Self-tuning PID Controller Research Articles

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Simplified dynamic modeling and Fuzzy gain scheduled PID control of woodward-governor controlled grid-interactive twin-shaft gas turbine plants

Biomass-integrated gas turbines for electricity generation are competitive with coal and nuclear energy production. Gas turbine-based power generation system looks to be an excellent solution amid the emerging environmental conditions and the increased energy crisis. This paper presents a simplified dynamic analysis model for biomass-based, Woodward governor-controlled, twin-shaft heavy-duty gas turbine (HDGT) plants, alongside an intelligent controller for stability enhancement. Grid-connected HDGTs can become unstable under load disturbances, potentially causing system shutdowns. A simplified model for twin-shaft gas turbines, rated from 18.2 MW to 106.7 MW, has been identified based on control characteristics during startup. The speed controller is dominant, while the acceleration controller is only active at startup, and the temperature controller's effect is minimal during normal operation. This model suits all dynamic studies of twin-shaft gas turbines, regardless of varying power ratings and rotor time constants. For improving the grid stability, a Takagi-Sugeno-Kang (TSK) fuzzy gain scheduling PID controller has been proposed in this work and its behavior is validated with 5001M, 7001Ea, and 9001Ea models. Step response of fuzzy PID controller under load disturbances and set-point variations are compared with fixed gain PID controllers tuned by Ziegler-Nichols (ZN) and performance index-based tuning using the typical gas turbine model. Further the controller behavior is validated with field test-based model parameters as derived from the real-world combustion turbines used in Alaskan Railbelt system. Extensive research simulation and validation results reveal that the fuzzy self-tuning PID controller showed superior adaptability for grid-interactive twin-shaft HDGTs under load variations and set-point variations. Time domain specifications and the performance indices confirms that the proposed fuzzy tuned PID controller ensure reliable and stable operation for the energy management in grid-operative mode. The proposed simplified model and intelligent control logic enable various dynamic studies in grid-connected environments for both simple cycle and combined cycle operations.

Adaptive fixed-time PID-based control of uncertain nonlinear systems and its application to unmanned surface vehicles

This article shows that the PID control, applied in linear systems, is introduced to handle the adaptive fixed-time PID-based control issue of uncertain nonlinear systems. For handling smooth complex unknown nonlinearities existing in the considered system, neural networks will be introduced to estimate these functions. Furthermore, by integrating the self-tuning gain PID controller, an actual controller is proposed. The stability analysis and the simulations all demonstrate the utilisability of the given scheme.

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources.

Modelling and simulation of MR damper characterization and uncertainty assessment in marine engine vibration isolation with FLPID control

ABSTRACT This paper meticulously examines the magneto-rheological (MR) damper to exploring its application in a semi-active isolator system. The MR damper’s distinctive capability to regulate damping force through an externally applied magnetic field is investigated via extensive experimental testing. Sinusoidal displacement inputs with varying electromagnet currents reveal a direct correlation between higher current levels and augmented MR damper forces. Validation of the experimental results is achieved through the Viscous Plus Dahl Model, showcasing its accuracy in capturing the damper’s hysteresis behavior. The study extends its scope to practical implementation by integrating the MR damper into a marine diesel engine system. A fuzzy logic-based self-tuning PID controller collaborates with the MR damper to establish a semi-active vibration isolator. The integrated model, encompassing primary and secondary masses, the engine, and the isolator with passive and semi-active systems, is rigorously analyzed. Experimental validation and theoretical simulations, aligned with data gathered from experiments, affirm the model’s fidelity in representing the MR damper. Simulation outcomes underscore the significant enhancement in vibration reduction characteristics of the semi-active isolator system compared to its passive counterpart, particularly evident in the improved performance of the 1200 kg engine under diverse operating conditions.

