Proportional Integral Derivative Controller Research Articles

Power Quality Conditioners-Based Fractional-Order PID Controllers Using Hybrid Jellyfish Search and Particle Swarm Algorithm for Power Quality Enhancement

Power quality (PQ) is a major issue in today’s electrical system that affects both utilities and customers. The proliferation of power electronics devices, smart grid technology, and renewable energy sources (RES) have all contributed to the emergence of PQ concerns in today’s power system. The Unified Power Quality Conditioner (UPQC) is a versatile tool that can be used to fix distribution grid issues caused by irregular voltage, current, or frequency. Several tuning parameters, however, restrict the effectiveness of the Fractional-Order Proportional Integral Derivative (FOPID) control technique, which is proposed to improve UPQC performance. To move beyond these restrictions and find the optimal solution for the FOPID controller problem, a hybrid optimization strategy called the Hybrid Jellyfish Search Optimizer and Particle Swarm Optimizer (HJSPSO) is employed. To meet the load requirement during PQ issue periods, the suggested model incorporates a renewable energy source into the grid system. Whether the load is linear or non-linear, the design maintains PQ problems to a minimum. Furthermore, the FOPID control technique is compared with other controllers. Results show that grid-connected RES systems using the proposed FOPID control approach for UPQC have fewer PQ problems. The presented UPQC with HJSPSO strategy significantly outperformed, with the shortest computing time of 127.474 s and an objective function value of 1.423.

PID Optimization Tuning Using Lagrangian Mechanics

Creating a simulation of a system enables the tuning of control systems without the need for a physical system. In this paper, we employ Lagrangian Mechanics to derive a set of equations to simulate an inverted pendulum on a cart. The system consists of a freely-rotating rod attached to a cart, with the rod’s balance achieved through applying the correct forces to the cart. We manually tune the proportional, integral, and derivative gain coefficients of a Proportional Integral Derivative controller (PID) to balance a rod. To further improve PID performance, we can optimize an objective function to find better gain coefficients.

Research step of PID control method of stepper motor based on improved fuzzy control algorithm

The significance of PID control within the management system of stepper motors is noteworthy; nonetheless, it is worth noting that stepper motors are susceptible to issues such as low power, step loss, and vibration. The conventional Proportional-Integral-Derivative (PID) control method is insufficient in addressing the control challenge specific to stepper motor management systems. Hence, this research work presents an enhanced fuzzy control method that integrates the principles of fuzzy control theory with traditional PID control theory. The integration of fuzzy control into the P ID control is undertaken to create a fuzzy controller that satisfies the demands of stepper motor control. Additionally, the division of indices is conducted in accordance with the specifications of the fuzzy controller in order to mitigate the disruptive elements of PID control. then, the use of fuzzy control rules is employed to achieve control over the stepper motor, resulting in the development of an enhanced scheme that is then subjected to rigorous validation. The present study employs a MATLAB simulation to compare the performance of the enhanced fuzzy control algorithm with that of the P-ID control method. The results demonstrate that the improved fuzzy control algorithm significantly enhances the stability and dynamic performance of the stepper motor. Superior to traditional Proportional-Integral-Derivative (PID) control.

Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid

In a renewable energy-based islanded microgrid system, frequency control is one of the major challenges. In general, frequency oscillations occur in islanded microgrids due to the stochastic nature of load and variable output power of distributed generating units (DGUs). In the presented research proposal, frequency oscillations are suppressed by implementing the proportional integral derivative (PID) controller-based control design strategy for an islanded microgrid. The modeling of the islanded microgrid is firstly presented in the form of a linearized transfer function. Further, the derived transfer function is approximated into its equivalent first-order plus dead time (FOPDT) form. The approximated FOPDT transfer function is obtained by employing the reaction curve method to calculate the parameters of the FOPDT transfer function. Furthermore, the desired frequency regulation is achieved for the manifested FOPDT transfer function by incorporating PID control design. For PID controller tuning, different rule-based methods are implemented. Additionally, comparative analysis is also performed to ensure the applicability of the comparatively better rule-based tuning method. The Wang–Chan–Juang (WCJ) method is found effective over other rule-based tuning methods. The efficacy of the WCJ method is proved in terms of transient response and frequency deviation. The tabulated data of tuning parameters, time domain specifications, and error indices along with responses are provided in support of the presented control strategy.

