simulations show that by considering the suitable adjustment the desired regulation accuracy is achieved, such that the frequency trajectory shows the desired overshoot, and settling/rising time.</p></abstract>

Variable displacement axial-piston pump control by genetic algorithm tuned PID controller

The article present design, optimal tuning and experimental investigation of a multivariable PID controller intended for variable displacement axial-piston pump. The pump is designed for open circuit hydraulic drive systems. The regulation of the displacement volume of the pump is carried out through a proportional spool valve intended for its model. Unlike classic systems in which the valve receives control electrical signals from an analogue electronic amplifier, in this system the proportional valve receives a control signal generated by a specially developed embedded PID control system. The controller is designed on the base of a model obtained by means of system identification. The set of the controller is performed through an optimization procedure based on a genetic algorithm. Experimental studies were carried out with the designed controller on a laboratory test rig. А comparison between the experimental and simulation results is done.

Design of Fitness Function for Intelligent Parameter Tuning of PID Controller on Belt Conveyor with PSO

Design of Fitness Function for Intelligent Parameter Tuning of PID Controller on Belt Conveyor with PSO

Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems

Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms: Arithmetic optimization algorithm (AOA), Artificial Gorilla troop’s optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation Algorithm (PPA), and hybrid between AOA and GTO (HAOAGTO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error ( (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model. It is observed that the PPA technique achieves the highest values of best fitness value for simulation results among other control approaches, while the HAOAGTO approach reduces the best fitness function compared to other optimization techniques used. The results indicate that HAOAGTO is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.

Flight Controller Design for Altitude Control of a Quadcopter using PID and Fuzzy Methods

A quadcopter can be manually controlled or can be autonomous. Therefore, a control system is needed to support the quadcopter's movement. PID and fuzzy control are used in this research to adjust the quadcopter's altitude position. The control system proposed in this research is to keep the quadcopter at a certain altitude. The control system design uses a PID controller, where the control parameters are obtained using the Ziegler-Nichols 2 tuning method. Ziegler-Nichols 2 tuning method is used to obtain the parameters Kp, Ti, and Td. PID control tuning becomes a reference for fuzzy control design in determining input and output membership functions and the rules. Also, in this study, the flight controller board design is proposed. The experiment results show that the PID control has a faster rise time of 0.2 seconds, a better maximum overshoot of 1.56%, a quicker settling time of 1.69 seconds, and a steady-state error of 0% compared to the fuzzy control. The result shows that the use of PID control is more suitable for proposed quadcopter plants because it requires a fast output response.

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms.

Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group's use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model's ability for the researchers in robotic research.

PID-controller tuning algorithm development for a dynamical system “crane-load”

One of the main problems in the operation of cranes is the oscillation of the load on a flexible suspension. One of the ways to eliminate load oscillations on a flexible suspension is to use a proportional-integral-differential controller that generates a control signal for the crane movement. However, for it to function properly, it must be properly tuned. Standard approaches to tuning a PID controller, which is common in the practice of engineering calculations, do not allow solving this problem, and that is why it can be considered as a scientific and applied one. The research aims to develop an algorithm for tuning a proportional-integral-differential controller. For this purpose, a research issue was defined, which includes a mathematical model of the dynamic system, restrictions on the overload capacity of the crane drive and the control function, conditions for achieving the steady-state speed of the crane and eliminating pendulum oscillations of the load on a flexible suspension. Using the modified particle swarm method, ME-D-PSO, the coefficients of the proportional-integral-differential controller were determined for a wide range of values of the load mass and the length of the flexible suspension. Based on the obtained values of the coefficients, an algorithm is presented that allows calculating the values of the coefficients for any values of the cargo mass and the length of the suspension. The dynamics of the movement of the crane-load system are analyzed for the smallest and largest selected parameters and for the case obtained by applying the developed algorithm. Practical application of the developed algorithm will allow obtaining optimal values of the proportional-integral-differential controller, which in turn eliminates oscillations of the load on a flexible suspension during crane operation, which in turn increases the safety of crane operation, structural durability, and increases the crane’s productivity

Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm

Voltage regulation in automatic voltage regulator (AVR) system has been one of the most challenging engineering problem due to the uncertain load condition. Therefore, the control of AVR system by using PID based controller is one of the essential approach to maintain the performance of the AVR system. Subsequently, the application of FOPID controller in AVR system is gaining more attention recently. This is because the FOPID has additional control parameters at the derivative and integral parts than the PID controller, which has the advantage to improve the output response of AVR system while retaining the robustness and simple construction as the PID controller. Nevertheless, many existing optimization tools for tuning the FOPID controller, which are based on multi-agent based optimization, require large number of function evaluation in their algorithm that could lead to high computational burden. Therefore, this study proposes a modified smoothed function algorithm (MSFA) based method to tune the FOPID controller of AVR system since it requires fewer number of function evaluation per iteration. Moreover, the proposed MSFA based method also can solve the unstable convergence issue in the original smoothed function algorithm (SFA), thus able to provide better convergence accuracy. The simulations of step response analysis, Bode plot analysis, trajectory tracking analysis, disturbance rejection analysis, and parameter variation analysis are conducted to evaluate the effectiveness of the proposed MSFA-FOPID controller of AVR system. Consequently, the results obtained from the simulations revealed that the proposed method is highly effective and significantly improved as compared to the other existing FOPID controllers.

