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A bridge hybrid isolation system, integrating both active and passive control strategies, is proposed and investigated to enhance seismic performance. The system is modeled as a two-layer structure, with the upper layer subjected to active control via a Proportional–Integral–Derivative (PID) controller, and the lower layer employing a conventional passive base isolation system. The displacement response of the superstructure is minimized by the control forces generated by the PID controller, which also accounts for the reaction forces transmitted to the lower isolation layer. To optimize the controller's performance, a genetic algorithm is implemented for real-time tuning of the PID parameters. Numerical simulations, conducted using the Newmark method, are employed to assess the influence of active control on the lower isolation system. The results reveal that, while active control increases the peak displacement of the superstructure to some extent, it significantly prolongs the structural period, thus enhancing the system's overall seismic resilience and stability. [ABSTRACT FROM AUTHOR]

Aerodynamic modeling and control law design methods are crucial foundational technologies that enable efficient maneuvering flight of aircraft. Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. To conduct this, a modified NACA0012 airfoil with a tail functioning as an elevator is employed as the geometry model. Through the utilization of the rigid dynamic grid method and overlapping grid technology, the pitch of the airfoil and the deflection of the elevator are efficient to execute. Firstly, the pitching moment coefficient is sampled through both steady and unsteady computations. Then, multivariate nonlinear regression (MNR) models and artificial neural network (ANN) models are established based on steady and unsteady sampling data, respectively. Additionally, the obtained model is evaluated using open‐loop control laws. Based on the evaluation, the proportional‐integral‐derivative (PID) control algorithm is used to design the airfoil pitching control law using the MNR model. Meanwhile, deep reinforcement learning (DRL) is used to design the pitching control law using the ANN model. Finally, the PID and DRL controllers are implemented in a CFD environment for airfoil pitching control to verify their effectiveness in application scenarios. The results suggest that models based on both steady and unsteady data can reflect dynamic aerodynamic characteristics. However, using unsteady computation for data sampling significantly reduces time consumption compared to steady computation. Furthermore, the model constructed by ANN may have unexpected excellent characteristics. Both the PID and DRL control laws, designed based on the models, perfectly complete the control process in the CFD environment. This study provides valuable insights for the implementation of controllable maneuvering flight in aircraft. [ABSTRACT FROM AUTHOR]

In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems. [ABSTRACT FROM AUTHOR]

This article presents the design of a control strategy for a lower limb gait training system catering to patients with Spinal Cord Injury (SCI) or stroke. The system operates by driving the hip and knee joints individually through pneumatic cylinders. The focus lies on the study and development of a control strategy for the pneumatic actuators within the gait training system, specifically targeting trajectory tracking control of pneumatic double-acting cylinders utilizing a PID Controller. The experiment setup comprises a pneumatic cylinder regulated by a proportional valve, incorporating feedback via position and pressure sensors. The experimental results show that the system exhibits good trajectory-tracking performance, particularly at low frequencies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents the synthesis results of a robust H∞ (H-infinity) loop-shaping controller combined with a PID (Proportional-Integral-Derivative) controller using fuzzy logic for the pitch autopilot of a tail control missile. The results take into consideration the change of mass, moment of inertia, and aerodynamic coefficients. The combination of the control signal of the robust controller and the PID controller is formed on the basis of adjusting the membership function of fuzzy logic according to the ITAE (Integral of Time-multiplied Absolute value of Error) optimization criterion. The performance and robustness of the missile autopilot are verified by nonlinear simulation. [ABSTRACT FROM AUTHOR]

Industrially, one of the techniques used to enhance the optimum production of petrochemicals is the configuration of process control units such as gas chromatography (G.C. Analyzer) for concentration and thermocouple for temperature in the production system. This research focused on the application of the principles of mass and energy conservation in the development of dynamic or unsteady state models for predicting the behaviour of the system or process involved in the production of ethylene glycol from a reaction involving oxidation of ethylene-to-ethylene oxide which further undergoes hydrolysis to produce ethylene glycol in a continuous stirred tank reactor (CSTR). The high mathematical complexities of the mass and energy balance models of the Process as a result of the reaction kinetic scheme of the nonelementary reaction of the process which integrated both linear and non-linear terms into the models were resolved by the application of the principles of Taylor's series expansion, Laplace Transform, partial fraction and matrix in the development of the dynamic models. Process simulation tool (Simulink) was utilized in the configuration of closed-loop systems with thermocouple and G. C. Analyzer, at the feed point of the reactor for process variables (temperature and concentration) control during the process using proportional, integral and derivative (PID) as controller parameters. The results of the process behaviour showed that manual tuning of different controller parameters experienced a high level of fluctuations and would not attain its stability or steady state even at a processing time above 16 seconds. Whereas, automatic tuning of desired controller gains of 1.361, 1.056 and 0.4109 for KP, KI and KD for temperature and pressure with a tuning time of 143.9 Seconds, requires a maximum time of 8 seconds for the system to attain stability. This study showed that automated tuning of the controller resulted in better performance characteristics for optimum production of ethylene glycol in a non-isotheral continuous stirred tank reactor within the shortest possible time. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the tracking performance and antidisturbance ability of the widely used proportional-integral-derivative (PID) controllers in practice. Towards this end, we consider a basic class of second-order nonlinear stochastic control systems subject to model uncertainties and external disturbances, and focus on the ability of the classical PID controller to track time-varying reference signals. First, under some suitable conditions on the system nonlinear functions, reference signals, and external disturbances, we show that such control systems can be stabilized in the mean square sense, provided that the three PID gains are selected from a stability region constructed in the paper. Besides, it is shown that the steady-state tracking error has an upper bound proportional to the sum of the varying rates of reference signals, the varying rates of external disturbances, and the intensity of random noises. Meanwhile, its proportional coefficient depends on the selection of PID gains, which can be made arbitrarily small by choosing suitably large PID gains. Finally, by introducing a desired transient process which is shaped from the reference signal, a new PID tuning rule is presented, which can guarantee both the expected steady state and transient tracking performance. [ABSTRACT FROM AUTHOR]

