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Motivated by the benefits that recent finite-time continuous control approaches have proven to give rise, this work aims to design a proportional-integral-derivative (PID) type control scheme for the global regulation of constrained-input mechanical systems that incorporates design features characteristic of such finite-time continuous algorithms. This is proven to be achieved through a more general PID type control structure that incorporates exponential weights on the P and D type terms, through which such control actions are permitted to loose Lipschitz-continuity at the desired equilibrium values. This entails an important challenge consisting on the introduction of an appropriate analytical framework and the development of a suitable closed-loop analysis through which the resulting design is properly supported. The study is complemented by experimental tests which show that appropriate (less-than-unity) values on the incorporated exponential weights indeed give rise to closed-loop improvements characteristic of finite-time continuous control approaches, such as reduction of overshoot on the position error responses and of the control effort, alleviating such a performance adjustment task.

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This research introduces a hardware implementation of DC-DC boost converter designed to elevate the DC voltage generated by renewable sources while effectively regulating it against line and load fluctuations for inverter application. The main objective is to boost the DC link voltage to the level of Vmax in the output AC voltage obtained from inverter circuits. This enables the inverters for transformer-less power conversion from DC to AC to reduce magnetic losses, size and weight of the inverter circuits used in the utility application. The proposed converter's topology and switching sequences play a crucial role in enhancing overall performance. Utilizing a Zero Current Switching (ZCS) technique, the converter efficiently recovers stored energy from the magnetics. The proposed converter attained the output voltage of 350 V at its current of 1A from the input voltage of 20 V at its current of 19 A. The ZCS technique and the topology of the converter enhances the efficiency to 92 %. The study employs traditional Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers for effective voltage regulation, analysing time domain specifications. Additionally, a Fuzzy logic controller is introduced as an alternative to PID controllers to compare their performance metrics, evaluating the optimization of the converter's transient and steady-state behaviours. The proposed converter is designed, simulated and their performance metrics are analysed using MATLAB for both with and without controllers. The step-time characteristics of the proposed converter with load resistance of RL = 500 Ω and an input voltage of Vi = 20 V has been determined and analysed. The PID system attained a rise time of 88.781 ms, an overshoot value of 9.341 %, and a steady-state error of 0.00043. The fuzzy system achieved a low-rise time of 10.624 ms, a low overshoot of 0.55 %, and a steady-state error of 0.0584. The hardware prototype of the proposed converter is implemented with a FPGA based PID and Fuzzy logic controllers for providing better voltage regulation and to improve the performance metrics of the converter. The simulation and experimental findings are contrasted, examined, and confirmed to ensure improved consistency in performance measures.

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Global energy demand is experiencing a notable surge due to growing energy security. Renewable energy sources, like ethanol, are becoming more viable. In the present study, the application of a PSO-PID (Particle Swarm Optimization - Proportional Integral Derivative) controller with a split-range control strategy was suggested for the regulation of temperature within the fermentation system. To optimize performance, a POS-PID controller with a split-range arrangement utilizing two control valves for hot and cold utilities was constructed. The study began by examining the open-loop dynamic response of the system to inlet temperature and concentration disturbances during ethanol production fermentation. Subsequently, a transfer function model was developed through linearization at the steady-state operating point. The split-range controller structure, implemented by optimizing the PSO-PID controller parameters using PSO, effectively demonstrated temperature control in simulations of a nonlinear model. In this investigation, the ethanol fermentation system was modeled as a CSTR using a modified Monod equation for microbial growth kinetics. Various dynamic behavioral disturbances were explored and verified in the model with plant data in this study. The simulation model results were validated through plant data. The proposed method showed superior closed-loop performance with respect to errors, with the actuators proving to be effective than other reported methods for temperature control.

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Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.

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To address the issues of sluggish response and inadequate precision in traditional gate opening control systems, this study presents a novel approach for direct current (DC) motor control utilizing an enhanced beetle antennae search (BAS) algorithm to fine-tune the parameters of a fuzzy proportional integral derivative (PID) controller. Initially, the mathematical model of the DC motor drive system is formulated. Subsequently, employing a search algorithm, the three parameters of the PID controller are optimized in accordance with the control requirements. Next, software simulation is employed to analyze the system's response time and overshoot. Furthermore, a comparative analysis is conducted between fuzzy PID control based on the improved beetle antennae search algorithm, and conventional approaches such as the traditional beetle antennae search algorithm, the traditional particle swarm algorithm, and the enhanced particle swarm algorithm. The findings indicate the superior performance of the proposed method, characterized by reduced oscillations and accelerated convergence compared to the alternative methods.

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The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional-Integral-Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched. The first method verifies whether the value of the control signal sent to the control plant in the previous step is the actual value generated by the controller. The second method relies on detecting sudden, unusual changes in output variables, taking into account the inertial nature of dynamic plants. In the third method, a copy of the controller parameters is used to detect an attack on the controller's parameters implemented in the PLC. The fourth method uses the golden run in attack detection.