Modelling and simulations of modified slime mould algorithm based on fuzzy PID to design an optimal battery management system in microgrid

Due to the advantages of renewable energy sources (RESs), are integrated into the grid as a means to reduce pollution caused by the burning of fossil fuels. To achieve this successful operation various controllers are used to perform an optimal operation of RESs. As we know that the microgrid, on the other hand, is a particular kind of energy network that can run in either grid or island mode depending on the circumstances of the power supply. So, the significance of this study is to perform microgrid operation well, for that a novel modified slime mould algorithm (MSMA) based on fuzzy PID control is used to design an optimal battery management system. Because the battery energy storage system is essential for the steady functioning of microgrid voltage stabilization. The objective can be tackle at firstly, such techniques are essential for reducing the uncertainty of the automatic voltage variation and power management, with proper tuning of the fuzzy PID control. With the aid of MSMA, such an algorithm can regulate uncertainty issues that arise from the RESs. Lastly, with the help of MATLAB 2021b simulation, model the photovoltaic energy source with wind energy source generators based microgrid system. Which can further test how better the proposed controller made using the battery energy management-based MSMA approach worked under different loads, to validate the objectives.

The automatic generation controller based on whale optimization algorithm in PV-thermal power systems

Güç sistemlerinde üretilen güç ile tüketilen güç arasında denge sağlanması amacıyla otomatik üretim kontrolü (OÜK) adı verilen bir kontrol sürecinin gerçekleştirilmesi gerekmektedir. Rüzgâr türbinleri (RT) ve fotovoltaik (FV) paneller gibi yenilenebilir enerji kaynaklarının (YEK) kullanımının artması bu süreci daha zor hale getirmektedir. Bu nedenle, OÜK sürecinin daha hassas bir şekilde gerçekleştirilmesi gerekmektedir. Bu çalışmada, fotovoltaik (FV) sistem ve termal generatörden meydana gelen hibrit bir güç sisteminde OÜK işlemi için balina algoritması (BA) kullanılarak PID kontrolörün parametrelerinin belirlenmesi amaçlanmıştır. Alan-1 ve alan-2’de meydana gelen yük değişimleri durumunda amaçlanan BA ayarlı PID kontrolörün performansının yeterliliği incelenmiştir. Ayrıca, ateşböceği algoritması (ABA), genetik algoritma (GA) ve aşırı nüfus optimizasyonu (ANO) gibi optimizasyon tekniklerinin performanslarıyla bir karşılaştırma yapılmıştır. Elde edilen sonuçlar, çalışmada önerilen BA ayarlı PID kontrolörün, sistem frekansının aşma değeri ve oturma süresi bakımından diğer yöntemlere göre daha iyi sonuçlar verdiğini göstermiştir.

RETRACTED ARTICLE: Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics

RETRACTED ARTICLE: Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics

A New Strategy for PI Tuning in Photovoltaic Irrigation Systems Based on Simulation of System Voltage Fluctuations Due to Passing Clouds

One of the greatest challenges in stand-alone photovoltaic irrigation systems (PVIS) without batteries is the tuning of PID controllers and the evaluation of their performance once the system is tuned. Tuning method must be applied in clear days (constant irradiance) while performance must be evaluated in the most unfavourable circumstances, which occur when the passage of a cloud causes a sudden drop in available power. In short, tuning and testing must be done under different weather conditions. To solve this problem, a tuning method that is complemented by a method to simulate voltage fluctuations due to cloud passage has been developed. This allows tuning and evaluation of the system’s performance in the same session. Furthermore, the new PI tuning method achieves a better adjustment of the parameters and solves the instability problems that arise when applying traditional closed-loop tuning methods. Both methods use the feedforward input that most variable frequency drivers have. A signal generator is used to carry out the simulation of the clouds. This input is also used to introduce a triangular signal used for the tuning of the PI controller. The results show that the performance of the system, characterized by the voltage of the PV generator, with simulated clouds is similar to the response with real clouds. With regard to the tuning, the new method achieves better performance than previous methods. These methods can be applied on clear days, under conditions of constant irradiance, which greatly simplifies its implementation and greatly reduces the time required for commissioning the system.