Enhancing Power Generation for Solar-Based Cardiac Pacemakers: A Novel Approach with FOPID and HCMKH-BS2

Biomedical implants, such as implantable cardioverter heart monitors, rely on limited battery life, which necessitates costly replacements and patient discomfort. To address these challenges, a novel solar-based pacemaker model is proposed that is integrated with a fractional-order proportional integral derivative (FOPID) controller and a Hybridized Chaotic multiverse Krill Herd-based Backtracking Search Strategy (HCMKH-BS2) to enhance power generation for pacemaker operation. Our approach considers the impact of partial shading conditions on solar power. To mitigate these conditions, a FOPID controller with an HCMKH-BS2 model is developed which also addresses environmental issues through the adoption of a DC-DC converter. The gain parameters of the FOPID controller are determined using the proposed HCMKH-BS2 algorithm. The proposed technique is implemented to evaluate its performance using various measures in terms of duty cycle, convergence analysis, and power. The performance validation is performed based on the analyses of constant irradiance and changed irradiance. The proposed method achieved a fitness value of 0.01 from the convergence analysis and the duty cycle of 0.48D. These results demonstrate the superiority of our proposed technique over previous methods, highlighting its potential for enhancing the performance of solar-based cardiac pacemakers.

Enhancing Power Quality with PDO-FOPID Controller in Unified Power Quality Conditioner for Grid-Connected Hybrid Renewables

Addressing the pressing research problem of power fluctuations and grid harmonics in the integration of renewable energy, our proposed control strategy utilizes the Prairie Dog Optimization Fractional Order Proportional Integral Derivative (PDO-FOPID) controller within a Unified Power Quality Conditioner (UPQC) system. This innovative approach is tailored to mitigate harmonics and meet load requirements in grid-connected hybrid renewables. The UPQC system is instrumental in regulating coupling point voltage, countering voltage and current harmonics to enhance overall power quality. The PDO-FOPID controller dynamically adapts control parameters to system dynamics and load changes, ensuring a stable power supply despite the variability of renewable sources. Simulations in MATLAB/ Simulink confirm its superiority over traditional control strategies, such as PI, sliding mode, and fuzzy control, in harmonics mitigation, load fulfilment, and power stability. By effectively addressing these challenges, our proposed solution not only contributes to resolving a critical research problem but also advances the seamless integration of Hybrid Renewable Energy Sources (HRES) into power systems, thereby enhancing overall grid performance and the efficacy of renewable energy integration.

Transient Behavior and Control of Polyethylene Production in a Fluidized Bed Reactor Utilizing Population Balance Model.

In this study, a fluidized bed reactor for polyethylene production was employed using a dry mode approach, where the recycle stream may contain components of a nature that cannot be condensed through standard cooling. To analyze the behavior of the fluidized bed reactors during the copolymerization of ethylene with butene, a dynamic population balance model was employed. The study includes sensitivity analyses through computer simulations to examine the variations in reactor temperature, molecular weights, catalyst feed rate, and monomer/comonomer concentrations in the fluidized bed reactor. It is noteworthy that the reactor exhibits instability under normal operational conditions and is sensitive to changes in the catalyst feed rate and coolant temperature of the heat exchanger. The findings also highlight challenges such as temperature fluctuations above the polymer melting point. This underscores the importance of implementing a temperature control system to prevent issues like reactor shutdown due to elevated temperatures. Dynamic instabilities were observed under specific circumstances and were successfully controlled using Proportional Integral Derivative (PID) control strategies. The population balance model is essential for understanding the complexity of transient polymerization reactions. It enables researchers to simulate and optimize polymerization processes by utilizing the detailed kinetics of the reaction.