Self-Tuning Controller Using Shifting Method

This paper presents a newly implemented self-tuning PID controller that uses a relay feedback identification using a recently designed relay shifting method to determine the mathematical model of the process and subsequently adjust the controller parameters. The controller is applicable to proportional and integrating systems and is even applicable to systems with transport delays if steady-state oscillation can be achieved in the relay control of the system. After briefly introducing the relay shifting method, the current paper describes the hardware (HW) and software (SW) of the proposed controller in detail. The relay feedback identification and control of a laboratory setup by an automatically tuned controller is demonstrated on a real laboratory device called “Hot air tunnel”. The evaluation of the experiment and the characteristics of the controller are presented at the end of the paper. The advantage of the relay method is that it is not as computationally intensive as other identification methods. It can thus be implemented on more energy-efficient microcontrollers, which is very important nowadays.

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

Research on Variable Universe Fuzzy Multi-Parameter Self-Tuning PID Control of Bridge Crane

The bridge-type bridge crane is a common lifting equipment used in modern factories and workshops. During the crane’s operation, the positioning of the trolley and the swing of the load can significantly impact the bridge crane’s safety and reliability. In this paper, we propose a variable universe fuzzy multi-parameter self-tuning PID (VUFMS-PID) control strategy for controlling the trolley’s movement. Our control strategy uses scaling factor variation to dynamically adjust the number of fuzzy control rules based on the system error and error rate of change. This approach improves control accuracy and enhances the crane’s stability and safety. Simulation results demonstrate that our proposed control strategy outperforms both the fuzzy PID and traditional PID control strategies. Specifically, it reduces the crane trolley’s positioning time and the maximum swing angle of the load. Our control strategy exhibits good adaptive ability and robustness, which further improves the stability and safety of the bridge-type bridge crane operation.

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.

A New Self-Tuning Nonlinear PID Motion Control for One-Axis Servomechanism with Uncertainty Consideration

This paper introduces a new study for one-axis servomechanism with consideration the parameter variation and system uncertainty. Also, a new approach for high-performance self-tuning nonlinear PID control was developed to track a preselected profile with high accuracy. Moreover, a comparison study between the proposed control technique and the well-known controllers (PID and Nonlinear PID). The optimal control parameters were determined based on the COVID-19 optimization technique. The parameters of the servomechanism system changed randomly at a preselected range through the online simulation. The change of these parameters acts as the nonlinearity resources (friction, backlash, environmental effects) and system uncertainty. A comparative study between the linear and nonlinear models had been accomplished and investigated. The results show that the proposed controller can track several operating points with high accuracy, low rise time, and small overshoot.

Intelligent VIV control of 2DOF sprung cylinder in laminar shear-thinning and shear-thickening cross-flow based on self-tuning fuzzy PID algorithm

A self-tuning fuzzy PID (ST-FPID) control scheme is implemented within a joint interactive (Matlab/Simulink/Fluent) co-simulation framework for effective two degrees of freedom (2DOF) vortex-induced vibration (VIV) control of an elastically-mounted circular cylinder in laminar cross-flow of incompressible non-Newtonian power-law fluids based on the control action of a single transverse force actuator. The model-free controller, which systematically tunes the control parameters online in real time based on given rules, is well-known to be highly advantageous over the previously employed conventional PID controllers. It is particularly capable of handling the intricate non-linear dynamic effects inherent in the complex fluid rheology of non-Newtonian flow past the cylinder in presence of unmodeled system dynamics, high parametric uncertainties, diverse operational conditions, and time-varying external disturbances and control signals. Extensive numerical simulations reveal that the complex shear-thinning and shear-thickening behaviors of fluid viscosity can substantially influence the cylinder dynamic response, applied hydrodynamic forces, and flow structure. In particular, effectiveness and high performance of the adopted ST-FPID control strategy in substantial suppression of the high amplitude coupled 2DOF VIV of the elastically-mounted cylinder at selected critical reduced velocities in the lock-in region are established for a wide range of power-law index parameters (e.g., up to 83% reduction in RMS value of cylinder cross-flow displacement and up to 35% reduction in RMS value of cylinder in-line displacement for n=1and U* = 5 at Re = 100). Also, the vigorous action of the error-driven ST-FPID controller in forcing the high strength vortex shedding patterns of the uncontrolled cylinder out of the lock-in condition into the classical von Kármán vortex street of 2S-type mode of moderately weaker strengths is verified.

Hybrid Intelligent Controller Design and Stability Analysis for Controlling Boeing-767 Dynamics using Matlab

The main objective of this research is to provide better controlling and stability to B767 aircraft, where a study of elevators for pitch control for the described longitudinal movement is conducted. Intelligent controllers are designed and introduced to the surfaces of the linearized model of B767 aircraft, to identify the performance level based on time response and stability analysis by using different transfer functions. Self-tuning PID Controller and designed hybrid intelligent controller with a combination of PID controller and fuzzy controller are proposed along with few sensors installation to obtain results. A comparative assessment and quantitative analysis of controllers are carried out using MATLAB Simulink. This research has shown that the performance of the aircraft has improved significantly.

2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model

2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model

Simulation on control system of the parallel ankle rehabilitation mechanism driven by Pneumatic Artificial Muscles

The ankle is the most weight-bearing joint in the body, which plays a vital role in ensuring people’s walking. Aiming at the problems of complex mechanism, rigid drive and difficult control of the current ankle joint rehabilitation equipment, a 3-SPS/S type ankle parallel rehabilitation mechanism based on pneumatic artificial muscles is developed, and a self-tuning PID controller based on BP neural network is proposed to improve the accuracy of the control system. According to the static characteristics of the pneumatic artificial muscle, the physicians mathematical model of the artificial muscle is optimized, the kinematic position inverse solution of the ankle joint parallel rehabilitation mechanism is analysed and the control system is established. Combined with traditional PID control and BP neural network control algorithm, the BP_PID controller is completed, the control system is simulated and analysed. The simulation results show that BP_PID controller can meet design requirements for the ankle joint parallel rehabilitation mechanism, and is obviously superior to the PID controller in response speed and control accuracy.

Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor

There has been a lot of interest in generating electricity using nuclear energy recently. This interest is due to the features of such a source of energy. The main part of the nuclear energy system is the reactor core, especially the most widely used Pressurized Water Reactor (PWR). This reactor is the hottest part of the nuclear system; security risks and economic possibilities must be considered. Controlling this reactor can increase the security and efficiency of nuclear power systems. This study presents a dynamic model of the (PWR), including the reactor's core, the plenums of the upper and lower, and the connecting piping between the reactor core and steam generator. In addition, an adaptive neuro-fuzzy (ANFIS) self-tuning PID Controller for the nuclear core reactor is presented. This adaptive controller is used to enhance the performance characteristics of PWR by supporting the profile of the reactor power, the coolant fuel, and hot leg temperatures. The suggested proposed ANFIS self-tuning controller is estimated through a comparison with the conventional PID, neural network, and fuzzy self-tuning controllers. The results showed that the proposed controller is best over traditional PID, neural network, and fuzzy self-tuning controllers. All simulations are throughout by using MATLAB/SIMULINK.

Research on DC Electronic Load System Based on PSO-PID Algorithm

At present, the control part of the DC electronic load is mainly based on analog control, that is, the control loop is controlled by an operational amplifier. Therefore, it is easy to cause slow response speed and overshoot in the process of current rise, and the improvement of response speed is of great significance to the test link. Therefore, this paper studies the digital control of DC electronic load. The DC electronic load control system has the characteristics of nonlinearity, and it is difficult to complete the theoretical modeling. Therefore, the MATLAB system identification toolbox is used to obtain the mathematical model. The traditional PID tuning method has a long time and poor effect. This paper adopts a parameter self-tuning PID controller based on particle swarm optimization algorithm. Through MATLAB simulation, the traditional PID control and particle swarm optimization PID control are simulated and compared. The results show that the digital controller has faster response speed and smaller overshoot than the conventional controller, and the system performance is significantly improved.

Research on Diesel Engine Speed Control Based on BPNN Self-tuning PID Controller

As a more efficient power machine, diesel engine is widely used in industry, large vehicles, ships, power generation and other industries. Because of its high thermal efficiency, low fuel consumption, strong power and long service life, diesel engine will continue to occupy a leading position in its application field in the coming decades. Therefore, the speed control of diesel engine has always been a hot research topic. However, there are some disadvantages if only using PID controller for diesel engine speed control. This paper studies the structure of diesel engine speed control and the form of neural network. The combination of BPNN (Back Propagation Neural Network) and PID control makes it adjust the PID parameters in real time according to the speed error and achieve the tracking of actual speed and ideal speed. The realization of this method solves the problem of difficult PID parameters selection to a certain extent. And it has the characteristic of automatically adjusting parameters under variable working conditions. So that the engine can still maintain the desired speed under variable working conditions.

Design and Evaluation of Self-Tuning Fuzzy PID Controller with Reference Model for Ball and Beam Mechanism

—Fuzzy Logic controller is one of the popular techniques in control system. However, the accuracy depends on numbers and the design of membership functions. Further, the output does not always fulfill specific design requirements. In this study, Fuzzy PID controller with first-order reference model had been designed and evaluated. The proposed new structure is able to guide fuzzy mechanism to produce desired output response as specified by the first-order model such as the time constant, rise time and settling time. The study was implemented on a ball and beam system to control the position of the ball while rolling on a beam. The control of a ball and beam system is challenging because it is an open-loop unstable system as the ball will continuously moves on the beam without control. The study also compares the results acquired from Fuzzy PID and Fuzzy PI plus PD against the proposed Fuzzy PID with first-order reference model controller. The evaluation covers for a pole starting from 0 until 1.0 for the first-order model. As a results, the proposed controller is able to produce an output response as specified where the fastest response was acquired when pole is set to 0.9. Whereas the Fuzzy PID and Fuzzy PI plus PD always produce the same transient response during step input test.

Active Vibration Control for the Mitigation of Wheel Squeal Noise Based on a Fuzzy Self-Tuning PID Controller

The wheel squeal noise of a train is often made when it passes a tight curve. The noise annoys the passengers and the people living close to railway tracks. According to the research background, wheel vibration, as a result of unstable contact force, is the main source of wheel squeal noise. This study presents a novel method to reduce wheel squeal noise based on the active vibration control of wheels and the use of piezoelectric actuators attached to wheel treads. The proposed method is implemented in an experimentally validated time model involving the linear dynamics of wheel and track and nonlinear contact forces. Then, the model is modified to enhance the effect of the piezoelectric actuators. The relationship between the momentum and the voltage applied to the piezoelectric patch is also considered in modeling. To determine the amplitude and the direction of the applied voltage, a feedback controller is designed based on the fuzzy self-tuning PID controller scheme. This controller is similar to the conventional PID controller, but its coefficients are tuned by the fuzzy tuning mechanism according to the wheel response. The results show that the proposed method is capable of suppressing wheel squeal noise, especially in high frequencies. Furthermore, it is as applicable to worn wheels as to new ones.

Speed Control of Separately Excited D.C. Motor using Self-Tuned Parameters of PID Controller

This paper presents a simulation and hardware implementation of a closed loop control of a separately excited D.C. motor using a self-tuning PID controller. The PID controller design is based on using the Field Programmable Analog Array (FPAA) technology. Parameters tuning of the PID controller is achieved by using the genetic algorithm (GA). The FPAA controller based technology gives the advantage of low power, no quantization noise, high bandwidth and high speed response. The practical results show that a self-tuning controller can outperform a hand-tuned solution and demonstrate adaptability to plant drift; also it gives very acceptable results in the reduction of overshoot, stability time and the steady-state transient response of the controlled plant.