The experimental setup of HVAC testing requires easy but accurate instrumentation, and recent analyses focus on the control based on the mean radiant temperature in order to better perform respect to the users satisfaction. Indeed, the research contribution of this work is the use of Arduino to develop a PID control for an electric heater. As a main feature, this control system can operate by maintaining the set-point on the air temperature or on the mean radiant temperature of the environment where it is placed. The paper describes the design and development of the PID control, as well as the testing of the control system applied to an electric heater, to heat a room in a laboratory in Bologna (Italy). In an Appendix the Matlab script employed to store data on the local pc an to send data to Google Sheets is reported, together with the Google script code employed to write the data from Matlab to the online sheet. As result, the developed PID is accurate in maintaining the required set-point temperature, with minimal deviations from the set-point, in the interval ± 0.2 K. [ABSTRACT FROM AUTHOR]

Ship course-keeping control is of great significance to both navigation efficiency and safety. Nevertheless, the complex navigational conditions, unknown time-varying environmental disturbances, and complex dynamic characteristics of ships pose great difficulties for ship course-keeping. Thus, a PSO-based predictive PID-backstepping (P-PB) controller is proposed in this paper to realize the efficient and rapid course-keeping of ships. The proposed controller takes the ship's target course, current course, yawing speed, as well as predictive motion parameters into consideration. In the design of the proposed controller, the PID controller is improved by introducing predictive control. Then, the improved controller is combined with a backstepping controller to balance the efficiency and stability of the control. Subsequently, the parameters in the proposed course-keeping controller are optimized by utilizing Particle Swarm Optimization (PSO), which can adaptively adjust the value of parameters in various scenarios, and thus further increase its efficiency. Finally, the improved controller is validated by carrying out simulation tests in various scenarios. The results show that it improves the course-keeping error and time-response specification by 4.19% and 9.71% on average, respectively, which can efficiently achieve the course-keeping of ships under various scenarios. [ABSTRACT FROM AUTHOR]

In this research paper, a comprehensive performance analysis was carried out for a 48-watt transformerless DC-DC boost converter using a Proportional–Integral–Derivative (PID) controller through dynamic modeling. In a boost converter, the optimal design of the magnetic element plays an important role in efficient energy transfer. This research paper emphasizes the design of an inductor using the Area Product Technique (APT) to analyze factors such as area product, window area, number of turns, and wire size. Observations were made by examining its response to changes in load current, supply voltage, and load resistance at frequency levels of 100 and 500 kHz. Moreover, this paper extended its investigation by analyzing the failure rates and reliability of active and passive components in a 48-watt boost converter, providing valuable insights about failure behavior and reliability. Frequency domain analysis was conducted to assess the controller's stability and robustness. The results conclusively underscore the benefits of incorporating the designed PID controller in terms of achieving the desired regulation and rapid response to disturbances at 100 and 500 kHz. The findings emphasize the outstanding reliability of the inductor, evident from the significantly low failure rates in comparison to other circuit components. Conversely, the research also reveals the inherent vulnerability of the switching device (MOSFET), characterized by a higher failure rate and lower reliability. The MATLAB® Simulink platform was utilized to investigate the results. [ABSTRACT FROM AUTHOR]

This work is a response to the appeal of various international health organizations and the Automatic Control Community for collaboration in addressing Coronavirus/COVID-19 challenges during the initial stages of the pandemic. Specifically, this study presents scientific evidence supporting the efficacy of three primary non-pharmacological strategies for pandemic mitigation. We propose a control system to aid in formulating a public decision policy aimed at managing the spread of COVID-19 caused by the SARS-CoV-2 virus, commonly known as coronavirus. The primary objective is to prevent overwhelming healthcare systems by averting the saturation of intensive care units (ICUs). In the context of COVID-19, understanding the peak infection rate and its time delay is crucial for preparing healthcare infrastructure and ensuring an adequate supply of intensive care units equipped with automatic ventilators. While it is widely recognized that public policies encompassing confinement and social distancing can flatten the epidemiological curve and provide time to bolster healthcare resources, there is a dearth of studies examining this pivotal issue from the perspective of control system theory. In this study, we introduce a control system founded on three prevailing non-pharmacological tools for epidemic and pandemic mitigation: social distancing, confinement, and population-wide testing and isolation in regions experiencing community transmission. Our analysis and control system design rely on the susceptible-exposed–infected–recovered–deceased (SEIRD) mathematical model, which describes the temporal dynamics of a pandemic, tailored in this research to account for the temporal and spatial characteristics of SARS-CoV-2 behavior. This model incorporates the influence of conducting tests with subsequent population isolation. An On–off control strategy is analyzed, and a proportional–integral–derivative (PID) controller is proposed to generate a sequence of public policy decisions. The proposed control system employs the required number of critical beds and ICUs as feedback signals and compares these with the available bed capacity to generate an error signal, which is utilized as input for the PID controller. The control actions outlined involve five phases of "Social Distancing and Confinement" (SD&C) to be implemented by governmental authorities. Consequently, the control system generates a policy sequence for SD&C, with applications occurring on a weekly or biweekly basis. The simulation results underscore the favorable impact of these three mitigation strategies against the coronavirus, illustrating their efficacy in controlling the outbreak and thereby mitigating the risk of healthcare system collapse. [ABSTRACT FROM AUTHOR]