Development of renewable energy fed three-level hybrid active filter for EV charging station load using Jaya grey wolf optimization

This work develops a hybrid active power filter (HAPF) in this article to operate in conjunction with the energy storage system (ESS), wind power generation system (WPGS), and solar energy system (SES). It employs three level shunt voltage source converters (VSC) connected to the DC-bus. Optimization of the gain values of the fractional-order proportional integral derivative controller (FOPIDC) and parameter values of the HAPF is achieved using the Jaya grey wolf hybrid algorithm (GWJA). The primary objectives of this study, aimed at enhancing power quality (PQ), include: (1) ensuring swift stabilization of DC link capacitor voltage (DCLCV); (2) reducing harmonics and improving power factor (PF); (3) maintaining satisfactory performance under different combinations of loads like EV charging load, non linear load and solar irradiation conditions. The proposed controller's performance is evaluated through three test scenarios featuring different load configurations and irradiation levels. Additionally, the HAPF is subjected to design using other optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) to assess their respective contributions to PQ improvement.

Jellyfish Search Optimized Gated Recurrent Unit Based FOPID/PID for Enhanced Automatic Voltage Regulator

The most essential mechanism for regulation in power systems is the Automatic Voltage Regulator (AVR) device, which maintains generator terminal voltage within predefined parameters. This research paper introduces an innovative approach to enhance AVR system performance through the ideal setting for a fractional-order PID (FOPID) controller that leverages Bidirectional Gate Recurrent Unit (BiGRU)-based deep learning techniques in conjunction with the Jellyfish Search Optimization (JSO) algorithm. To enhance flexibility and precision in control, the conventional proportional integral derivative (PID) controller is replaced with a FOPID controller, whose parameters are optimized by means of the JSO algorithm. The effectiveness of the proposed Jellyfish Search Optimization-based most advanced controllers (JSO-FOPID/PID) is compared with the existing FOPID controller implemented using the Seagull Optimization Algorithm (SOA-FOPID). Comprehensive analyses, including transient time, robustness, stability, and convergence analysis, are conducted subject to applying both stable and alternating load situations to validate the proposed approach. Experimental validation is performed using the MATLAB R2022a application with FOMCON tools and a laboratory prototype for testing. The simulations and experiments demonstrate that the projected AVR consistently delivers optimum dynamic responses and enhances overall stability when compared to existing methods, underscoring its efficacy in voltage regulation and power system control.

Polymerase Chain Reaction Microchip and PID Controller Based Thermal Cycler Design

Numerous specific procedures are needed for nucleic acid detection using traditional approaches. Because of the time and instrumentation requirements, poor detection limits, and other factors, they are behind in creating point-of-care (POC) testing. In addition, some of these traditional approaches can be extremely sensitive. Microfluidic technologies are being used to overcome these challenges by enabling the efficient and automatic performance of all analytical stages, such as sample pre-treatment, reactions, separations, and diagnostics in microchannels on a small chip. The use of microfluidics technology in polymerase chain reaction (PCR) technology has resulted in many advantages. PCR microchips and portable thermal cyclers that can be used for POC testing have enabled faster amplification with accurate results. This paper proposes a low-cost, fast enough thermal cycler for POC testing that uses a polymer-glass microchip and an affordable Peltier element to quickly heat samples. A laser cutter was used to build the microchip needed to perform PCR, which was designed using a computer-aided design (CAD) software. The heating and cooling elements employed in the creation of the thermal cycler were Peltier elements, heat sink, centrifugal fan, and CPU fan. A microcontroller and H-Bridge modules were used to control the temperature with the help of proportional-integral-derivative (PID) controller algorithm. After that, the circuit components and PID control algorithm—which was created for temperature control—were combined to create the PCR cycle. The analysis focuses on the heating and cooling performances of suggested thermal cycler.

