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The time delay (TD) in the levitation control system significantly affects the dynamic performance of the closed-loop system in electromagnetic suspension (EMS) maglev vehicles. Excessive TD can cause levitation instability, making it essential to explore effective mitigation methods. To address this issue, a Smith Predictor (SP) is integrated into the traditional PID levitation control system. The combination of theoretical analysis and numerical simulation is employed to assess the stability of the time-delay levitation control system after the integration of the Smith Predictor. Theoretical analysis reveals that when TD exceeds a critical threshold, the levitation system becomes unstable. The addition of SP alters the root trajectory of the system characteristic equation from positive to negative, and recovers the levitation system to stable status. Assuming complete knowledge of the dynamic system, the TD compensation value in the SP becomes a key parameter that determines its performance. A minimum effective value (MEV) for TD compensation is identified, correlating with the system's stability region. Under the influence of TD, more complex systems and higher running speeds of the maglev vehicle lead to a narrower stable region and a larger MEV for TD compensation. Given the simulation parameters in this paper, with a system TD of 15 ms and a maximum vehicle speed of 160 km/h, the MEV for TD compensation in the SP should be set at 12 ms. • Smith Predictor is introduced into maglev control system to mitigate the adverse impact from TD. • Analyzed application value of Smith Predictor in levitation control system with time delay. • Provided suggestions for the broader implementation of Smith Predictor in practical engineering. [ABSTRACT FROM AUTHOR]

Smart earth station antennas have been used for several decades in many applications, from satellite communications to space object detection and tracking. The accuracy of the azimuth position for such antennas plays a crucial role in most steerable ground station antennas. Satellite tracking and space object detection demand precise tracking capabilities from the Earth. Several methods and techniques have been developed and used in industry to control the directions of ground station antennas, including the azimuth position. The challenge of azimuth tracking is increasing with the demand for full-sky coverage and with the exponential increase in space objects, including man-made satellites and operational and nonoperational objects; thus, providing accurate tracking is a key technology that demands continuous enhancement and development. This article presents the use of a PID-proportional-integral-derivative controller, a slide mode controller and a fractional order PID controller. It also introduces a new methodology based on model predictive control (MPC). The manuscript provides the core design for each of these controllers and provides insight into the performance of each controller even in the presence of disturbance. The camel optimization algorithm (COA) was used to obtain the optimal design parameters of each controller in the considered scenarios. [ABSTRACT FROM AUTHOR]

This research covers the process of heat exchange in a cooling microunit equipped with Peltier modules. We put forward that by choosing the control algorithm, not only the control signal quality in such a system is affected but also its energy consumption. Tests were carried out for the following algorithms: relay, parallel PID, serial PID, and PID + DD. An experimental setup was developed that allowed for recording the step response of the investigated plant. Next, the transfer function of the plant was formulated, and a simulation model of the control system was developed using the MatLab®-Simulink environment. Through computer simulation for a selected system operation procedure (cooling down to three set temperatures and maintaining them for 5000 s), the quality of control signals and the influence on energy use were investigated. The cumulative energy value for each of the algorithms and the cumulative difference in energy consumption between the controllers were calculated. The best results in terms of control quality were obtained for the parallel PID controller. The lowest energy consumption was observed for the relay controller, with the difference compared to other investigated controllers reaching 4.3% and 9.0%, without and with the presence of signal disturbances, respectively. [ABSTRACT FROM AUTHOR]

In recent years, increasing attention has been given to reducing energy consumption in hydraulic excavators, resulting in extensive research in this field. One promising solution has been the integration of hydrostatic transmission (HST) and hydraulic pump/motor (HPM) configurations in parallel systems. However, these systems face challenges such as noise, throttling losses, and leakage, which can negatively impact both tracking accuracy and energy efficiency. To address these issues, this paper introduces an intelligent real-time prediction framework for system positioning, incorporating particle swarm optimization (PSO), long short-term memory (LSTM), a gated recurrent unit (GRU), and proportional–integral–derivative (PID) control. The process begins by analyzing real-time system data using Pearson correlation to identify hyperparameters with medium to strong correlations to the positioning parameters. These selected hyperparameters are then used as inputs for forecasting models. Independent LSTM and GRU models are subsequently developed to predict the system's position, with PSO optimizing four key hyperparameters of these models. In the final stage, the PSO-optimized LSTM-GRU models are employed to perform real-time intelligent predictions of motion trajectories within the system. Simulation and experimental results show that the model achieves a prediction deviation of less than 3 mm, ensuring precise real-time predictions and providing reliable data for system operators. Compared to traditional PID and LSTM-GRU-PID controllers, the proposed controller demonstrated superior tracking accuracy while also reducing energy consumption, achieving energy savings of up to 10.89% and 2.82% in experimental tests, respectively. [ABSTRACT FROM AUTHOR]

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV's positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA). [ABSTRACT FROM AUTHOR]

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]

Unmanned aerial vehicles (UAVs) have seen a rise in use during the last few years. Such aircrafts are now a convenient way to complete dangerous, dirty, and tedious tasks. Given that their operation involves a control problem which is non-linear and coupled, it is difficult to analyse. This paper presents the modeling and control of a fixed-wing unmanned aircraft as a contribution to this field. The system's flight dynamics is derived using Newton's second law of motion. The system is designed to have a non-linear Proportional Integral Derivative (NPID) controller and a higher-order sliding mode controller (HOSMC). When simulating the system using MATLAB Simulink software, an external disturbance was added to test the robustness of the controllers. Five performance indices which include mean square error (MSE), integral time square error (ITSE), integral absolute error (IAE), integral time absolute error (ITAE), and integral square error (ISE), were used to compare the controllers performance. These indices are used to provide a numerical assessment of the two controllers' performance. The outcomes demonstrate that the roll, pitch, and yaw states performed better than the super-twisting sliding mode controller. On the airspeed control, the non-linear PID performed better than the super-twisting sliding mode controller. [ABSTRACT FROM AUTHOR]

Transfer of laboratory-scale experiments to production-scale ethanol fermentation is time-consuming and involves expensive prototype systems from complex experimental designs that determine optimal operating conditions for minimal substrate and product inhibitions. The study developed and validated a Simulink-based model for optimal pH and temperature control using fuzzy logic and PID controllers respectively and taking advantage of 2D and 1D substrate and product inhibition models from which suitable ethanol fermentation reaction rates models were selected. Temperature and pH levels and substrate, product, and biomass concentrations were measured. Selected inhibition models were linear-product, linear substrate-sudden stop product, and linear substrate for cassava, maize, and sorghum, respectively. Fuzzy logic controller ascertained optimal flow rate of acid and base as 0.000196 ml/s and 0.000204 ml/s, respectively, and pH error and rate of pH error as 0.00334 and 0.00368, respectively. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1905; maize: F critical 0.9704, F calculated = 0.2149; sorghum: F critical = 0.9704, F calculated = 0.2488). PID logic controller showed model curves and experimental curves with good fit. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1288; maize: F critical = 0.9704, F calculated = 0.2083; sorghum: F critical = 0.9704, F calculated = 0.2016). The study provided an improved approach as solution for optimal pH and temperature conditions in order to mitigate substrate and product inhibitions during ethanol fermentation. It illustrated that the application of artificial intelligence-based controllers provides satisfactory outcomes that are desirable for implementation in the industrial space. Highlights: ✓ Inhibition models were selected for three feed stocks using the model fitness coefficient and applied to ethanol fermentation batch dynamic model. ✓ Selected inhibition models included the following: linear-product, linear substrate-sudden stop product, and linear substrate for cassava, maize, and sorghum, respectively. ✓ Fuzzy logic controller determined optimal pH profile, flow rates of acid and base, pH error, and rate of pH error. ✓ There was no significant difference between model and experimental profiles (cassava: F critical = 0.9704, F calculated = 0.1905; maize: F critical 0.9704, F calculated = 0.2149; sorghum: F critical = 0.9704, F calculated = 0.2488). ✓ PID logic controller determined optimal temperature profile. ✓ There was no significant difference between model and experimental profiles (cassava: F critical = 0.9704, F calculated = 0.1288; maize: F critical = 0.9704, F calculated = 0.2083; sorghum: F critical = 0.9704, F calculated = 0.2016). [ABSTRACT FROM AUTHOR]

The tuning of fractional-order proportional-integral-derivative (FOPID) controllers for automatic voltage regulator (AVR) systems presents a complex challenge, necessitating the solution of real-order integral and differential equations. This study introduces the Dumbo Octopus Algorithm (DOA), a novel metaheuristic inspired by machine learning with animal behaviors, as an innovative approach to address this issue. For the first time, the DOA is invented and employed to optimize FOPID parameters, and its performance is rigorously evaluated against 11 existing metaheuristic algorithms using 23 classical benchmark functions and CEC2019 test sets. The evaluation includes a comprehensive quantitative analysis and qualitative analysis. Statistical significance was assessed using the Friedman's test, highlighting the superior performance of the DOA compared to competing algorithms. To further validate its effectiveness, the DOA was applied to the FOPID parameter tuning of an AVR system, demonstrating exceptional performance in practical engineering applications. The results indicate that the DOA outperforms other algorithms in terms of convergence accuracy, robustness, and practical problem-solving capability. This establishes the DOA as a superior and promising solution for complex optimization problems, offering significant advancements in the tuning of FOPID for AVR systems. [ABSTRACT FROM AUTHOR]

Load frequency regulation (LFR) is an indispensable scheme in planning electrical power production to provide consumers with stable, reliable and uninterrupted power. In the face of complicated power system (PS) structures with increasing and intricate power demand, new controllers that offer not only good performance, but also easy commissioning in practice are required. To this end, this research introduces an exponential PID (EXP-PID) controller as a new control scheme to ameliorate the LFR performance of PSs. This controller is simple to design and has a nonlinear feature inherited from two tunable exponential functions, which are placed in front of the PID controller and act on the error signal and its time derivative individually. To achieve the utmost performance, the EXP-PID controller's parameters are procured by a corrected variant of the snake optimizer (co-SO). To validate the proposed control scheme, various single-/multi-area single-/multi-source PSs favored in the area are considered as test benches. A thorough comparison with the state-of-the-art approaches is performed to disclose the true efficacy of our proposal. Among the rivals, co-SO tuned EXP-PID controller, despite its simplicity, is found to render credible and promising performance in mitigating frequency and tie-line power deviations effectively. • An EXP-PID controller is introduced for LFR of power systems. • The new controller enjoys advantages of nonlinear feature and adaptive gains. • Corrected variant of snake optimizer is presented for controller optimization. • Several power systems are studied for performance validation. • The dominance of our proposal is vividly affirmed over the state-of-the-art. [ABSTRACT FROM AUTHOR]