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Adaptive laboratory evolution (ALE) is a widely used microbial strain development and optimization method. ALE experiments, to select for faster-growing strains, are commonly performed as serial batch cultivations in shake flasks, serum bottles, or microtiter plates or as continuous cultivations in bioreactors on a laboratory scale. To combine the advantages of higher throughput in parallel shaken cultures with continuous fermentations for conducting ALE experiments, a new Continuous parallel shaken pH-auxostat (CPA) was developed. The CPA consists of six autonomous parallel shaken cylindrical reactors, equipped with real-time pH control of the culture medium. The noninvasive pH measurement and control are realized by biocompatible pH sensor spots and a programmable pump module, to adjust the dilution rate of fresh medium for each reactor separately. Two different strains of the methylotrophic yeast Ogataea polymorpha were used as microbial model systems for parallel chemostat and pH-auxostat cultivations. During cultivation, the medium is acidified by the microbial activity of the yeast. For pH-auxostat cultivations, the growth-dependent acidification triggers the addition of fresh feed medium into the reactors, leading to a pH increase and thereby to the control of the pH to a predetermined set value. By controlling the pH to a predetermined set value, the dilution rate of the continuous cultivation is adjusted to values close to the washout point, in the range of the maximum specific growth rate of the yeast. The pH control was optimized by conducting a step-response experiment and obtaining tuned PI controller parameters by the Chien-Hrones-Reswick (CHR) PID tuning method. Two pH-auxostat cultivations were performed with two different O. polymorpha strains at high dilution rates for up to 18 days. As a result, up to 4.8-fold faster-growing strains were selected. The increased specific maximum growth rates of the selected strains were confirmed in subsequent batch cultivations.

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Unmanned Aerial Vehicles (UAVs) and quadrotors are being used in an increasing number of applications. The detection and management of forest fires is continually improved by the incorporation of new economical technologies in order to prevent ecological degradation and disasters. Using an inner-outer loop design, this paper discusses an attitude and altitude controller for a quadrotor. As a highly nonlinear system, quadrotor dynamics can be simplified by assuming several assumptions. Quadrotor autopilot is developed using nonlinear feedback linearization technique, LQR, SMC, PD, and PID controllers. Often, these approaches are used to improve control and to reject disturbances. PD-PID controllers are also deployed in the tracking and surveillance of smoke or fire by intelligent algorithms. In this paper, the efficiency using a combined PD-PID controllers with adjustable parameters have been studied. The performance was assessed by simulation using matlab Simulink. The computational study conducted to assess the proposed approach showed that the PD-PID combination presented in this paper yields promising outcomes.

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This study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air-fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air-fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.

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In laser beam processing, the angle or offset between the auxiliary gas and the laser beam axis have been proved to be two new process optimization parameters for improving cutting speed and quality. However, a traditional electromechanical actuator cannot achieve high-speed and high-precision motion control with a compact structure. This paper proposes a magnetic levitation actuator which could realize the 5-DOF motion control of a lens using six groups of differential electromagnets. At first, the nonlinear characteristic of a magnetic driving force was analyzed by establishing an analytical model and finite element calculation. Then, the dynamic model of the magnetic levitation actuator was established using the Taylor series. And the mathematical relationship between the detected distance and five-degree-of-freedom was determined. Next, the centralized control system based on PID control was designed. Finally, a driving test was carried out to verify the five-degrees-of-freedom motion of the proposed electromagnetic levitation actuator. The results show it can achieve a stable levitation and precision positioning with a desired command motion. It also proves that the proposed magnetic levitation actuator has the potential application in an off-axis laser cutting machine tool.

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Patients with COVID-19 are not eligible for any therapy. Patients who have had respiratory failure and are unable to provide oxygen via noninvasive means obtain supportive care in ICUs. Since the onset of the outbreak, every sick COVID-19 patient has received oxygen via a mechanical ventilator. This study describes and simulates the transient stability of systems in an automated pressure regulator utilizing a single-acting cylinder in a clinical ventilator. These components include horizontal controllers, control devices, connecting tubes, and PID for electro-pneumatic control. Increased system stability and nonlinearity in electro-pneumatic actuator systems are accomplished by the implementation of PID. The redesigned PID control architecture was enhanced with alternative acceleration feedback through the close loop with an integral control method to get the system stable. This introduces the standard value N from the outside vicious circle and applies a form control law to integrate all reference control supply through into the gadget. Even as proportional gain (Kp) gets increased, the controller output would increase proportionately while maintaining the exact degree of accuracy. A derivative term boosts the ability of the Kd regulator to "detect" malfunctions. The integral term of the Ki controller minimizes its set point distortion. The system was updated to make it feasible for transferrable knowledge and competencies by incorporating real industrial components. The completed fluid control system was simulated through FluidSIM, which is frequently helpful for educational purposes.

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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.

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Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions.