2DOF‐FOPID‐IF control with improved sparrow modulation for cascaded H‐bridge multilevel inverter in PV applications

SummaryPhotovoltaic (PV) systems benefit from the cascaded H‐bridge multilevel inverter (CHMLI) topology due to its high flexibility and efficiency. However, PV mismatches in three‐phase grid‐connected systems result in an unbalanced power supply, which leads to an unbalanced grid current. Proportional integral derivative (PID) controllers can be used to control H‐bridge inverters because of their simplicity of tuning as well as their basic structure. However, due to the nonlinearity and high sensitivity of the PID controller, its performance declines as a result of high overshoot, high settling time, and high rise time during significant external disturbances. Moreover, the switching angle of the inverter must be adjusted to provide the required fundamental voltage while reducing harmonic content. In order to address this problem, a control technique with modulation compensation is proposed. The present work offers a two‐degree‐of‐freedom fractional‐order PID controller with an integrated filter (2DOF–FOPID–IF). Moreover, the optimum switching angles are determined for the CHMLI by employing an improved sparrow optimization (ISO) algorithm. To prove the practicality of the suggested technique, simulation, and experimental data are compared with the existing techniques. The proposed technique shows better performance in terms of settling time, overshoot, rise time, and low harmonics.

Novel Proportional–Integral–Derivative Control Framework on Continuous-Time Positive Systems Using Linear Programming

This paper considers the proportional–integral–derivative (PID) control for continuous-time positive systems. A three-stage strategy is introduced to design the PID controller. In the first stage, the proportional and integral components of the PID control are designed. A matrix decomposition approach is used to describe the gain matrices of the proportional and integral components. The positivity and stability of the closed-loop systems without the derivative component of PID control are achieved by the properties of a Metzler and Hurwitz matrix. In the second stage, a non-negative inverse matrix is constructed to maintain the Metzler and Hurwitz properties of the closed-loop system matrix in the first stage. To deal with the inverse of the derivative component of PID control, a matrix decomposition approach is further utilized to design a non-negative inverse matrix. Then, the derivative component is obtained by virtue of the designed inverse matrix. All the presented conditions can be solved by virtue of a linear programming approach. Furthermore, the three-stage PID design is developed for a state observer-based PID controller. Finally, a simulation example is provided to verify the effectiveness and validity of the proposed design.

System Identification and Fractional-Order Proportional–Integral–Derivative Control of a Distributed Piping System

The vibration of piping systems is one of the most important causes of accelerated equipment wear and reduced work efficiency and safety. In this study, an active vibration control method based on a fractional-order proportional–integral–derivative (PID) controller was proposed to suppress pipeline vibration and reduce pipeline damage. First, a mathematical model of the distributed piping system was established using the finite element analysis method, and the characteristics of the distributed piping system were studied effectively. Further, the time-frequency domain parameter identification method was used to realise the system identification of the cross-point vibration transfer function between the brake and sensor, and the particle swarm optimisation algorithm was utilised to further optimise the transfer function parameters to improve the system identification accuracy. Therefore, a fractional-order PID controller was designed using the D-decomposition method, and the optimal controller parameters were obtained. The experimental and numerical simulation results show that the improved system identification algorithm can significantly improve modelling accuracy. In addition, the designed fractional-order PID controller can effectively reduce the system’s overshoot, oscillation time, and adjustment time, thereby reducing the vibration response of piping systems.

Open Thermal Control System for Stable Polymerase Chain Reaction on a Digital Microfluidic Chip.

In this paper, a digital microfluidic thermal control system was introduced for the stable polymerase chain reaction (PCR). The system consists of a thermoelectric cooler unit, a thermal control board, and graphical-user-interface software capable of simultaneously achieving temperature control and on-chip droplet observation. A fuzzy proportional-integral-derivative (PID) method was developed for this system. The simulation analysis was performed to evaluate the temperature of different reagents within the chip. Based on the results, applying fuzzy PID control for PCR will enhance the thermal stability by 67.8% and save the time by 1195 s, demonstrating excellent dynamic response capability and thermal robustness. The experimental results are consistent with the simulation results on the planar temperature distribution, with a data consistency rate of over 99%. The PCR validation was carried out on this system, successfully amplifying the rat GAPDH gene at a concentration of 193 copies/μL. This work has the potential to be useful in numerous existing lab-on-a-chip applications.

Design and stabilization of a Coandă effect-based UAV: Comparative study between fuzzy logic and PID control approaches

Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research.