This study proposes a direct synthesis-based two-degree-of-freedom (2-DOF) controller for various types of integrating processes with time delays. This 2-DOF controller includes a proportional-integral-derivative (PID) controller to enhance load disturbance rejection performance and a set-point filter to improve servo response performance. The main PID controller parameters are expressed as process model parameters and a single adjustment variable, while the set-point filter is composed of PID controller parameters with weighted factors. The adjustment variable is tuned to achieve an optimal balance between response performance and robustness, based on the maximum magnitude of the sensitivity function (Ms). Controller parameters for various Ms values and guidelines for setting these parameters are provided in a consistent formulaic form using a curve-fitting method. These parameter-setting formulas facilitate the accurate implementation of PID controllers with specified Ms values and allow the controller design to be extended to processes with larger dimensionless time delays for a given Ms value. Although a 2-DOF controller was proposed, the adjustment variable for setting the parameters of the main PID controller and the set-point filter was solely the desired time constant. The proposed method was applied to various integrating processes with time delays, and its performance was compared with existing methods reported in the literature, based on performance indices such as settling time, overshoot, integral of absolute error, total variation in input usage, and global performance index. Simulations were conducted using six examples of various integrating processes with time delays to verify the effectiveness and applicability of the proposed controller. • All parameters of a PID controller are given the same form of formulas. • Formulas for PID controller parameter setting are derived through a curve fitting method • Controller parameters that almost match the specified level of robustness are obtained. • In 2-DOF PID controller including a set-point filter, there is only one adjustment variable. [ABSTRACT FROM AUTHOR]

In order to realize effective laser communication on underwater mobile platforms, the active tracking and alignment technology of underwater laser communication (UWLC) is studied. Firstly, the servo control principle of the UWLC system is analyzed. Secondly, aiming at underwater disturbance, an adaptive fuzzy PID controller is designed to realize parameter self-tuning, thereby improving the system's anti-disturbance ability. The designed algorithm was simulated, and the simulation results show that the adaptive fuzzy PID algorithm has better anti-disturbance ability and tracking performance than the traditional PID. Finally, an experimental platform was built for dynamic tracking experiments, and the results show that the dynamic tracking accuracy of the designed control algorithm was improved by 30.29% compared with the traditional control algorithm, which provides a certain reference for the development of laser communication on underwater moving platforms. [ABSTRACT FROM AUTHOR]

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks. [ABSTRACT FROM AUTHOR]

The hydraulic support face guard system is essential for supporting the exposed coal wall at the working face. However, the hydraulic support face guard system approaching the coal wall may cause impact disturbances, reducing the load-bearing capacity of coal walls. Particularly, the hydraulic support face guard system is characterized by a large turning radius when mining thick coal seams. A strong disturbance and impact on the coal wall may occur if the approaching speed is too fast, leading to issues such as rib spalling. In this paper, a feedforward fuzzy PID displacement velocity compound controller (FFD displacement speed compound controller) is designed. The PID controller, fuzzy PID controller, feedforward PID controller, and FFD displacement speed compound controller are compared in terms of the tracking characteristics of the support system and the impact response of the coal wall, validating the controller's rationality. The results indicate that the designed FFD displacement speed compound controller has significant advantages. This controller maintains a tracking error range of less than 1% for target displacement with random disturbances in the system, with a response adjustment time that is 34% faster than the PID controller. Furthermore, the tracking error range for target velocity is reduced by 8.4% compared to the feedforward PID controller, reaching 13.8%. Additionally, the impact disturbance of the support system on the coal wall is suppressed by the FFD displacement speed compound controller, reducing the instantaneous contact impact between the support plate and the coal wall by 350 kN. In summary, the FFD compound controller demonstrates excellence in tracking responsiveness and disturbance rejection, enhancing the efficacy of hydraulic supports, and achieving precise control over the impact on the coal wall. [ABSTRACT FROM AUTHOR]

DC–DC buck converters have been designed by incorporating different control stages to drive the switches. Among the most commonly used controllers, the sliding mode control (SMC) and proportional-integral-derivative (PID) controller have shown advantages in accomplishing fast slew rate, reducing settling time and mitigating overshoot. The proposed work introduces the implementation of both SMC and PID controllers by using the field-programmable gate array (FPGA) device. The FPGA is chosen to exploit its main advantage for fast verification and prototyping of the controllers. In this manner, a DC–DC buck converter is emulated on an FPGA by applying an explicit multi-step numerical method. The SMC controller is synthesized into the FPGA by using a signum function, and the PID is synthesized by applying the difference quotient method to approximate the derivative action, and the second-order Adams–Bashforth method to approximate the integral action. The FPGA synthesis of the converter and controllers is performed by designing digital blocks using computer arithmetic of 32 and 64 bits, in fixed-point format. The experimental results are shown on an oscilloscope by using a digital-to-analog converter to observe the voltage regulation generated by the SMC and PID controllers on the DC–DC buck converter. [ABSTRACT FROM AUTHOR]

Aiming to address the problems of nonlinearity, a large time delay, poor adjustment ability, and a difficult parameter setting process of the tension control system of belt conveyor tensioning devices, an adaptive Proportional-Integral-Derivative (PID) parameter self-tuning algorithm based on an improved seeker optimization algorithm (ISOA) is proposed in this paper. The algorithm uses inertia weight random mutation to determine step size. An improved boundary reflection strategy avoids the defect of a large number of out-of-bound individuals gathering on the boundary in a traditional algorithm, and projects the individual reflection beyond the boundary into the boundary, which increases the diversity of the population and improves the convergence accuracy of the algorithm. To improve the system response speed and suppress the overshoot problem of the control target, coefficients related to the proportional term are introduced into the fitness function to accelerate the convergence of the algorithm. The improved algorithm is tested on three test functions such as Sphere and compared with other classical algorithms, which verify that the proposed algorithm is better in accuracy and stability. Finally, the interference and tracking performance of the ISOA-PID controller are verified in industrial experiments, which show that the PID controller optimized using the ISOA has good control quality and robustness. [ABSTRACT FROM AUTHOR]

In ex vivo heart perfusion (EVHP), the control of aortic pressure (AoP) is critical for maintaining the heart's physiologic aerobic metabolism. However, the complexity of and variability in cardiac parameters present a challenge in achieving the rapid and accurate regulation of AoP. In this paper, we propose a method of AoP control based on deep reinforcement learning for EVHP in Langendorff mode, which can adapt to the variations in cardiac parameters. Firstly, a mathematical model is developed by coupling the coronary artery and the pulsatile blood pump models. Subsequently, an aortic pressure control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. This method enables the regulation of the blood pump and the realization of closed-loop control. The control performance of the proposed DDPG method, the traditional proportional–integral–derivative (PID) method, and the fuzzy PID method are compared by simulating single and mixed changes in mean aortic pressure target values and coronary resistance. The proposed method exhibits superior performance compared to the PID and fuzzy PID methods under mixed factors, with 68.6% and 66.4% lower settling times and 70.3% and 54.1% lower overshoot values, respectively. This study demonstrates that the proposed DDPG-based method can respond more rapidly and accurately to different cardiac conditions than the conventional PID controllers. [ABSTRACT FROM AUTHOR]

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Aiming at the electric vehicle charging pile control system has the characteristics of multi-parameter, strong coupling and non-linearity, and the existing traditional PID control and fuzzy PID control methods have the problems of slow charging speed, poor control performance and anti-interference ability, as well as seriously affecting the service life of the battery, this paper designs a kind of improved particle swarm algorithm to optimize the PID controller of the charging control system for electric vehicle charging piles, and utilizes the improved particle swarm algorithm to Adaptive and precise adjustment of proportional, integral and differential parameters, so that the system quickly reaches stability, so as to improve the accuracy of the system control output current or voltage. Simulation results show that the optimized system response speed of the improved particle swarm algorithm is improved by 3.077 s, the overshooting amount is reduced by 1.01%, and there is no oscillation, which has strong adaptability and anti-interference ability, and can significantly improve the control accuracy and charging efficiency of the charging pile control system. [ABSTRACT FROM AUTHOR]

Upper limb prostheses are commonly propelled by pneumatic artificial muscles organized in an antagonistic arrangement. Nonetheless, the control of upper limb prostheses under changing/unknown situations is difficult and necessary for a variety of real-world applications. Adaptive control, learning-based control, and robust control have been studied to deal with such challenges. However, their adaptability is insufficient for prostheses used in daily life, which are exposed to variable task levels, user motor characteristics, and prosthetic features. This paper introduces a highly adaptive controller for the first time based on Generative Adversarial Nets and proportional–integral–derivative controller (G-PID controller). G-PID controller comprises a generator for generating compensation actions to enhance PID responsiveness when controlling the unknown/changing system. Moreover, it incorporates a discriminator that receives responses from both a user-preselected reference system and the compensated changing/unknown system, and simultaneously determines the source of these responses. Through continuous updates, the compensator modifies the response of unknown/changing system to align with the reference system, thereby facilitating adaptive control. The G-PID controller's effectiveness is evaluated through 1-degree of freedom (DoF) joint and 2-DoF shoulder prostheses in simulation experiments, and further validated in prototype experiments focusing on online learning for unknown and time-varying payload. The results demonstrate its ability to deal with diverse types of unknowns/changes, marking a significant advancement towards incorporating prostheses seamlessly into daily life. [ABSTRACT FROM AUTHOR]

In this study, we aim to explore the birth mechanism of coexisting Strange Nonchaotic Attractors (SNAs) in a multistability system. The underlying mechanism behind the emergence of coexisting SNAs is understood by examining the interruption of coexisting torus-doubling bifurcations. We identify three different routes that lead to the creation of coexisting SNAs, namely, the intermittent route, Heagy–Hammel route, and fractalization route. To effectively characterize SNAs, we employ several metrics, including the nontrivial largest Lyapunov exponents, phase sensitivity exponents, and power spectrum analysis. Notably, SNAs exhibit a fractal (strange) nature, which has inspired us to develop a novel optimization algorithm termed the Strange Nonchaotic Optimization (SNO) algorithm. This algorithm is used to optimize the Proportional-Integral-Derivative (PID) controller parameters in time-delay systems based on a defined objective function. Simulation results validate that the proposed SNO algorithm presents better performance than the available algorithms in the literature for time-delay systems. [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]

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors. [ABSTRACT FROM AUTHOR]

The mechanical systems were modeled using various combinations of mass-damper-spring elements to analyze the system dynamics and improve the system stability. Due to the marginal stability property of the mass-damper rectilinear system, a proper control law is required to control the mass position accurately, improve the relative stability, and enhance the dynamical response. In this paper, a mathematical model of the electromechanical system was first derived and analyzed. Next, a digital PID controller was developed based on the root locus technique, and a systematic design procedure is presented in detail. The proposed digital control system was simulated in MATLAB and compared with other control schemes to check their tracking performance and transient response characteristics. In addition, the digital PID control algorithm of the mass-damper rectilinear system was implemented via dSPACE platform to investigate the real-time control system performance and validate the control design methodology. It has been shown that the digital PID controller yields zero percentage overshoot, fast transient response, adequate stability margins, and zero steady-state error. [ABSTRACT FROM AUTHOR]

This paper presents a model-based PID tuning method for a reactor used in microwave-assisted chemistry. The reactor is equipped with a solid-state source of microwaves and a PID controller capable of increasing or decreasing the delivered microwave power to maintain the reacting substances at the desired temperature. The model has the form of an algorithm applied in numerical simulations of two simultaneous processes: heating a substance by absorbing microwave radiation and cooling it by dissipating heat to the surroundings. It has proven its suitability for tuning the PID controller in a time-efficient manner. Despite some noticeable inaccuracies, the presented approach easily finds PID coefficients that result in stable and repeatable controller operation. In this way, significant time savings can be achieved, as well as minimizing the risks associated with, for example, boiling liquid spills. The article demonstrates that a carefully designed, but still relatively simple, model can yield significant benefits in tuning PID controllers. [ABSTRACT FROM AUTHOR]

The main causes of frequency instability or oscillations in islanded microgrids are unstable load and varying power output from distributed generating units (DGUs). An important challenge for islanded microgrid systems powered by renewable energy is maintaining frequency stability. To address this issue, a proportional integral derivative (PID) controller is designed in this article. Firstly, islanded microgrid model is constructed by incorporating various DGUs and flywheel energy storage system (FESS). Further, considering first order transfer function of FESS and DGUs, a linearized transfer function is obtained. This transfer function is further approximated into first order plus time delay (FOPTD) form to design PID control strategy, which is efficient and easy to analyze. PID parameters are evaluated using the Chien-Hrones-Reswick (CHR) method for set point tracking and load disturbance rejection for 0% and 20% overshoot. The CHR method for load disturbance rejection for 20% overshoot emerges as the preferred choice over other discussed tuning methods. The effectiveness of the discussed method is demonstrated through frequency analysis and transient responses and also validated through real time simulations. Moreover, tabulated data presenting tuning parameters, time domain specifications and comparative frequency plots, support the validity of the proposed tuning method for PID control design of the presented islanded model. [ABSTRACT FROM AUTHOR]

An EPS system is used to improve the stability and safety of the car when steering while also simplifying the steering process. This article introduces a novel control solution for the EPS system called BSSMCPID. This algorithm combines two nonlinear techniques, BS and SMC, with the input signal corrected by a PID technique. This algorithm provides three new contributions compared to existing algorithms: reducing system errors and eliminating phase differences, ensuring stability even when exposed to external disturbances, and reducing power consumption. The system's stability is evaluated according to the Lyapunov criterion, while the algorithm's performance is evaluated based on numerical simulation results. According to the article findings, the RMS error of the steering column angle and steering motor angle values (controlled objects) is approximately zero, and the RMS error of the steering column speed and steering motor speed is about 0.01 rad/s, which is much lower than the results obtained with traditional BS and PID controllers. When the EPS system is controlled by the integrated nonlinear method proposed in this work, the output values always closely follow the reference values with negligible errors under all investigated conditions. Additionally, power steering performance increases as speed decreases or driver torque increases, which follows the ideal assisted power steering curve. In general, the responsiveness and stability of the system are always ensured when applying this algorithm. [ABSTRACT FROM AUTHOR]

The tension system is one of the most critical systems in gravure printed electronic equipment. It possesses a complex structure that spans the entire feeding process, from unwinding through printing to rewinding. This article focuses on the research of multi-span tension control for printing systems. Firstly, the characteristics and requirements of the tension-control system in the printing section were analyzed, and a multi-span tension-control structure was devised. Then, based on the coupled mathematical model of the multi-span tension system, a static decoupling model was formulated, and a first-order active disturbance rejection control (ADRC) controller was designed utilizing active disturbance rejection control technology. Finally, to verify the control performance of the ADRC decoupling controller for the printing tension system, simulation and experimental studies were conducted using MATLAB/Simulink R2018a and a dedicated experimental platform, and the results were then compared with those obtained from a traditional PID controller. The research findings indicate that the designed multi-span tension-control system demonstrates outstanding decoupling performance and anti-interference capabilities, effectively enhancing the tension-control accuracy of gravure printed electronic equipment. [ABSTRACT FROM AUTHOR]

In this paper, a novel switched/modulated capacitor filter scheme is proposed for enhancing vehicle‐to‐house (V2G) battery‐charging stations utilized in electric vehicles (EVs). The novel approach is tested on two controllers with a classical optimized PID controller. The technique, which employs modified multimode weighted charging modes for fast charging, improved power quality, and minimal inrush currents, results in reduced voltage transients on the DC side and less harmonics on the AC side. An intercoupled DC‐AC capacitor interface that features dual complementary switching modes is used by the switched modulated filter as a way to provide optimal pulsing in both the tuned‐arm filter and capacitive compensator modes of operations. This switched intercoupled AC‐DC filter compensation approach leads to enhanced power usage in EVs, along with lower AC‐DC voltage transients and inrush currents. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching–Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications. [ABSTRACT FROM AUTHOR]

The paper presents a metamaterial with reconfigurable effective properties, intended to operate in alternating magnetic fields. The structure of a resonator, based on a series connection of a planar inductor and a lumped capacitor, is expanded using an additional capacitor with a MOSFET transistor. Due to the presence of the controllable active element, it is possible to dynamically change the phase of the current flowing through a meta-cell and shift a frequency response within an assumed range. Since the transistor is driven by the unipolar square wave with a changeable duty cycle and time delay, two closed-loop controllers were utilized to achieve a smart material, able to automatically attain and maintain the imposed resonant frequency. As a result, the complex effective magnetic permeability of the metamaterial can be smoothly changed, during its operation, via an electrical signal, i.e. by adjusting the parameters of a control signal of the active element. The design of the meta-cell, as well as the measuring, data operation and control part, are presented in detail. An illustrative system is examined in terms of achieving the user-defined resonance point of the metamaterial. Transient responses with estimated settling times and steady-state errors and the effective permeability characteristics for the exemplary cases are shown. The meta-cell is also tested experimentally to validate the theoretically determined effective properties. [ABSTRACT FROM AUTHOR]

This paper presents the application of a modified Whale optimization algorithm for fine tuning of PID controller parameters in load frequency control of an interconnected Micro Grid (MG) system consisting of renewable source distributed generations. The Renewable source based DGs like solar (PV cell), wind turbine generators (WT) tidal and biomass generations. Because renewable based DGs energy sources are intermittent. In a power system, controlling the frequency is a difficult task in specific the tie line power and frequency disturb in different operating conditions. To analyse the nonlinearity of a two-area interconnected Micro-Grid an exact model is considered. A multi objective based optimization algorithm is implemented here to find optimal PID control Parameters. The MG system integrated with PID and MWOA, is simulated with different operating conditions like switching off the individual DGs powers for certain period of time and then is reconnected. The conventional PID controller with arbitrary gain selections, is not capable to handle the frequency fluctuations and the tie line power. However, the modified whale optimized algorithm based PID controller overwhelms the challenges and performs better than PID controller in various uncertainties and disturbances. [ABSTRACT FROM AUTHOR]

Robotic arms have become increasingly popular and widely used in various industrial applications. However, conventional control methods are not capable of adequately controlling a robotic arm in tasks that require contact with a surface. To address this issue, this study proposes a Cartesian impedance control method to control a 3-DOF robotic arm in real-time during contact with a surface. The proposed controller consists of two control loops: an inner loop and an outer loop. The inner loop utilizes a motion control method in the joint space, with the parameters of the controller being calculated through system identification. The outer loop implements Cartesian impedance control in the Cartesian space using a mass-spring-damper model. The coefficients of the Cartesian impedance control were determined based on the over-damped response with real-time applications. By selecting the inner loop in the joint space and the outer loop in the Cartesian space, the control of the robotic arm is guaranteed. The proposed method was tested in real-time, and its performance was compared with the PID with gravity compensation control in the Cartesian space. The results indicated that the proposed method was able to successfully follow reference trajectories and reduce the contact force. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties. • We propose an adaptive probabilistic recurrent TSK fuzzy neural PID controller. • The Lyapunov stability criterion is used to update the proposed controller parameters. • The proposed controller is applied practically for controlling a servo motor. [ABSTRACT FROM AUTHOR]

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable. • Practical data-driven design method for FOPID controller is developed. • The proposed method requires only one-shot input/output experimental data. • Closed-loop stability is explicitly considered in the proposed scheme. • Model matching is achieved via numerical optimization. • Numerical simulations show the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents an altitude and attitude control system for a newly designed rocket-type unmanned aerial vehicle (UAV) propelled by a gimbal-based coaxial rotor system (GCRS) enabling thrust vector control (TVC). The GCRS is the only means of actuation available to control the UAV's orientation, and the flight dynamics identify the primary control difficulty as the highly nonlinear and tightly coupled control distribution problem. To address this, the study presents detailed derivations of attitude flight dynamics and a control strategy to track the desired attitude trajectory. First, a Proportional-Integral-Derivative (PID) control algorithm is developed based on the formulation of linear matrix inequality (LMI) to ensure robust stability and performance. Second, an optimization algorithm using the Levenberg–Marquardt (LM) method is introduced to solve the nonlinear inverse mapping problem between the control law and the actual actuator outputs, addressing the nonlinear coupled control input distribution problem of the GCRS. In summary, the main contribution is the proposal of a new TVC UAV system based on GCRS. The PID control algorithm and LM algorithm were designed to solve the distribution problem of the actuation model and confirm altitude and attitude tracking missions. Finally, to validate the flight properties of the rocket-type UAV and the performance of the proposed control algorithm, several numerical simulations were conducted. The results indicate that the tightly coupled control input nonlinear inverse problem was successfully solved, and the proposed control algorithm achieved effective attitude stabilization even in the presence of disturbances. • A newly designed thrust vector control based rocket-type UAV prototype is presented. • The governing equations of the under-actuated and unstable dynamics system are derived. • A robust PID controller based on the formulation of LMI guaranteeing robust performance in the sense of the Lyapunov is designed. • A configuration of the GCRS is designed to realize the thrust vector control. • A LM-based optimal algorithm is presented to solve the nonlinear force inverse mapping problem induced by the GCRS. [ABSTRACT FROM AUTHOR]

Presented in this paper is a direct model predictive control (MPC) for directly controlling the rotor speed and flux of an inverter-fed induction motor, which is also applicable to induction generators. Discrete dynamic model of the induction machine is applied in stationary reference frame with pre-set present and past reference values of the speed and rotor flux for generating the stator current references The uniqueness of the MPC proposed herein is that it does not require outer cascade proportional plus integral (PI) controllers used for regulating rotor speed and flux. Elimination of the outer loop cascade is made possible in this article by the introduction of a unique four-term cost function comprising of weighted magnitudes of the errors between four measured state variables and their reference commands. The four state variables in the unique cost function are the rotor speed, the rotor flux linkage, quadrature axis and direct axis stator currents. The MPC minimizes the cost function at every switching instance by selecting the switching states that gives the least cost function. Consequently, the selected optimal switching states are used to switch optimal inverter output voltages across the machine's stator terminals. As a precursor for obtaining an optimal rotor flux command in the MPC for every torque output, another unique efficiency improvement scheme is developed, which uniquely determines the stator angular velocity and the rotor flux that minimizes the core and copper losses while maintaining a constant slip operation. Therefore, efficacy of the proposed direct model predictive control scheme is verified by comparing its results to results obtained from equivalent vector control on equivalent machine. Results presented show lower loss regime with MPC on optimal stator angular velocity and rotor flux than vector control. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of a fractional-order proportional–integral–derivative (FO-PID) controller against the standard proportional–integral–derivative (PID) controller, applied to a nonlinear robotic arm manipulator systems. The genetic algorithm (GA) optimization method was implemented to tune the gain parameters of the FO-PID and PID controllers. The performance of the FO-PID and PID controllers were evaluated though different cost functions, including integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of time-weighted squared error (ITSE). The performance of these controllers was examined via extensive simulations by using MATLAB/SIMULINK for different operating scenarios of the robotic arm manipulator system. Based on the obtained results, a comparative performance matrix is proposed, wherein cost functions ISE, IAE, ITAE, and ITSE are represented as columns while characteristic parameters (overshoot, rising time, and settling time) are represented as rows. The proposed performance matrix facilitates the selection between the PID and FO-PID controllers. [ABSTRACT FROM AUTHOR]

Bridge crane control systems are complex, multivariable, and nonlinear. However, traditional fuzzy PID control methods rely heavily on expert experience for initial parameter tuning and lack adaptive adjustment for the fuzzy universe. To address these issues, we propose a variable universe fuzzy PID controller based on the improved sparrow search algorithm (ISSA-VUFPID). First, tent chaotic mapping is introduced to initialize the sparrow population, enhancing the algorithm's global search capability. Second, the positioning strategy of the northern goshawk exploration phase is integrated to improve the search thoroughness of sparrow discoverers within the solution space and to accelerate the optimization process. Last, an adaptive t-distribution perturbation strategy is employed to adjust the positions of sparrow followers, enhancing the algorithm's optimization ability in the early search phase and focusing on local exploitation in the later phase to improve solution accuracy. The improved algorithm is applied to tune the initial parameters of the PID controller. Additionally, system error and its rate of change are introduced as dynamic parameters into the scaling factor, which is used to achieve adaptive adjustment of the fuzzy universe, thereby enhancing the safety and reliability of the control system. Simulation results demonstrate that the proposed ISSA-VUFPID control method outperforms ISSA-FPID and ISSA-PID control methods. It reduces the trolley's positioning time and minimizes the load's maximum swing angle, demonstrating strong adaptability and robustness. This approach greatly enhances the robustness and safety of bridge crane operations. [ABSTRACT FROM AUTHOR]

PI-PD controllers have superior performance compared to traditional PID controllers, especially for controlling unstable and integrating industrial processes with time delays. However, computing the four tuning parameters of this type of controller is not an easy task. Recently, there has been significant interest in determining the tuning rules for PI-PD controllers that utilize the stability region. Currently, most tuning rules for the PI-PD controller are presented graphically, which can be time-consuming and act as a barrier to their industrial application. There is a lack of analytical tuning guidelines in the literature to address this shortfall. However, the existing analytical tuning guidelines do not consider a rigorous design approach. This work proposes new robust analytical tuning criteria based on predefined gain and phase margin bounds, as well as the centroid of the stability region. The proposed method has been tested using various simulation studies related to a DC–DC buck converter, a DC motor, and a heat exchanger. The results indicate that the proposed tuning rules exhibit strong performance against parameter uncertainty with minimal overshoots. Furthermore, the suggested technique for simultaneous control of yaw and pitch angles has been tested in a real-time application using the twin rotor multi-input multi-output system (TRMS). Real-time results indicate that, compared to other methods under investigation, the suggested approach provides nearly minimal overshoots. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The challenges of the modern world require mobile robots with the ability to navigate in congested environments with high levels of manoeuvrability. Therefore, the Mecanum wheel may be viable for addressing this challenge. This paper presents the experimental results of a kinematic control strategy, which involves considering the dynamic model as a black box, with only the input and output signals being known. To do this, high‐level and low‐level controllers are formulated and explained. The high level aims to control the desired position of the mobile robot, which can be useful for navigation tasks. On the other hand, low‐level control involves nested controllers to regulate the speed of the mobile robot wheels. Both levels are related and computed through pure kinematics transformations with a dSPACE ds1103 card and MATLAB/Simulink software. In total, 120 experiments were conducted to determine the repeatability of the tests, using the combination of three widely explored control techniques in the literature: proportional‐integral‐derivative (PID), PID plus sliding modes, and PID plus quasi‐sliding modes. The experiments conducted are described in detail, and the results are analysed using statistical indices based on the RMS error and percentage improvement. [ABSTRACT FROM AUTHOR]

With technological advances and industrial upgrading, electro-mechanical actuators (EMAs) have gradually replaced traditional hydraulic actuation systems. During operation, force servo systems inevitably suffer from external force or position disturbances, thus affecting the output performance of the system. Therefore, it is of significant engineering application value to develop EMA anti-disturbance control strategies that exhibit strong robustness and are more easily applicable to engineering practice. In this study, an open-loop transfer function of the system with command signals and disturbance signals as inputs was established based on the nonlinear mathematical models built for the core components of EMAs. To overcome the impact of external position disturbances on the motion performance of the force servo system, a proportional integral derivative (PID) controller was introduced and a high-order transfer function associated with various parameters such as speed and acceleration was derived and obtained as feedforward compensation based on the mathematical model. By incorporating a three-loop PID controller, the impact of external disturbance forces on the motion performance of the position servo system was overcome and the tracking accuracy of the system was also improved. Finally, simulation models were built using AMESim software (AMESim 2020, LMS Imagine.Lab, Roanne, France) and a dual-channel EMA performance testing system was developed. Simulation and test results indicated that both anti-disturbance control methods exhibited strong robustness and excellent anti-disturbance performance, with the control accuracy and dynamic performance almost unaffected by disturbances. This verified the correctness of the single-channel EMA anti-disturbance control strategy and the usability of the simulation model. [ABSTRACT FROM AUTHOR]

The lower limb exoskeleton (LLE) plays an important role in production activities requiring assistance and load bearing. One of the challenges is to propose a control strategy that can meet the requirements of LLE trajectory tracking in different scenes. Therefore, this study proposes a control strategy (DBO–FPID) that combines the dung beetle optimizer (DBO) with feedforward proportional–integral–derivative controller (FPID) to improve the performance of LLE trajectory tracking in different scenes. The Lagrange method is used to establish the dynamic model of the LLE rod, and it is combined with the dynamic equations of the motor to obtain the LLE transfer function model. Based on the LLE model and target trajectory compensation, the feedforward controller is designed to achieve trajectory tracking in different scenes. To obtain the best performance of the controller, the DBO is utilized to perform offline parameter tuning of the feedforward controller and PID controller. The proposed control strategy is compared with the DBO tuning PID (DBO–PID), particle swarm optimizer (PSO) tuning FPID (PSO–FPID), and PSO tuning PID (PSO–PID) in simulation and joint module experiments. The results show that DBO–FPID has the best accuracy and robustness in trajectory tracking in different scenes, which has the smallest sum of absolute error (IAE), mean absolute error (MEAE), maximum absolute error (MAE), and root mean square error (RMSE). In addition, the MEAE of DBO–FPID is lower than 1.5 degrees in unloaded tests and lower than 3.6 degrees in the hip load tests, with only a few iterations, showing great practical potential. [ABSTRACT FROM AUTHOR]

This research focuses on a novel convertible unmanned aerial vehicle (CUAV) featuring four rotors with tilting capabilities combined with a tapered form. This paper studies the transition motion between multirotor and fixed-wing modes based on the mechanical and aerodynamics design as well as the control strategy. The proposed CUAV involves information about design, manufacturing, operation, modeling, control strategy, and real-time experiments. The CUAV design considers a fixed-wing with tiltrotors and provides the maneuverability to perform take-off, hover flight, cruise flight, and landing, having the characteristics of a helicopter in hover flight and an aircraft in horizontal flight. The manufacturing is based on additive manufacturing, which facilitates the creation of a lattice structure within the wing. The modeling is obtained using the Newton–Euler equations, and the control strategy is a PID controller based on a geometric approach on SE(3). Finally, the real-time experiments validate the proposed design for the complete regime of flight, and the research meticulously evaluates the feasibility of the prototype and its potential to significantly enhance the mission versatility. [ABSTRACT FROM AUTHOR]

Dual-mode coupling drive system can improve the dynamic performance of electric vehicles through mode switching, and the quality of mode switching directly affects the comfort of drivers and passengers. Mechanical coupling on the left and right sides of the single actuator causes mutual interference during shifting, resulting in prolonged power interruption time and shifting shock. Therefore, this paper analyzes the mutual interference mechanism of single-actuator shifting, designs a new dual actuator, and proposes a staged fuzzy PID controller. Finally, the effectiveness of dual-actuator shifting is proven through simulations and real vehicle testing. Compared with conventional PID control and BP neural network PID control, the shock degree is reduced by 64% and 50%, which improves the mode-switching quality of the dual-mode coupling drive system. [ABSTRACT FROM AUTHOR]

Linear–quadratic regulator (LQR) is a control technique successfully implemented for several complex systems. However, LQR technique in certain cases fails due to disturbances mainly caused by the improper choice of weighting matrices Q and R. Methods using particle swarm optimisation (PSO) are used to find the weighting matrices of LQR and have been proven to be superior. The paper proposes an output feedback LQR proportional–integral–derivative (PID) controller, which is also compared with LQR and PSO-based LQR. The output feedback LQR–PID provides a better response, and the effectiveness of the proposed method is demonstrated through simulations, and experimental validations are conducted on various cases. [ABSTRACT FROM AUTHOR]

Gas turbine engines have several advantages over piston reciprocating engines, such as higher output per unit volume, reduced vibration, rapid acceleration and deceleration, high power output, and clean exhaust gases. As a result, their use for propulsion in ships has been steadily increasing. However, gas turbine engines exhibit significant parameter variations depending on the rotational speed, making the design of controllers to ensure system stability while achieving satisfactory control performance, a very challenging task. In this paper, a novel CEM-based 2-DOF PID controller design technique is proposed to ensure the stability of a gas turbine engine while improving tracking and disturbance rejection performance. The proposed controller consists of a PID controller focused on enhancing disturbance rejection performance and a set-point filter to improve tracking performance. The set-point filter is composed of gains from the controller and a single weighting factor. When tuning the gains of the controller, the maximum sensitivity is considered to maintain an appropriate balance between system stability and response performance. The key novelty of this study can be summarized in two main points. One is that the controller is designed by matching characteristic equations, and by setting the roots of the desired characteristic equation as multipoles, the gains of the PID controller can be tuned with only one adjusting variable, making the tuning of the 2-DOF controller easier. The other is that the controller parameters are tuned based on maximum sensitivity, thus taking into account the robust stability of the control system. To demonstrate the feasibility of the proposed method, simulations are conducted for four scenarios using various performance indices. [ABSTRACT FROM AUTHOR]

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes. [ABSTRACT FROM AUTHOR]