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In order to improve the accuracy of concrete dynamic principal identification, a concrete dynamic principal identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, the apparent stress-strain curves of concrete containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test to decouple and separate the damage and rheology, and this system was modeled by using LSTM network. Secondly, for the problem of low convergence accuracy and easy to fall into local optimum of Dung Beetle Algorithm (DBO), the greedy lens imaging reverse learning initialization population strategy, the embedded curve adaptive weighting factor and the PID control optimal solution perturbation strategy are introduced, and the superiority of IDBO algorithm is proved through the comparison of optimization test with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Algorithm, and Fruit Fly Algorithm and the combination of LSTM is built to construct the IDBO-LSTM dynamic homeostasis identification model. The final results show that the IDBO-LSTM model can recognize the concrete material damage without considering the damage; in the case of considering the damage, the IDBO-LSTM prediction curves basically match the SHPB test curves, which proves the feasibility and excellence of the proposed method.

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This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles-25%, 50%, 75%, and 100%-showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system.

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We designed three new controllers: a sigmoid-based controller, a polynomial dynamic inversion-based controller, and a proportional-integral-derivative (PID) impulsive controller for cancer differentiation therapy. We compared these three controllers to existing control strategies to show the improvement in performance and compare their robustness. The sigmoid-based controller adds a sigmoid term associated with the error of the controlled state and a selected observed state. The sigmoid term is multiplied by a control gain, thereby decreasing the control effort for state transition. The polynomial dynamic inversion-based controller adds a cubic error term in the error dynamic aiming to achieve a shorter convergence time to the desired value of the controlled state. The PID impulsive controller considers the accumulated controlled state error and the rate of change of the controlled state error, thereby forcing the controlled state to converge to the desired value and alleviating the damping effect in the steady state. For the considered cancer network, the 3 new cancer control strategies exhibit superior and robust performance. The PID impulsive controller has a significant improvement in robustness compared to the impulsive controller and has greater potential for cancer differentiation therapy.

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This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the PID system, thereby elucidating its potential for incorporation into AI-enabled radio and networks. This research unveils crucial insights for augmenting the performance and safety parameters of vehicle platooning systems within B5G networks, concurrently underlining the prospective applications of LLMs within such technologically advanced communication environments.

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Inertial sensors can serve as inertial references for space missions and require charge management systems to maintain their on-orbit performance. To achieve non-contact charge management through UV discharge, effective control strategies are necessary to improve the optical power output performances of UV light sources while accurately modeling their operating characteristics. This paper proposes a low-power photo-electro-thermal model for widely used AlGaN-based UV LEDs, which comprehensively considers the interaction of optical, electrical, and thermal characteristics of UV LEDs during low-power operations. Based on this model, an optical power control system utilizing a fuzzy adaptive PID controller is constructed, in which a switch is introduced to coordinate the working state of the controller. Thus, the steady-state performance is effectively improved while ensuring dynamic performance. The results show that the proposed model has an average prediction error of 5.8 nW during steady-state operations, and the fuzzy adaptive PID controller with a switch can reduce the fluctuation of light output to 0.67 nW during a single discharge task, meeting the charge management requirements of high-precision inertial sensors.

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To improve the quality and efficiency of robot grinding, a design and a control algorithm for a robot used for grinding the surfaces of large, curved workpieces with unknown parameters, such as wind turbine blades, are proposed herein. Firstly, the structure and motion mode of the grinding robot are determined. Secondly, in order to solve the problem of complexity and poor adaptability of the algorithm in the grinding process, a force/position hybrid control strategy based on fuzzy PID is proposed which greatly improves the response speed and reduces the error of the static control strategy. Compared with normal PID, fuzzy PID has the advantages of variable parameters and strong adaptability; the hydraulic cylinder used to adjust the angle of the manipulator can control the speed offset within 0.27 rad/s, and the grinding process can be carried out directly without obtaining the specific model of the surface to be machined. Finally, the experiments are carried out, the grinding force and feed speed are maintained within the allowable error range of the expected value, and the results verify the feasibility and effectiveness of the position tracking and constant force control strategy in this paper. The surface roughness of the blade is maintained within Ra = 2~3 µm after grinding, which proves that the grinding quality meets the requirements of the best surface roughness required for the subsequent process.

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This paper plans to establish a warehouse management system based on an unmanned aerial vehicle (UAV) to scan the QR codes printed on packages. This UAV consists of a positive cross quadcopter drone and a variety of sensors and components, such as flight controllers, single-board computers, optical flow sensors, ultrasonic sensors and cameras, etc. The UAV stabilizes itself by proportional-integral-derivative (PID) control and takes pictures of the package as it reaches ahead of the shelf. Through convolutional neural networks (CNNs), the placement angle of the package can be accurately identified. Some optimization functions are applied to compare system performance. When the angle is 90°, that is, the package is placed normally and correctly, the QR code will be read directly. Otherwise, image processing techniques that include Sobel edge computing, minimum circumscribed rectangle, perspective transformation, and image enhancement is required to assist in reading the QR code. The experimental results showed that the proposed algorithm provided good performance of a recognition rate of 94% for the stochastic gradient descent (SGD) and 95% for Adadelta optimization functions. After that, successful QR code reading was presented.