PID Parameters Research Articles

Chaotic-Based Improved Henry Gas Solubility Optimization Algorithm: Application to Electric Motor Control

This study presents a novel meta-heuristic optimization method that combines the Henry Gas Solubility Optimization (HGSO) technique with symmetric chaotic systems. By leveraging the randomness of chaotic systems, the parameters of the HGSO algorithm that require random generation are produced through chaotic processes, allowing the algorithm to exhibit chaotic behavior in its pursuit of optimal values. This innovative approach is termed Chaotic Henry Gas Solubility Optimization (CHGSO), with the primary objective of enhancing the performance of the HGSO method. The randomness of the data obtained from chaotic systems was validated using NIST-800-22 tests. The CHGSO method was applied to both 47 benchmark functions and the optimization of parameters for a PID controller utilized in the speed control of a DC motor. To evaluate the effectiveness of the proposed method, it was compared with several widely recognized algorithms in the literature, including PSO, WOA, GWO, EA, SA, and the original HGSO algorithm. The results demonstrate that the proposed method achieved the best performance in 43 of the benchmark functions, outperforming the other algorithms. In the context of controller design, the PID parameters were optimized using the error-based ITSE objective function. According to the controller responses, the proposed method has achieved the best results in the simulation studies, with a settling time of 0.035 and a rise time of 0.014 without overshooting, and in the experimental studies, with a settling time of 0.15 and a settling time of 1.4%. When the results are examined, it is observed that it has achieved successful results in the controller design problem.

Modeling and Regulation of Dynamic Temperature for Layer Houses Under Combined Positive- and Negative-Pressure Ventilation.

The environmental control of layer houses with multi-tiered cage systems is influenced by factors such as the structure of the henhouses and the heat dissipation of the flock, leading to low precision and large fluctuations in temperature control. Based on a new combined positive- and negative-pressure ventilation (CPNPV) mode, a dynamic temperature model is constructed. Additionally, a temperature control method for a layer house is designed using a variable universe fuzzy PID control algorithm (VFPID). First, based on the principles of energy and mass balance, and by decoupling the relationship between positive- and negative-pressure ventilation volumes, a dynamic temperature model for layer houses under CPNPV was established. Then, the PID parameters and the proportional relationship between positive- and negative-pressure ventilation were optimized through fuzzy rules, and a proportional exponential function was introduced to adjust the scaling of the universe, enabling fine-tuned control. Finally, a temperature control model for the layer house was built using Simulink. The results show that the coefficients of determination (R2) of the constructed dynamic temperature models are between 0.79 and 0.88, respectively, indicating high accuracy. The designed VFPID method outperformed traditional on-off control and improved control precision by 20-23.53% and 10.34-22.22% compared with PID control and fuzzy PID(FPID) control methods, respectively. This study provides new insights for the development of environmental control equipment and precise environmental regulation of layer houses.

TD3 Algorithm of Dynamic Classification Replay Buffer Based PID Parameter Optimization

TD3 Algorithm of Dynamic Classification Replay Buffer Based PID Parameter Optimization

GUI-Driven Real-Time PID Control Tool for Educational Virtual Systems

Abstract: This research presents a pioneering approach in virtual control systems, focusing on developing a highly intuitive Graphical User Interface (GUI) for real-time PID control of LED brightness using Arduino IDE. By embracing a robust pedagogical framework, the study meticulously outlines specific learning objectives that adhere to the SMART criteria, ensuring precise educational outcomes. The key to this research is the tailored design of the GUI, which considers diverse audience needs, prior knowledge, and learning styles. The interfaces are meticulously crafted for optimal user experience, ensuring seamless navigation and engagement. Interactive elements, encompassing simulations, virtual experiments, problem-solving scenarios, and quizzes, foster active participation and hands-on learning. Multimedia elements, such as text, images, videos, animations, and audio, are thoughtfully integrated to cater to varied learning preferences. This paper underscores the critical role of collaborative learning facilitated through discussion forums, chat functionalities, and collaborative virtual environments. The study emphasizes continuous improvement through regular assessments and detailed user feedback analysis. Notably, the research highlights the significant educational impact of the designed GUI. By offering a practical platform where students can dynamically adjust PID parameters and observe immediate effects on LED brightness, the GUI enhances the learning experience in classroom settings. This approach equips students with a tangible understanding of complex PID principles, preparing them for real-world applications. In summary, this research showcases the creation of engaging, practical virtual control system tools, fostering profound and meaningful learning experiences for users within educational contexts. Keywords : control systems, pedagogy, facilitation

Intelligent vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation

Targeting the lateral motion control problem in the intelligent vehicle autopilot structural system, this paper proposes a feedforward + predictive LQR algorithm for lateral motion control based on Genetic Algorithm (GA) parameter optimisation and PID steering angle compensation. Firstly, based on the vehicle dynamics tracking error model, the intelligent vehicle LQR lateral motion controller as well as the feedforward controller are designed, and upon which the predictive controller is added to eliminate the system lag.Subsequently, exploiting the advantage that the PID algorithm is not model-based, a PID steering angle compensation controller that can directly control and correct the lateral error is designed. Second, a LQR controller based on path tracking deviation is designed by using the parameter rectification method of genetic algorithm (GA), which optimizes the control parameters of the lateral motion controller and improves the adaptivity of the control accuracy. Finally, Based on the Carsim-Simulink co-simulation platform, the simulation validation and analysis of double lane change (DLC) test and circular condition test (CCT) are carried out, and the results indicate that compared with the other two LQR controllers, the optimised controllers improved more than 50% in lateral error and heading error control, and the vehicle sideslip angle and vehicle yaw rate are in the range of −0.05° to 0.05° and − 0.15 rad/s to 0.10 rad/s, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

Next-generation data center energy management: a data-driven decision-making framework

In the era of society’s ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, Decision, and Action) loop and based on insights from an Ericsson-operated DC in Linköping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs’ building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency.

Adaptive Control of Ships’ Oil-Fired Boilers Using Flame Image-Based IMC-PID and Deep Reinforcement Learning

The control system of oil-fired boiler units on ships plays a crucial role in reducing the emissions of atmospheric pollutants such as nitrogen oxides (NOx), sulfur dioxides (SO2), and carbon dioxide (CO2). Traditional control methods using conventional measurement sensors face limitations in real-time control due to response delays, which has led to the growing interest in combustion control methods using flame images. To ensure the precision of such combustion control systems, the system model must be thoroughly considered during controller design. However, finding the optimal tuning point is challenging due to the changes in the system model and nonlinearity caused by environmental variations. This study proposes a controller that integrates an internal model control (IMC)-based PID controller with the deep deterministic policy gradient (DDPG) algorithm of deep reinforcement learning to enhance the adaptability of image-based combustion control systems to environmental changes. The proposed controller adjusts the PID parameter values in real-time through the learning of the determination constant lambda (λ) of the IMC internal model. This approach reduces computational resources by shrinking the learning dimensions of the DDPG agent and limits transient responses through constrained learning of control parameters. Experimental results show that the proposed controller exhibited rapid adaptive performance in the learning process for the target oxygen concentration, achieving a reward value of −0.05 within just 105 episodes. Furthermore, when compared to traditional PID tuning methods, the proposed controller demonstrated superior performance, achieving a target value error of 0.0032 and a low overshoot range of 0.0498 to 0.0631, providing the fastest response speed and minimal oscillation. Additionally, experiments conducted on an actual operating ship verified the practical feasibility of this system, highlighting its potential for real-time control and pollutant reduction in marine applications.

Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID

The air suspension system’s superior variable stiffness, low vibration frequency, and resistance to road impacts significantly elevate both the comfort of vehicle occupants and the overall ride quality. By effectively controlling the air suspension system, its superior characteristics can be fully exploited to enhance the overall performance of vehicles. However, the parameter tuning process of the fuzzy PID controller for air suspension involves subjectivity and blindness, which affects the performance of the suspension system. To overcome these shortcomings, a control strategy combining genetic algorithms with fuzzy PID control is proposed. This strategy involves a genetic algorithm-optimized fuzzy PID air suspension control approach specifically targeting the fuzzy PID controller for air suspension. A 1/4 two-degree-of-freedom air suspension fuzzy PID controller is designed in MATLAB 2019a, utilizing genetic algorithms to optimize the PID parameter tuning process. The ride comfort of the fuzzy PID air suspension after tuning is then investigated. In the study of ride comfort on Class B road surfaces, the simulation and experimental results were consistent. Using a genetic algorithm to optimize a fuzzy PID-controlled air suspension resulted in reductions of the root mean square values for vertical body acceleration, suspension deflection, and wheel dynamic load by 30%, 26%, and 9%, respectively, compared to passive suspension. These reductions are further improvements over the corresponding indices controlled by the fuzzy PID alone, which decreased by 23%, 18%, and 6%, respectively. Thus, the control effect of the genetic algorithm-optimized fuzzy PID is superior to that of the fuzzy PID control. This demonstrates that the fuzzy PID control of air suspension optimized by genetic algorithms can further improve the comfort of vehicle occupants and the ride comfort of driving, providing a reference for active control of air suspension systems.

Study on the automatic control method for evaporation treatment of wet flue gas desulfurization wastewater in power plants

ABSTRACT DRFNN is used to predict the evaporation state of wastewater, and incremental PID algorithm is combined to adjust the control quantity. By real-time monitoring the state and parameter changes of wastewater evaporation treatment process, online adjusting and optimizing the parameters of DRFNN and incremental PID, the automatic control of desulfurization wastewater evaporation treatment is completed. The experimental results show that this method can achieve more accurate and reliable automatic control, short control time, strong ability to deal with emergencies, and can converge to stability in a short time. This method can effectively reduce the number of early alarms and failures of the system and save maintenance costs. The IAE values of chloride ion (Cl) and mercury (Hg) in this method are 0.3558 and 0.3872, respectively, which proves that the difference between the actual output and the expected output of this method is very small, which proves that the system in this paper has high accuracy.

Magnesium hydrogen storage: Temperature control via particle swarm with nonlinear inertia strategy

Magnesium-based hydrogen storage has garnered significant attention in the field of hydrogen storage due to its notable advantages, including high hydrogen storage capacity and low material cost. However, the relatively high thermodynamic stability of magnesium-based hydrogen storage materials necessitates heating the material to a specific temperature during the hydrogen charging and discharging processes, severely limiting the practical application and further development of this technology.Therefore, this paper aims to design a temperature control system for a magnesium-based solid-state hydrogen storage bottle. The system employs an optimized particle swarm optimization (PSO) algorithm to determine PID parameters, enabling rapid preheating of the hydrogen storage bottle and maintaining a stable operating temperature. Furthermore, by incorporating an appropriate penalty function, the system effectively suppresses temperature overshoot, preventing adverse effects on hydrogen storage performance caused by transient high temperatures, thereby ensuring the safety and stability of the hydrogen storage process.

High-speed 3D printing temperature system with optimized PlD parameters based on improved ant lion optimization algorithm

An Ant Lion Optimization algorithm based on elite Opposition-based learning and Cosine factors (OCALO) is proposed to address the problem of poor response and stability during heating process in high-speed 3D printing temperature system.The generation of the initial solution in OCALO algorithm is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping, which guarantees the diversity of population. The PID parameters are optimized using the improved algorithm. Compared with two existing classical algorithms and three improved ALO algorithms, the proposed algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The outcomes of simulation and experimentation demonstrate that the algorithm improves the transient and steady-state performance of temperature control with better accuracy and robustness. It takes at least 123 s faster than other controllers to reach stability and is more than two times stronger than other PID controllers, making it better suited to high-speed 3D printing temperature systems.

A decentralized optimal PID controller with disk margin-based robust stability analysis for higher-order industrial systems

ABSTRACT Decentralized control systems are frequently employed to tackle control challenges in multi-input multi-output (MIMO) systems. Hence, this paper proposes a decentralized PID controller that relies on frequency domain specifications. This controller is designed to minimize the loop interactions and fulfill the design criteria of the MIMO system. The PID parameters are calculated based on gain and phase margin specifications. Additionally, a first-order plus dead time model is derived for each of the decoupled subsystems. Furthermore, robust stability is analysed using disc margin analysis, which overcomes the constraints of traditional margins. Moreover, the paper determines a secure range of uncertainty for various levels of gain and phase uncertainties. The findings suggest that the proposed controller achieves robust stability over a broader range of margins when compared to alternative control methods.

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors.

Research on optimizing the controller configuration for high-power supply control system in nuclear fusion based on hybrid algorithm WOA-IAPSO

With the development of nuclear fusion technology, the high power supply control system plays a crucial role in the operation of nuclear fusion, and the PID controller is widely used in the field of power supply control. In this research, a novel hybrid algorithm is proposed that combines whale optimization algorithm (WOA) and the improved adaptive particle swarm optimization (IAPSO) to tune PID parameters in high-power supply. This organically combines the two methods into a new optimization strategy that is different from the simple weighted average method. By comparing the performance of WOA, IAPSO,SA,GA,SVM, and the hybrid WOA-IAPSO in step response under different working conditions, the simulation results show that the proposed algorithm can achieve more sensitive, stable and efficient response in high-power operation. This research is not only of great significance for the further development nuclear fusion technology, but also provides a useful reference for the application of adaptive optimization methods in other fields.

Particle swarm optimization solution for roll-off control in radiofrequency ablation of liver tumors: Optimal search for PID controller tuning.

The study investigates the efficacy of a bioinspired Particle Swarm Optimization (PSO) approach for PID controller tuning in Radiofrequency Ablation (RFA) for liver tumors. Ex-vivo experiments were conducted, yielding a 9th order continuous-time transfer function. PSO was applied to optimize PID parameters, achieving outstanding simulation results: 0.605% overshoot, 0.314 seconds rise time, and 2.87 seconds settling time for a unit step input. Statistical analysis of 19 simulations revealed PID gains: Kp (mean: 5.86, variance: 4.22, standard deviation: 2.05), Ki (mean: 9.89, variance: 0.048, standard deviation: 0.22), Kd (mean: 0.57, variance: 0.021, standard deviation: 0.14) and ANOVA analysis for the 19 experiments yielded a p-value ≪ 0.05. The bioinspired PSO-based PID controller demonstrated remarkable potential in mitigating roll-off effects during RFA, reducing the risk of incomplete tumor ablation. These findings have significant implications for improving clinical outcomes in hepatocellular carcinoma management, including reduced recurrence rates and minimized collateral damage. The PSO-based PID tuning strategy offers a practical solution to enhance RFA effectiveness, contributing to the advancement of radiofrequency ablation techniques.

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

BackgroundChemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary. MethodsFirstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution. Significant findingsThis paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.

Depth control of bionic robotic fish based on fuzzy PID algorithm

Abstract This paper proposes a staged depth control method based on fuzzy PID control to improve the execution of various underwater tasks by robotic fish in pectoral and caudal fin co-propulsion modes in complex underwater environments. The method obtains appropriate PID parameters and regulates the corresponding CPG models by introducing a fuzzy controller into the PID control framework. The method follows the fuzzification, fuzzy rule setting, and defuzzification steps. During the approach phase, the robotic fish adjusts the amplitude and offset of its pectoral fin. In the cruise phase, it adjusts the angle of attack of the pectoral fin to achieve staged depth control. Simulation and analysis results indicate that the staged fuzzy PID control is more effective than pure fuzzy control and PID control. It reduces delay time by 38.5% and 59.4%, optimizes rise time by 31.1% and 44.1%, reduces peak time by 49.7% and 44.6%, and reduces steady-state error by 42.4% and 76.6%. Therefore, the staged fuzzy PID control provides a faster response and greater stability, making it an effective method for depth control of underwater robots.

Modeling and virtual simulation of the boost chopper by DCM using the optimal PIDF control

This work introduces an improved method for modeling and simulating the Boost Converter utilizing Duty Cycle Modulation (DCM) regulated by an optimum PIDF (Proportional-Integral-Derivative with Filter) regulator. We optimized the characteristic parameters of the PIDF regulator for a second-order system generated from its transfer function by using a mix of theoretical study and simulation using the Matlab/LQR tool. The conventional PID parameters in the time domain were converted into their corresponding LQR (Linear Quadratic Regulator) counterparts, allowing for the solution of the Riccati problem and the creation of an optimum state trajectory model. The results of analog virtual simulations done in a Multisim environment indicate that the system has improved dependability. It maintains a high level of accuracy in a stable condition, with no static error and a reaction time of 1.5 ms, without any overshooting. The effectiveness of the optimum PIDF control in regulating the DCM Boost Converter is highlighted by the system's strong ability to handle changes in load during transient states within a time frame of 300 ms. This study represents a substantial enhancement compared to conventional PID-based approaches, providing valuable knowledge about the possible uses in power electronics and control systems.

Cascade PID Control for Altitude and Angular Position Stabilization of 6-DOF UAV Quadcopter

UAVs are commonly used in transportation, especially in the express delivery of light cargo parcels. However, controlling UAVs is difficult because of their complex structure and wide range of operations in space. The research contribution is proposed a cascade control structure using six PID controllers for the 6-DOF UAV quadcopter, that ensures the altitude angulars positions control at the desired values and maintains flight balance stability for the 6-DOF UAV quadcopter. First, the mathematical dynamic models for the 6-DOF UAV quadcopter have been researched and developed, including the translational dynamic mathematical model and the rotational dynamic mathematical model of the 6-DOF UAV quadcopter. This is a complex object with strong nonlinearity and difficult control. And then, the article introduces the method of designing six PID controllers for 6-DOF UAV quadcopter to meet the requirements, based on applying the Ziegler-Nichols experimental method. Applying the Ziegler-Nichols experimental method makes the process of designing a UAV quadcopter control system simple, straightforward and heuristics with fast controller parameters tuning. Next, the article presents the results of modeling and simulation of the 6-DOF UAV quadcopter control system on Matlab/Simulink. The simulation results show that the six proposed PID controllers have ensured the flight balance stability at the desired altitude and angular positions with overshoot less than 20%, steady-state error less than 1%. This shows the prospect of applying the proposed PID control method to physical UAVs, easily adjusting PID parameters to suit the flight environment.

Innovative Fuzzy PID Algorithm: Advancing Quadcopter Control with Adaptive Precision

The integration of fuzzy logic techniques into PID controllers shows promising advantages in quadcopter control. By incorporating fuzzy logic, the PID controller achieves enhanced adaptability and robustness, resulting in more dynamic and resilient control performance. This is achieved through the automatic adjustment of PID parameters based on real-time system feedback, enabling a control strategy that closely resembles human reasoning. Overall, the combination of fuzzy logic and PID control offers significant potential for improving quadcopter control in various environmental conditions.