A modern power system demands an open communication channel to support the vast number of real-time data exchanges, which may introduce time delays and communication failures thus creates new challenges in power systems. To cope up with these issues, the paper proposed an Internal Model-Based Robust Controller (IMBRC) and IMBRC-PID controller designs for the decentralized LFC (Load Frequency Control) of the modern power system. Initially, a finite-ordered linear model of the power system integrated with RES (Renewable Energy Sources) and aggregated Electrical Vehicles (EV) has been developed. Later the full-order model was employed in the proposed design to achieve complete decentralized, robust, more reliable, and effortless control performances. The Internal Model Compensator (IMC) filter time constant is tuned using Artificial Bee Colony (ABC) optimization algorithm. The objective function considered was the scalarized integral of squared and absolute errors with various weighting factors. The Least-Square Model (LSM) approximation of the IMBRC transfer function determines the PID controller gains. The controller's robustness is verified for the power system components affected by structured and unstructured uncertainties. The error performance indices and simulation results convey that the suggested design keeps the system robustly stable even when subject to varying time delays and uncertainties. [ABSTRACT FROM AUTHOR]

This paper presents an auto‐tuning method for dc‐ac voltage source converters (VSCs) with an inductive load that is based on the modified relay feedback test (MRFT). Controllers tuned by the proposed method can produce near‐optimal dynamic performance for dc–ac VSCs without requiring knowledge of the plant's parameters. The controller consists of a PID cascaded with a digital notch filter. The tuning involves two steps. In the first step, the MRFT is used to tune the digital notch filter to compensate for the LCL resonance resulting from the LC filter's interaction with the inductive load. In the second step, one more MRFT is used to tune the PID controller using optimal tuning rules that are derived in advance (offline). The proposed optimal for the class of the dc–ac VSCs tuning rules can guarantee a specified phase margin and near optimal transient performance for any randomly picked VSC. The performance of the proposed tuning of the PID + notch filter is verified by simulations and experiments. The dynamic response of the auto‐tuned controller compares well to the response of a theoretically optimal but non‐auto‐tuned PID + notch filter designed using the full knowledge of the LC filter and the load. [ABSTRACT FROM AUTHOR]

PID control is widely used for designing controllers in production plants and automotive and robotic applications. Although it suits linear systems, with minor modifications, it can also be applied to nonlinear plants. As a simple control approach, it can be easily realized conceptually, but there is a large cost involved in the production of the working controller. A digital PID controller based on a fixed point representation of the operands can be considered for approximate realization. The field programmable gate array (FPGA), with its concurrent architecture, is an ideal way of producing programmable, cost‐, power‐, and speed‐optimized controllers with a compromised controller area compared to application‐specific approaches. In this research, the PID controller for quadcopters is initially tested for performance using MATLAB/Simulink based on continuous/analog domain operations. Then, FPGA‐based architecture mapping is performed using HDL Coder, with fixed‐point‐based operands produced using the fixed‐point conversion toolbox. With satisfactory performance obtained with system generator‐based cosimulation of the controller, Vivado IDE is used to implement the controller on a Zynq Ultrascale+ based FPGA device. The realized cost‐efficient controller is analysed for logical resources, computation power and controller frequency and inferred to be optimal with reference to all these indices. By adapting the thrust, which is the control input for the translational motion of the quadcopter, robustness against coupled motion is ensured, with higher simultaneous tracking speeds. [ABSTRACT FROM AUTHOR]

Autonomous driving technology heavily depends on accurate and smooth path tracking. Facing complex urban driving scenarios, developing a suite of high-performance and robust parameters for controllers becomes imperative. This paper proposes a stochastic simulation-based optimization model for optimizing the Proportional–Integral–Differential (PID) controller parameters, with tracking accuracy and smoothness as bi-objectives, and solves it using a domination-measure-based efficient global optimization (DMEGO) algorithm. In this model, the tracking accuracy and smoothness are indexed by the normalized dynamic time warping (NDTW) and the mean absolute lateral acceleration (MALA), respectively. In addition, we execute the PID controller in a realistic simulation environment using a CARLA simulator, which consider various city scenes, diverse routes, different vehicle types, road slopes, etc., to provide a comprehensive and reliable evaluation for the designed PID controller. In the DMEGO method, each solution undergoes evaluation using a fixed number of costly simulations. Then, utilizing the solutions and their estimated bi-objective values, two surrogate models for the bi-objectives are constructed using the Gaussian process (GP) model. The preliminary nondominated solutions can be obtained by optimizing the two surrogate models. Finally, a novel performance metric known as the domination measure is employed to evaluate the quality of each solution. This metric is then integrated with the crowding distance to selectively retain a candidate solution exhibiting superior performance and good diversity for the next iteration. In our numerical experiments, we first test the DMEGO algorithm against three other counterparts using a stochastic FON benchmark. The proposed approach is then employed to optimize the PID parameters considering the complexity and uncertainty of urban traffic. The numerical results demonstrate that the nondominated solutions obtained by DMEGO exhibit excellent performance in terms of tracking accuracy and smoothness under limited simulation budgets. Overall, the proposed approach may be a viable tool for solving multi-objective simulation-based optimization problem under uncertainties. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of exponential synchronization in continuous-time complex dynamical networks with both time-delayed and non-delayed interactions. We employ a proportional integral derivative (PID) control strategy and a dynamic event-triggered approach to investigate this synchronization problem. Our approach begins with constructing a general model for complex dynamical networks that incorporate delays. We then derive synchronization criteria based on the PID control parameters, utilizing linear matrix inequality techniques in conjunction with a dynamic event-trigger mechanism. The application of Lyapunov stability theory and inequality techniques allows us to establish these criteria, considering the presence of hybrid delays. To illustrate the effectiveness of our proposed model, we provide two numerical examples showcasing synchronization dynamics. These examples demonstrate the successful theoretical results of a novel PID controller and dynamic event-trigger mechanism. [ABSTRACT FROM AUTHOR]

Rotary inverted pendulum (RIP) is a classical model of control engineering. Paper deals with a PID-adaptive structure which is based on structure of neuron to train Kp, Ki, Kd through operation. In simulation, our adaptive controller is proven to work in larger range than classical PID controller. Through experimental model using STM32F4, we prove vibration of system under adaptive-PID is smaller than under classical PID structure. Then, combination of neuron network (NN) and PID control can be used as simple structure for single-input multi output (SIMO) systems which are similar to RIP. [ABSTRACT FROM AUTHOR]

Copyright of Association of Arab Universities Journal of Engineering Sciences (JAARU) is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wireless networks experience some uncertainties which results from an unpredictable nature of wireless signals, interferences and signal propagation, leading to overall degradation of the entire system. The results lead to packet loss, fading, delays, reduced throughput and low latency. This paper is aimed at using PID Control Techniques to track network uncertainties in a TCP-based network and at the same time cancels it to enhanced the overall system performances. System performance measurement and analysis of the TCP-based network was performed with a link capacity of 3750, 4200 and 4500 packet/seconds respectively in three scenarios. The results showed that the PID control technique recorded sensitivity value of 0.0329dB, 0.0345dB and 0.0821dB. (all are less than 1dB), this means that it achieved good sensitivity levelto network disturbance for faster disturbance rejection capability and reduced sensitivity. I however recommend the PID control technique for wireless network improvement to attain enhanced performance, stability and good QoS. [ABSTRACT FROM AUTHOR]

Electrical Spark Discharge Method (ESDM), a chemical pollution-free preparation method used for the preparation of nano-Ag colloids, does not require additional surfactants or other chemical dispersants. In this study, ESDM and a developed micro-electrical discharge machining (m-EDM) with real-time monitoring capability were used to prepare nano-Ag colloids. The m-EDM enabled the identification of hardware defects such as Vgap instability, fluctuating potential, insufficient output current, and unfavorable discharge success rate. The design of hardware circuits based on these problems stabilized Vgap and enhanced discharge success rate. Furthermore, the five proportional-integral-derivative (PID) adjustment methods (CLASSIC PID, Pessen Integrals Rule, with less overshooting, without overshooting, and Tyreus–Luyben tuning formula) reduced the probability of short circuits and improved overall discharge efficiency. Finally, deionized water was verified to contain nano-Ag particles through ultraviolet–visible spectroscopy and Zetasizer. By combining ESDM with m-EDM based on the Z-N CLASSIC PID, a nano-Ag colloid was obtained with a discharge success rate of 50.54%, a particle size of 7.482 nm, a zeta potential of 39.7 mV, and an absorbance of 0.145. The nanoparticle characteristics were also analyzed through transmission electron microscopy, and particle is in circle shape and nano size. This confirmed that the nanoparticles conformed to nano characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Axial piston pumps provide a number of benefits, including strong pressure resistance, high efficiency, high transmission power, a broad speed range, and a long lifespan. These characteristics have led to their widespread usage in naval applications, construction machines, and hydraulic machinery. On the other hand, axial piston pumps frequently display reduced operating speeds as well as instability because of their inherently nonlinear properties. In this study, a mathematical model of these changeable (variable) processes is developed using a mix of theoretical calculations and acquired data. An investigation of variable mechanism control in axial piston pumps is carried out centered on robust control methods, and the controller is constructed utilizing robust H∞ theory. In terms of resilience, control precision, and system reaction time, simulations show that the H controller surpasses the PID controller. [ABSTRACT FROM AUTHOR]

The study aimed to develop a non-invasive pulmonary vibration device, specifically tailored for pediatric patients, to address a range of pulmonary conditions. The device employs a PID control system to ensure consistent and precise vibrations. The primary contribution of this research is the successful development, testing, and implementation of this innovative device. Utilizing technical components such as an Arduino, a vibration DC motor, and an ADXL335 accelerometer, the device was engineered to deliver stable and continuous vibrations even when subjected to external pressures or interactions with the patient. Controllers, including P, PI, PD, and PID types, were rigorously compared. The Ziegler-Nichols tuning technique was applied for meticulous evaluation of vibration control specifically within the context of this noninvasive pulmonary vibration device. Our findings revealed that the PID controller displayed superior accuracy in vibration control compared to P, PI, and PD controllers. Clinical trials involving pediatric patients showed that the PID-controlled device achieved treatment outcomes comparable to conventional methods. The device's precise control of vibration strength provides an added benefit, making it a well-tolerated, non-invasive treatment option for various pulmonary conditions in pediatric patients. Future research is necessary to assess the long-term effectiveness of the device and to facilitate its integration into standard clinical practice. In summary, this study represents a significant advancement in pediatric pulmonary care, demonstrating the critical role that PID control systems adapted for non-invasive pulmonary vibration devices can play in enhancing treatment precision and outcomes. [ABSTRACT FROM AUTHOR]

The appearance of limit cycle oscillations in control systems with fixed threshold based samplers degrades the performance of the control loop, accelerates the wear out of actuators, and introduces an unnecessary communication overhead in distributed control systems. In this paper, the role of the input signals to the control loop is taken into account when analyzing the existence of limit cycles induced by fixed threshold samplers. With this analysis, a methodology to re-tune PID controllers while running to avoid limit cycle oscillations generated by ramp-like excitation signals is presented. Implementation guidelines and several examples to illustrate the usefulness of the method are provided. • Biased limit cycle oscillations induced in a kind of fixed sampled loops are studied. • A methodology to retune the controller to correct the bias effect is proposed. • The robustness assured by the original controller is granted after the retuning. • The method is applicable to the most commonly found controllers in industry. [ABSTRACT FROM AUTHOR]

Underwater robots play a vital role in the exploration and development of marine resources and the inspection and maintenance of offshore platforms. In this paper, the motion control technology of ROV is studied, the kinematics and dynamics of ROV are analyzed, the kinematics and dynamics models of ROV are established, and the degrees of freedom of the models are decouple according to the control requirements. The fluid damping coefficient of ROV was obtained using Fluent software, and an ROV control system based on sliding mode variable structure was designed. The saturation function was introduced into the sliding mode controller to reduce the adverse effects of buffeting. The classical PID controller, fuzzy PID controller, and sliding mode controller designed in this paper were simulated and analyzed by Simulink. A semi-physical simulation platform based on Unity3D was established. It can be seen from the simulation results and the pool experiment results that the performance of the sliding mode controller designed in this paper is better than the classical PID controller and the fuzzy PID controller. The sliding mode control method is used to control the ROV motion, which has better control effect and precision. [ABSTRACT FROM AUTHOR]

Water-soluble polymers are widely employed as additives in many different industries. The need to treat sewage contaminated with water-soluble polymers is essential to prevent persistent pollutants from entering our environment. The advanced oxidation process (AOP), UV/H 2 O 2 process, is used in this study to degrade polyvinyl alcohol (PVA) in aqueous solutions. However, a suitable modeling approach and a control scheme are required to remove the PVA polymer and reduce hydrogen peroxide (H 2 O 2) residual in the treated effluent within a safe level to prevent adverse effects on the aquatic system as well as subsequent biological processes. This study presents black-box modeling for identifying the dynamics of polyvinyl alcohol (PVA) degradation in a series of UV/H 2 O 2 photo-reactors, where the process input and response variables are inlet hydrogen peroxide concentrations and effluent pH, respectively. Data processing, model development, and process simulation are performed using MATLAB R2019b software. In this study, the linear AutoRegressive with eXogenous input (ARX), non-linear ARX (NARX), and Hammerstein-Wiener models are considered as base system models, where the sigmoid-network-based NARX produced the best representation with 82.24% of the trainset and 76.28% of the validation-set of the process dynamics. The design of PID controllers tuned using ARX and sigmoid-network-based NARX models are discussed, and the controller performance is analyzed for set-point tracking and disturbance rejection through simulation studies. The closed-loop response of ARX-PID and NARX-PID are deemed adequate. In fact, the NARX-PID performs much better for the studied process achieving an integrated absolute error (IAE) of 0.6211 with a settling time of 4.63 h, whereas the ARX-PID has lower IAE (0.4238) and produces a more aggressive controlled output response robust in disturbance rejection. Thus, ARX-PID is adequate for frequent process disturbances but less suitable for process set-point tracking. • Development of black-box modeling for identifying dynamics of PVA degradation in a UV/H 2 O 2. • Design of a conventional controller and tuning to minimize H 2 O 2 residual in UV/H 2 O 2 process. • Data-driven linear and nonlinear system identification and controller design applied to UV/H 2 O 2. • Controller parameters were optimized for set-point tracking for the H 2 O 2 residual. • Controller parameter optimization for input, H 2 O 2 dosage, disturbance rejection was studied. [ABSTRACT FROM AUTHOR]

Copyright of Low-Carbon Chemistry & Chemical Engineering is the property of Low-Carbon Chemistry & Chemical Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the performance achievable with a fractional-order PID regulator controlling the Depth of Hypnosis (measured via the Bispectral Index Scale) through the administration of propofol during the maintenance phase of total intravenous anesthesia. In particular, two different methodologies were applied to tune the controller: in the first case, genetic algorithms (GAs) were used to minimize the integrated absolute error, while in the second case, the isodamping approach—a method that targets phase margin invariance with respect to the process dc gain—was employed. In both cases, the performance was extensively analyzed and compared with that of a standard PID controller by simulating multiple patients through a Monte Carlo method. The results demonstrate that a fractional-order PID controller can be effectively used to control the Depth of Hypnosis, but the improvement with respect to a standard PID controller is marginal. [ABSTRACT FROM AUTHOR]

In this paper, a new optimal reduced order fractionalized PID (ROFPID) controller based on the Harris Hawks Optimization Algorithm (HHOA) is proposed for aircraft pitch angle control. Statistical tests, analysis of the index of performance, and disturbance rejection, as well as transient and frequency responses, were all used to validate the effectiveness of the proposed approach. The performance of the proposed HHOA‐ROFPID and HHOA‐ROFPID controllers with Oustaloup and Matsuda approximations was then compared not only to the PID controller tuned by the original HHO algorithm but also to other controllers tuned by cutting‐edge meta‐heuristic algorithms such as the atom search optimization algorithm (ASOA), Salp Swarm Algorithm (SSA), sine‐cosine algorithm (SCA), and Grey wolf optimization algorithm (GOA). Simulation results show that the proposed controller with the Matsuda approximation provides better and more robust performance compared to the proposed controller with the Oustaloup approximation and other existing controllers in terms of percentage overshoot, settling time, rise time, and disturbance rejection. [ABSTRACT FROM AUTHOR]

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions. [ABSTRACT FROM AUTHOR]

The automation and intelligence of transportation vehicles is the focus and hotspot of the future research and development of the entire transportation industry, which can effectively improve a series of social problems such as traffic accidents, exhaust pollution, and traffic jams caused by the current increase in the number of transportation vehicles. With the rapid development of rail transit in our country, more and more people choose urban rail transit for travel, which is greatly convenient for travel. The comfort of Automatic Train Operation (ATO) system is an inevitable consideration when people choose it. Aiming at the comfort of ATO system, this paper designs a train operation target curve that can meet the comfort index of train. At the same time, two simulation models are established by using the SIMULINK module of MATLAB software to compare the experiments. One model is the train simulation model based on PID control, and the other model is the train simulation model based on fuzzy PID. The final simulation results show that fuzzy PID control has stronger superiority in train comfort in the process of train motion simulation, and the traditional PID control is not as good as fuzzy PID control in train comfort. [ABSTRACT FROM AUTHOR]

This paper describes a methodology to obtain a neuro-fuzzy model of a hydro-generator unit (HGU) and its PID controller from the identification of its linear time-invariant (LTI) model. The study performs a cause-effect record, which allows a continuous time identification of the LTI type, then obtaining a classic PID controller capable of complying with a performance specification given by a pole of a second order system, which enables the training of the proposed neuro-fuzzy model. Applying the cost function of the root mean square error and the root of the percentage relative mean square error, the system parameters were adjusted using the decreasing gradient method. By means of linear models, the initialization of the singletons of the neuro-fuzzy models was done in two stages using the decreasing gradient and a cost function. The first stage was carried out without dynamics and the second stage with the dynamics of the simulated system. A case study of the Hydro-Agoyán HGU was selected and the results showed that the development of the LTI model allowed the development of the neuro-fuzzy model able to represent the behavior of the power plant and its response under variations of the power setpoint of 5, 10, 15 and 20%. [ABSTRACT FROM AUTHOR]

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The present work proposes the use of a hybrid controller combining concepts of a PID controller with LQR and a feedforward gain to control the positioning of a 2 DOF robotic arm with flexible joints subject to non-ideal excitations. To characterize the performance of the controls, two cases were studied. The first case considered the positioning control of the two links in fixed positions, while the second case considered the situation in which the second link is in rotational movement and the first one stays in a fixed position, representing a system with a non-ideal excitation source. In addition to the second case, the sensitivity of the proposed controls for changes in the length and mass of the second link in the rotational movement was analyzed. The results of the simulations showed the effectiveness of the controls, demonstrating that the PID control combined with feedforward gain provides the lowest error for both cases studied; however, it is sensitive to variations in the mass of the second link, in the case of rotational movements. The numerical results also revealed the effectiveness of the PD control obtained by LQR, presenting results similar to the PID control combined with feedforward gain, demonstrating the importance of the optimal control design. [ABSTRACT FROM AUTHOR]

In this study, we aimed to find optimal PD controller gains to control orientation and position of a Dodecarotor UAV with minimum trajectory error. In this context, a cascaded PD controller approach which has velocity feedback in the inner loop and position feedback in the outer loop was adopted for each state (roll, pitch, yaw, altitude) in the flight control of the UAV. Subsequently, a fitness function was defined based on the system's time domain response and trajectory tracking error for each state, except the yaw angle, which is non-dominant in terms of trajectory tracking performance. Grey Wolf Optimizer (GWO) was used to obtain PD gains by minimizing the defined fitness function. At the same time, Particle Swarm Optimizer was used in order to benchmark the obtained results from GWO and to avoid a shallow solution space. The obtained PD controller parameters as a result of the optimization study of both algorithms were implemented to the system and the results were compared with each other. Finally, the gains that provided the best results for both algorithms were compared with each other and the results were discussed in terms of the time domain results and the actuator input smoothness. It has been observed that the GWO optimized controller provides a 40-46% improvement over PSO in all four different mass UAVs in terms of reducing axis position errors. [ABSTRACT FROM AUTHOR]

Today, we are witnessing an increasing trend in the number of soft pneumatic actuator solutions in industrial environments, especially due to their human-safe interaction capabilities. An interesting solution in this frame is a vacuum pneumatic muscle actuator (PMA) with a bellow structure, which is characterized by a high contraction ratio and the ability to generate high forces considering its relatively small dimensions. Moreover, such a solution is generally very cost-effective since can be developed by using easily accessible, off-the-shelf components combined with additive manufacturing procedures. The presented research analyzes the precision positioning performances of a newly developed cost-effective bellow PMA in a closed-loop setting, by utilizing a Proportional-Integral-Derivative (PID) controller and a Linear Quadratic Regulator (LQR). In a first instance, the system identification was performed and a numerical model of the PMA was developed. It was experimentally shown that the actuator is characterized by nonlinear dynamical behavior. Based on the numerical model, a PID controller was developed as a benchmark. In the next phase, an LQR that involves a nonlinear pregain term was built. The point-to-point positioning experimental results showed that both controllers allow fast responses without overshoot within the whole working range. On the other hand, it was discovered that the LQR with the corresponding nonlinear pregain term allows an error of a few tens of micrometers to be achieved across the entire working range of the muscle. Additionally, two different experimental pneumatic solutions for indirect and direct vacuum control were analyzed with the aim of investigating the PMA response time and comparing their energy consumption. This research contributes to the future development of the pneumatically driven mechatronics systems used for precise position control. [ABSTRACT FROM AUTHOR]

Aiming to solve the problems of poor dynamic response characteristics and the weak anti-jamming capability of the conventional proportional–integral–derivative (PID) controlled pump-motor servo system (PMSS) under the actual working environment, this study created a brand new hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithm to determine the best parameters of the PID controller for the PMSS speed control to make the PMSS achieve a constant speed control. We developed a GWOPSO-PID controller and compared it with a conventional PID controller, GWO-PID, PSO-PID, and GA-PID. In comparison to the other four control methods, the simulation and experimental results demonstrate that the designed GWOPSO-PID control had better dynamic response characteristics, with its rise times being reduced by 78.6%, 64.7%, 67.1%, and 41.5%, respectively. Additionally, the system under the GWOPSO-PID control exhibits a good stability and robustness even in the face of different load circumstances, with decreases in the re-equilibration times of 59.6%, 23.4%, 53.2%, and 41.9%, respectively, with a significantly improved immunity to disturbances. [ABSTRACT FROM AUTHOR]

This paper develops a novel Proportional–Integral–Derivative (PID) tuning method for multi-agent systems with a reinforced self-learning capability for achieving the optimal consensus of all agents. Unlike the traditional model-based and data-driven PID tuning methods, the developed PID self-learning method updates the controller parameters by actively interacting with unknown environment, with the outcomes of guaranteed consensus and performance optimization of agents. Firstly, the PID control-based consensus problem of multi-agent systems is formulated. Then, finding the PID gains is converted into solving a nonzero-sum game problem, thus an off-policy Q-learning algorithm with the critic-only structure is proposed to update the PID gains using only data, without the knowledge of dynamics of agents. Finally, simulations are given to verify the effectiveness of the proposed method. • A data-driven PID self-learning methodology is developed. • The data-driven PID based control protocols for reaching the consensus of all agents. • Virtual control protocols facilitate the learning of PID controller parameters. • Theoretical proofs and analysis of convergence of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The landmark Ziegler–Nichols (ZN) tuning method from 1942 for proportional‐integral‐derivative (PID) controller has had significant impacts on the industrial control industry to this day, even though it is thought to be an empirical method with little theoretical justifications. The situation has not changed much despite the fact that a large amount of research and applications has been reported over decades. This paper shows for the first time that the ZN tuning can be incorporated into the framework of active disturbance rejection (ADR), where the process dynamics is made to match the desired one. This leads to a unifying tuning method for the recently proposed disturbance rejection PID (DR‐PID) controller, applicable to a wide range of industrial processes. Unlike the original ZN method, this new tuning method can be fully understood by the practitioner in their own language of bandwidth and phase characteristics. Moreover, it can also be fully validated via rigorous stability analysis. Finally, extensive simulation results are provided to illustrate the effectiveness of the unifying ZN tuning method. [ABSTRACT FROM AUTHOR]

We study the sampled‐data implementation of extended PID control using delays for the nth‐order stochastic nonlinear systems. The derivatives are approximated by finite differences giving rise to a delayed sampled‐data controller. An appropriate Lyapunov–Krasovskii (L‐K) method is presented to derive linear matrix inequalities (LMIs) for the exponential stability of the resulting closed‐loop system. We show that with appropriately chosen gains, the LMIs are always feasible for small enough sampling period and stochastic perturbation. We further employ an event‐triggering condition that allows to reduce the number of sampled control signals used for stabilization and provide L2‐gain analysis. Finally, three numerical examples illustrate the efficiency of the presented approach. [ABSTRACT FROM AUTHOR]

This article developed a new method to described the set of stabilizing PID control. The method is based on D‐parameterization with natural description of the set. It was found that the stability crossing surface is a ruled surface that is completely determined by a curve known as discriminant. The discriminant is divided into sectors at the cusps. Corresponding to the sectors, the stability crossing surface is divided into positive and negative patches. A systematic study is conducted to identify the regions with a fixed number of right half‐plane characteristic roots. The crossing directions of characteristic roots for positive patches and negative patches are also studied. As a result, a systematic method is developed to identify the regions of PID parameter such that the system is stabilized. [ABSTRACT FROM AUTHOR]

This article presents a PID control design for a class of planar uncertain nonlinear systems with bounded time‐varying input delay. The delay is unknown with a known bound. The class of systems is characterized by the bounds on the growth rates of the nonlinear function in the system model. Based on these bounds on the growth rates, the system is placed in a linear differential inclusion, a convex hull with four linear systems as vertices. For a given set of PID control coefficients, a Lyapunov–Krasovskii functional is constructed that leads to sufficient conditions, in the form of linear matrix inequalities in the bound on the time delay, a positive scalar and two positive definite matrices, under which the equilibrium solution corresponding to the desired set point of the outputs of all vertex linear systems and, hence, that of the original uncertain nonlinear system, is globally uniformly asymptotically stable. With the bound on the time delay as a variable, an optimization problem with linear matrix inequalities as constraints can be formulated to determine the largest admissible bound on the time delay for the closed‐loop system under the given PID controller. By viewing the PID control coefficients as additional free variables, the optimization problem is then adapted for the design of the PID coefficients for the largest admissible bound on the time delay. The design is then applied to a magnetic suspension system. Simulation results illustrate the design algorithm and the performance of the resulting PID controller. [ABSTRACT FROM AUTHOR]

This article provides a simple low‐order controller design method (including PID controllers as special cases) for a class of unstable systems. First, PID controller design is considered for systems with two unstable poles and pole placement and delay margin issues are discussed. Then, a chain of integrators is considered with arbitrary stable dynamics in cascade. For a given desired minimum delay margin for this class of plants, a PID and low‐order controller design method is obtained in terms of an inequality constraint on the sum of k of the desired closed‐loop poles, where k is number of the integrators in the open‐loop transfer function. [ABSTRACT FROM AUTHOR]

An embedded real-time PID adaptive controller is proposed for three boost converters connected to three LED strings. The voltage control of the LEDs can generate any Red-Green-Blue (RGB) combination in order to obtain a reference RGB color. The LED system control is implemented in real-time using an adaptive multivariable decoupled control, where the voltage tracking for each LED string is performed simultaneously using a boost converter. The adaptive controller is based on an online least squares recursive estimation method and a PID type 2 controller. The proposed adaptive controller is evaluated in simulation over a boost converter and in real-time over three boost converters using the BOOSTXL-C2KLED system and a LAUNCHXL-F28379D microcontroller from Texas Instruments. A detailed analysis is performed in terms of the tracking error, the duty cycle signal, and the current signal. This analysis is shown for the simulated boost converter and also for the multivariate decoupled controller in real-time. [ABSTRACT FROM AUTHOR]

One-linked Rotary Inverted Pendulum is a classical model in control automation. Many directions of development to increase the complication is operated, such as, putting more link to create two-linked, three-linked... systems. Another direction which is less popular is creating a parallel double-linked rotary inverted pendulum. In this paper, we build a PID controller through investigation the operation of system for balancing this model and use genetic algorithm to find suitable control parameters. Genetic algorithm also show its ability in optimizing PID controller under different generations. These surveys are operated in Matlab/Simulink. [ABSTRACT FROM AUTHOR]

The Ball and Beam system with Deviated Axis is a single input-muti output (SIMO) system commonly used in laboratories to test control algorithms. In this paper, we build and investigate an PID-GA controller in simulation and apply to real model. The controller demonstrates the ability to control the balancing statement in different desired positions. Next, we conduct a survey of the above method in the object name Ball and beam system with deviated axis through STM32F4. Through simulation and experiment, our PID controller has successfully controlled the system and GA-PID has optimize well PID parameters. In addition, the control parameters had been adjusted to verify and summerize the theoretical rules. [ABSTRACT FROM AUTHOR]