Research on Three-Closed-Loop ADRC Position Compensation Strategy Based on Winch-Type Heave Compensation System with a Secondary Component

To mitigate the interference of waves on an offshore operation ship, heave compensation systems find widespread application. The performance of heave compensation systems significantly influences the efficiency and safety of maritime operations. This study established a mathematical model for a winch-based active heave compensation system. It introduced a three-loop active disturbance rejection control (ADRC) strategy that encompasses piston position control, winch speed control, and load-displacement control to enable the real-time estimation and compensation of system disturbances, thereby enhancing the performance of the heave compensation system. To assess the effectiveness of this control strategy, this study employed Matlab/Simulink and AMESim to construct a co-simulation model and conducted a comparative analysis with traditional proportional integral derivative (PID) control systems. The research findings indicate that the three-loop ADRC position control strategy consistently delivered superior compensation performance across various operational scenarios.

Heave attenuation in offshore managed pressure drilling with an integral sliding mode controller

Managed pressure drilling (MPD) is a technique used to precisely control the pressure within the wellbore during the drilling process to overcome drilling challenges, such as heave disturbance caused by waves. The mismatched nature of the heave disturbance presents a control challenge, making it difficult to reduce the disturbance effectively. However, MPD utilizing an appropriate controller can provide a solution to this challenge. As a result, this paper proposes an integral sliding mode controller (Integral SMC) based on feedback linearization and a nonlinear disturbance observer to attenuate the heave disturbance effect and provide stability for MPD systems. A comparison study is conducted between the proposed controller and industrial controllers, such as Proportional Integral Derivative (PID) and manual controllers, using root mean square error (RMSE) as the evaluation metric. Additionally, the robustness of the proposed controller and the nonlinear disturbance observer in the presence of parameter uncertainty and measurement noise is investigated. The results demonstrate that the proposed controller is a viable solution to overcome the heave disturbance effect on offshore MPD systems.

Inverse Estimation of Dynamic Heat Flux Using Proportional Integral Derivative Control

To improve long-term dynamic heat flux prediction using an embedded thermocouple, a novel inverse estimation approach using a proportional integral derivative (PID) control scheme is proposed. A feedforward model of the discrete integer-order transfer function was identified from a one-dimensional dynamic heat-flux sensor using the calibrated heat flux with varying amplitudes of the pseudo-random binary sequence and its single-temperature response data. Using the model and unit-step temperature response as the controlled plant and reference input signal, the PID controller with a low-pass filter was designed. Using the on-site temperature measurement data as the reference signal instead of the unit-step temperature, the time-history control output values, that is, the inverse estimated dynamic heat flux of the designed PID controller, were obtained. The comparison of the test results and the discussion indicate that 1) the proposed inverse method avoids the impact of the thermal inertia of the thermocouple and significantly alleviates the effects of measurement noise, and 2) in addition to high identification efficiency and measurement fidelity, further improvement in the effective measuring time of the dynamic heat flux was observed.

Mathematical Analysis and Real-Time Control of a Novel 5-DOF Robotic System with a Parallel Kinematics Structure for Additive Manufacturing Technologies

The systems that use parallel kinematic structures in additive manufacturing technology are particularly notable for their ability to provide exceptional precision and efficiency in the fabrication of intricate geometrically shaped items. This study introduces a novel system paradigm with five degrees of freedom, specifically developed to tackle existing additive manufacturing issues. In the proposed design, by incorporating rotational motions along the x and z axes, contributions were added to the efficiency of typical three-degrees-of-freedom (3-DOF) systems, resulting in a total of five degrees of freedom. In this way, it is aimed at increasing product durability, improving surface integrity, and saving production time. In this study, the conceptual design of the system was defined. Mathematical analyses were then used to determine the kinematic and dynamic models of the system, and a proposed model-based control technique was revealed. To evaluate the axis movement performance of the system, two different control techniques were used, and real-time test studies were conducted. The first control technique was the proportional–integral–derivative (PID) controller, and the second method was the sliding mode control (SMC) method, which was used to increase the performance of the system during trajectory tracking. The experimental results showed that the SMC method provides a reasonably good trajectory tracking response and a steady-state error compared to the classical PID controller.

Deep reinforcement learning implementation on IC engine idle speed control

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies.