Proportional-integral-derivative Gain Research Articles

Advancing Offshore Renewable Energy: Integrative Approaches in Floating Offshore Wind Turbine-Oscillating Water Column Systems Using Artificial Intelligence-Driven Regressive Modeling and Proportional-Integral-Derivative Control

This research investigates the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) to enhance sustainable energy generation, focusing on addressing dynamic complexities and uncertainties inherent in such systems. The novelty of this study lies in its dual approach, which integrates regressive modeling with an aero-hydro-elasto-servo-mooring coupled system with a deep data-driven network and implements a proportional-integral-derivative (PID) control mechanism to improve system stability. By employing Artificial Neural Networks (ANNs), the study circumvents the challenges of real-time closed-loop control on FOWT structures using the OpenFAST simulation tool. Data-driven models, trained on OpenFAST datasets, facilitate real-time predictive behavior analysis and decision-making. Advanced computational learning techniques, particularly ANNs, accurately replicate the dynamics of FOWT-OWC numerical models. An intelligent PID control mechanism is subsequently applied to mitigate structural vibrations, ensuring effective control. A comparative analysis with traditional barge-based FOWT systems underscores the enhanced modeling and control methodologies’ effectiveness. In this sense, the experimental results demonstrate substantial reductions in the mean oscillation amplitude, with reductions from 5% to 35% observed across various scenarios. Specifically, at a wave period from 20 s and a wind speed of 5 m/s, the fore-aft displacement was reduced by 35%, exemplifying the PID control system’s robustness and efficacy under diverse conditions. This study highlights the potential of ANN-driven modeling as an alternative to managing the complex non-linear dynamics of NREL 5 MW FOWT models and underscores the significant improvements in system stability through tailored PID gain scheduling across various operational scenarios.

Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach.

Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional-integral-derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10-5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

Artificial neural network assisted robust droop control of autonomous microgrid

AbstractElectric grid is vulnerable to power imbalance and inertia is the grid's response to overcome such disturbance. Augmentation of power electronic converter based renewable energy technologies like Photovoltaic Generators (PVG) and batteries in utility grid significantly reduces inertia. Inertia degradation is indicated by sharp Rate of Change of Frequency (ROCOF) events due to any grid component failure or imbalance. Fixed gain feedback Proportional Integral Derivative (PID) control is insufficient to deal with varying ROCOF events. This work proposes Sliding Mode (SM) robust droop control scheme assisted by Artificial Neural Network (ANN) algorithm for an islanded PVG integrated microgrid. Droop response is governed by swing equation that uses PVG Maximum Power Point (MPP) forecasted by ANN. ANN forecast is compared with optimized Gaussian process regression algorithm based on mean squared error and speed of training as key performance indicator. The algorithms are trained and validated based on climate dataset of Islamabad, Pakistan. SM control performance is compared with various PID gain settings and qualified as the most suitable against variable source, load and ROCOF scenarios. Finally, significance of accurate MPP forecast for droop control is established by comparing the ANN and deterministic forecaster assisted droop response in a microgrid case study.

PID Controller Design for Heating Furnace Temperature Based on Bat Algorithm (BA)

A furnace is a tool for heating materials, oil, and others. Furnaces use gas, coal, and oil as fuel. Temperature is the main parameter that needs to be controlled to remain stable, and precise and improve fuel efficiency. As technology develops, several methods can be used to control temperatures that are more reliable than conventional controls. The technology is a Proportional Integral Derivative (PID) controller. PID controllers have been proven and widely used in the industry, but determining the gain of the PID value is still not accurate. This can affect temperature stability, and slow response to reach the desired set point. Therefore, it is necessary to optimize the control system. Optimizing by looking for a better PID gain value with the artificial intelligence tuning method. The Artificial Intelligence method is Bat Algorithm(BA). The simulation results and discussion show that the best design is PID-BA with an overshoot of 0.0429, non undershoot, and the fastest settling time of 16.9 seconds

Desain Optimasi PID Controller Pada Temperatur Heating Furnace Berbasis Ant Colony Algorithm (ACO)

A furnace is a tool for heating materials, oil, and so on, which usually uses gas, coal, and oil as fuel. Temperature is the main parameter that needs to be controlled in order to remain stable, precise, and of course improve fuel efficiency. As technology develops, there are several methods that can be used to control temperatures that are more reliable than conventional controls. The technology is Proportional Integral Derivative (PID) controller. PID controllers have been proven to be the best controllers and are widely used in industry. But to determine the gain from the PID value is still not accurate and can affect temperature stability, the response is also still slow to reach the desired set point. Therefore, this paper is to simulate a better PID gain value by using the artificial intelligence tuning method. The artificial intelligence method is Ant Colony Optimization (ACO). The simulation results and discussion show that the best design is PID-ACO with 0.0081 overshot, no undershot, and the fastest settling time is 35 seconds

Validation of a Temperature-Feedback Controlled Automated Magnetic Hyperthermia Therapy Device.

We present in vivo validation of an automated magnetic hyperthermia therapy (MHT) device that uses real-time temperature input measured at the target to control tissue heating. MHT is a thermal therapy that uses heat generated by magnetic materials exposed to an alternating magnetic field. For temperature monitoring, we integrated a commercial fiber optic temperature probe containing four gallium arsenide (GaAs) temperature sensors. The controller device used temperature from the sensors as input to manage power to the magnetic field applicator. We developed a robust, multi-objective, proportional-integral-derivative (PID) algorithm to control the target thermal dose by modulating power delivered to the magnetic field applicator. The magnetic field applicator was a 20 cm diameter Maxwell-type induction coil powered by a 120 kW induction heating power supply operating at 160 kHz. Finite element (FE) simulations were performed to determine values of the PID gain factors prior to verification and validation trials. Ex vivo verification and validation were conducted in gel phantoms and sectioned bovine liver, respectively. In vivo validation of the controller was achieved in a canine research subject following infusion of magnetic nanoparticles (MNPs) into the brain. In all cases, performance matched controller design criteria, while also achieving a thermal dose measured as cumulative equivalent minutes at 43 °C (CEM43) 60 ± 5 min within 30 min.

Integral State Feedback Control Using Linear Quadratic Gaussian in DC-drive System

The electric motor is one of the technological developments which can support the production process. DC motor has some advantages compared to AC motor especially on the easier way to control its speed or position as well as its widely adjustable range. The main issue in the DC motor is controlling the angular speed with uncertainty and disturbance. The alternative solution of a control method with simple, easy to design, and implementable in a multi-input multi-output system is integral state feedback such as linear quadratic Gaussian (LQG). It is a combination between linear quadratic regulator and Kalman filter. One of the advantages of this method is the usage of fewer sensors compared with the original linear quadratic regulator method which uses sensors as many as the state in the system model. The design, simulation, and experimental study of the application of LQG as state feedback control in a DC-drive system have been done. Both performance and energy were analyzed and compared with conventional proportional integral derivative (PID). The gain of LQG was determined by trial whereas the PID gain is determined from MATLAB autotuning without fine-tuning. The load test and tracking test were carried out in the experiment. Both simulation and hardware tests showed the same result which LQG is superior in integral absolute error (IAE) by up to 74.37 % in loading test compared to PID. On the other side, LQG needs more energy, it consumes higher energy by 6.34 % in the load test.

Design, modeling and simulation of a control surface-less tri-tilt-rotor UAV

PurposeThe purpose of this study is to design a flight control model for a control surface-less (CSL) tri-tilt-rotor (TTR) unmanned aerial vehicle (UAV) based on a Proportional Integral Derivative (PID) controller to stabilize the altitude and attitude of the UAV subjected to various flying conditions.Design/methodology/approachFirst, the proposed UAV with a tilting mechanism is designed and analyzed to obtain the aerodynamic parameters. Second, the dynamics of the proposed UAV are mathematically modeled using Newton-Euler formation. Then, the PID controller is implemented in the simulation model to control flight maneuvers. The model parameters were implemented in a mathematical model to find the system’s stability for various flight conditions. The model was linearized to determine the PID gain values for vertical take-off and landing, cruise and transition mode. The PID controller was tuned to obtain the desired altitude and attitude in a short period. The tuned PID gain values were implemented in the PID controller and the model was simulated.FindingsThe main contribution of this study is the mathematical model and controller for a UAV without any control surface and uses only a thrust vector control mechanism which reduces the complexity of the controller. The simulation has been carried out for various flight conditions. The altitude PID controller and the attitude PID controller for CSL-TTR-UAV were tuned to obtain desired altitude and attitude within the optimum duration of 4 s and deviation in the attitude of 8%, which is within the allowable limit of 14%. The findings obtained from the simulation revels that the altitude and attitude control of the CSL-TTR-UAV was achieved by controlling the rpm of the rotor and tilt angle using the PID controller.Originality/valueA novel CSL TTR UAV mathematical model is developed with a dual tilting mechanism for a tail rotor and single axis tilt for the rotors in the wing. The flight control model controls the UAV without a control surface using a PID controller for the thrust vector mechanism.

Magnetic tweezers with magnetic flux density feedback control.

In this work, we present a single-pole magnetic tweezers (MT) device designed for integration with substrate deformation tracking microscopy and/or traction force microscopy experiments intended to explore extracellular matrix rheology and human epidermal keratinocyte mechanobiology. Assembled from commercially available off-the-shelf electronics hardware and software, the MT device is amenable to replication in the basic biology laboratory. In contrast to conventional solenoid current-controlled MT devices, operation of this instrument is based on real-time feedback control of the magnetic flux density emanating from the blunt end of the needle core using a cascade control scheme and a digital proportional-integral-derivative (PID) controller. Algorithms that compensate for a spatially non-uniform remnant magnetization of the needle core that develops during actuation are implemented into the feedback control scheme. Through optimization of PID gain scheduling, the MT device exhibits magnetization and demagnetization response times of less than 100ms without overshoot over a wide range of magnetic flux density setpoints. Compared to current-based control, magnetic flux density-based control allows for more accurate and precise magnetic actuation forces by compensating for temperature increases within the needle core due to heat generated by the applied solenoid currents. Near field calibrations validate the ability of the MT device to actuate 4.5 μm-diameter superparamagnetic beads with forces up to 25 nN with maximum relative uncertainties of ±30% for beads positioned between 2.5 and 40 µm from the needle tip.

Chaotic-based particle swarm optimization algorithm for optimal PID tuning in automatic voltage regulator systems

Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.

Pitch Angle Optimization for Small Wind Turbines Based on a Hierarchical Fuzzy-PID Controller and Anticipated Wind Speed Measurement

Wind energy is an alternative to meet the growing energy demand and protect the environment; however, in places with limited wind resources, only the installation of small horizontal-axis wind turbines (SHAWTs) is profitable. At the height of these turbines, the wind is usually unstable with gusts and turbulence due to obstacles in its path such as buildings and trees. The pitch angle must be adaptable to guarantee nominal rotation speed, and it is commonly regulated with a proportional-integral-derivative (PID) feedback controller. This controller works well when the wind is stable, but not with drastic changes in wind speed. To correct this problem, this article introduces a PID controller with automatic adjustment of the gain values using a fuzzy logic controller (FLC). The PID gain adjustment allows an optimal response speed of the system for different wind conditions. The membership functions of the FLC are determined from a methodology that includes: The measurement of the wind speed at a calculated distance, a statistical analysis of the wind variability, and a dynamic analysis of the wind path. In this way, it is possible to anticipate the response of the actuator to the arrival of a gust of wind to the rotor. The algorithm is implemented in 14 kW SHAWTs where the difference in performance with a conventional controller is quantified. Satisfactory results were obtained, the electrical output increased by 7%, and the risk of rotor damage due to vibrations or mechanical fatigue was reduced by 20%.

Fuzzy PID Gain Scheduling Controller for Networked Control System

The use of a communication network in the closed loop control systems has many advantages such as remotely controlling equipment, low cost, easy to maintenance, efficient information transmission, etc. However, the Networked Control System (NCS) has many drawbacks, such as network-induce end-to-end time delay and packet loss, which lead to significant degradation in controller performance and may result in instability. Aiming at solving performance degradation in NCS, this paper propose to take the advantages and strength of the conventional Proportional-Integral-Derivative (PID), Fuzzy Logic (FL), and Gain Scheduling (GS) fundamentals to design a Fuzzy-PID like-Gain Scheduling (F-PID-GS) control technique, which has been proved to be effective in obtaining better performance. The True Time toolbox is used to establish the simulation model of the NCS. Ethernet as a communication network is simulated for different load conditions and random packet loss. The design approach is tested on a second order stepper motor. The results obtained show the effectiveness of the proposed approach in improving the overall system performance.

PID Controlling Approach Based on FBG Array Measurements for Laser Ablation of Pancreatic Tissues

In this article, we propose a temperature-based proportional–integral–derivative (PID) controlling algorithm using highly dense fiber Bragg grating (FBG) arrays for laser ablation (LA) of ex vivo pancreatic tissues. Custom-made highly dense FBG arrays with a spatial resolution of 1.2 mm were fabricated with the femtosecond point-by-point writing technology and optimized for LA applications. In order to obtain proper PID gain values, finite element method-based iterative simulation of different PID gains was performed. Then, the proposed algorithm, with numerically derived PID gains, was experimentally validated. In the experiments, the point temperature was controlled at different distances from the laser fiber tip (6.0, 7.2, 8.4, and 10.8 mm). The obtained results report robust controlling and correlation between controlled distance and the resulting area of ablation. The results of the work encourage further investigation of FBG array application for LA control.

Mitigation of Derivative Kick Using Time-Varying Fractional-Order PID Control

In this paper, a novel approach for the design of a fractional order proportional integral derivative (FOPID) controller is proposed. This design introduces a new time-varying FOPID controller to mitigate a voltage spike at the controller output whenever a sudden change to the setpoint occurs. The voltage spike exists at the output of the proportional integral derivative (PID) and FOPID controllers when a derivative control element is involved. Such a voltage spike may cause a serious damage to the plant if it is left uncontrolled. The proposed new FOPID controller applies a time function to force the derivative gain to take effect gradually, leading to a time-varying derivative FOPID (TVD-FOPID) controller, which maintains a fast system response and significantly reduces the voltage spike at the controller output. The time-varying FOPID controller is optimally designed using the particle swarm optimization (PSO) or genetic algorithm (GA) to find the optimum constants and time-varying parameters. The improved control performance is validated through controlling the closed-loop DC motor speed via comparisons between the TVD-FOPID controller, traditional FOPID controller, and time-varying FOPID (TV-FOPID) controller which is created for comparison with all three PID gain constants replaced by the optimized time functions. The simulation results demonstrate that the proposed TVD-FOPID controller not only can achieve 80% reduction of voltage spike at the controller output but also is also able to keep approximately the same characteristics of the system response in comparison with the regular FOPID controller. The TVD-FOPID controller using a saturation block between the controller output and the plant still performs best according to system overshoot, rise time, and settling time.

Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment

Tuning of a proportional-integral-derivative (PID) controller for a complex multi-joint structure, such as an exoskeleton, using conventional methods is difficult and imprecise. In this paper, an optimal PID tuning method for a 3-dimensional model of a lower-limb human exoskeleton in gait training condition is presented. The dynamic equation of the human-exoskeleton is determined using a Lagrangian approach, and its transfer function is established in a closed-loop control system. PID controller gains, initialized by the Ziegler–Nichols (Z-N) method, are used as the input to an adaptive particle swarm optimization (APSO) algorithm for minimizing the multi-joint trajectory error. The optimized controller is tested in the Gazebo virtual environment and compared with the Z-N and conventional optimization methods. The numerical analysis shows that the PID controller tuned by a combination of Z-N and APSO improves the performance of a lower-limb human exoskeleton in gait training.

Speed Profile Tracking by an Adaptive Controller for Subway Train Based on Neural Network and PID Algorithm

Train running performance largely depends on the controller. In order to improve the controller performance of speed profile tracking, an adaptive control method for subway trains is addressed in this paper. Considered time-varying parameters of train motion, the train model with dynamic parameters is constructed. The model free adaptive controller combined with neural network and proportional-integral-derivative (PID) algorithm is proposed to realize adaptive control. The control laws and parameter tuning laws of the proposed controller are both illustrated, and PID gain range and actuator saturation are taken into consideration. Compared with other controllers, numerical tests are provided to verify the effectiveness and stability of the proposed controller. The controller is also applied on a real line, and the results show that the proposed controller outperforms the currently used controller. More specifically, the proposed controller reduces average absolute running time error by 0.67 s, average absolute stopping position error by 0.09 m, average maximum absolute speed tracking error by 1.22 m/s, and total energy consumption by 2.73 kWh.

Slip control during inertia phase of clutch-to-clutch shift using model-free self-tuning proportional-integral-derivative control

Transmissions require a good shift feeling and improved fuel efficiency. In state-of-the-art stepped automated transmissions, the number of gear stages increases, and the lock-up area is expanded to improve fuel efficiency. However, this makes it difficult to obtain a good shift feeling and it takes a large number of calibration man-hours. Therefore, to reduce the number of calibration man-hours and improve the shift feeling, we propose a slip control law between the engine and the clutch, which is composed of a proportional-integral-derivative (PID) controller and a disturbance observer. Moreover, PID gain is adjusted online by installing an automatic tuning method, which does not require a controlled object model. The effects of the proposed method are verified via an experiment using an actual vehicle. The experimental results show that the proposed method is effective for automatically adjusting PID gain and improving the shift feeling of the stepped automated transmission.

Differential equation approximation and enhancing control method for finding the PID gain of a quarter-car suspension model with state-dependent ODE

Active suspension control strategy design in vehicle suspension systems has been a popular issue in road vehicle applications. In this paper, we consider a quarter-car suspension problem. A nonlinear objective function together with a system of state-dependent ODEs is involved in the model. A differential equation approximation method, together with the control parametrization enhancing transform (CPET), is used to find the optimal proportional-integral-derivative (PID) feedback gains of the above model. Hence, an approximated optimal control problem is obtained. Proofs of convergences of the state and the optimal control of the approximated problem to those of the original optimal control problem are provided. A numerical example is solved to illustrate the efficiency of our method.

Neural Network-Based Real Time PID Gain Update Algorithm for Contour Error Reduction

In this study, a novel neural network-based proportional-integral-derivative (PID) controller gain update algorithm is presented. The proposed algorithm aims to improve the machining quality and efficiency by adjusting the PID gains in real time in order to minimize the contour error. Automatic PID gain tuning algorithms are desired as it can increase the machining efficiency while improving the machining quality. Unlike conventional tracking error-based PID gain update algorithms, the proposed algorithm is based on the contour error and thus, it is more suitable for machine tools. Furthermore, the contour error-based approach enables a scalar representation of the system errors, which leads to a significant reduction in the required computational load. Compare to conventional neural network-based gain tuning algorithms, which often requires two neural networks per axis for automatic PID gain update, the proposed algorithm can update the gains of the a multi-axis system using a single neural network. The experimental results with a 3-degrees of freedom (DOF) Cartesian machine shows a clear reduction in both the contour and tracking errors.

Fuzzy fractional PID gain controller for antilock braking system using an electronic wedge brake mechanism

This paper focuses on the design of a fuzzy fractional Proportional-Integral-Derivative (PID) controller. The controller which is based on the conventional PID controller is modified to be P1–α I1–β D1–γ with the objective to make the controller more flexible and robust to the changes in input and the parameters. In this control structure, the fuzzy logic control acts as the tuner for the parameters α, β and γ, so that the overall controller parameter can be varied and converge better to the changes in a system. The designed fuzzy fractional PID controller is applied to a validated antilock braking system model using an electronic wedge brake system as the actuator. Numerous numerical simulations and comparisons with other fractional PID/conventional PID controllers show that the fuzzy fractional PID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties.