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ABSTRACT This article addresses a solution to one of the main challenges of online data‐driven control (DDC) methods: reducing the sensitivity of the model‐free adaptive control (MFAC) method to initial conditions and control parameters with the new control cost function and added the output error rate and integral along with a new anti‐wind up strategy for multi‐input multi‐output (MIMO) systems. The parameters introduced to the new control law have been validated using the boundary‐input boundary‐output (BIBO) approach to design and converge the controller. The simulation findings on a nonlinear auto‐regressive moving average model with exogenous inputs (NARMAX) system with triangular control input demonstrate that the proposed control rule will outperform to prototype MFAC. Furthermore, to analyze the sensitivity of the controller to the initial conditions and the uncertainties of the control parameters, 30 Monte Carlo simulations were performed with random initial conditions in the presence of disturbance in the control input, and output noise, and the results were compared with the prototype MFAC and conventional PID controller using standard criteria such as integral time absolute error, standard deviation, steady‐state error, and mean maximum error, which shows a noticeable superiority of proposed controller relative to the prototype MFAC. [ABSTRACT FROM AUTHOR]

Many PID (Proportional plus Integral plus Derivative) controller tuning methods are proposed in the control literature based on different closed-loop specifications. Also, tuning techniques, based on the maximum sensitivity specification, have been proposed in the literature. In this paper, a novel PID controller design method, based on maximum sensitivity, has been proposed. In this work, the PID controller is viewed as a gain and dynamic part and tuned separately. The PID controller dynamic part is tuned using one of the existing popular tuning methods. The tuned dynamic part is cascaded with the process. After that, the PID controller gain is tuned for the maximum sensitivity specification. The maximum sensitivity versus controller gain graph can be generated. The user can choose the controller gain for the desired maximum sensitivity specification. The popular Ziegler & Nichols and IMC (Internal Model Control) methods tune the dynamic part of the system in this work. These proposed techniques can achieve the desired maximum sensitivity by tuning the controller gain in the subsequent stage. Many illustrative instances are considered to display the applicability and the proposed technique’s simplicity. [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]

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

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]

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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]

Summary As wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques. [ABSTRACT FROM AUTHOR]

For systems with uncertain parameters, it is very important to find a controller that can satisfy the preset robustness. The traditional method often ignores the influence of parameter coupling on the set boundary system when substituting the original system with the boundary system group, resulting in the calculated controller value region being too conservative. In this article, noise information is introduced based on affine algorithm to describe uncertain system parameters. Then, based on Kharitonov theorem, a new grouping method for boundary systems is proposed. This method takes the parameter coupling information into account when determining the boundary system, and avoids the problem of interval conservation. On this basis, a virtual phase margin tester is introduced to ensure that the obtained controller parameter range can make the system meet the specific robustness requirements. The results obtained in this article are general and strictly proved. Finally, examples are provided to illustrate the design process and verify the feasibility and efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The main goal of this research is to analyze the natural frequencies and dynamic response, and to utilize nonlinear control techniques for vibration control of a smart nanocomposite sector plate, while taking into consideration the effects of agglomeration and internal pores. The proposed composite configuration includes a porous core layer reinforced with agglomerated GPLs, as well as two layers of piezoelectric sensors and actuators. Both complete and partial agglomeration states are considered based on the Eshelby–Mori–Tanaka approach to predict the effective properties of the nanocomposite. The mechanical properties of the porous core are characterized using an open-cell metal foam with interconnected pores, notable for their low density and high surface area. The governing equations of motion are derived using Hamilton’s principle, which is based on the First-order Shear Deformation Theory (FSDT) plate theory and the finite element method. The nonlinear fuzzy PID controller is designed as a combination of a fuzzy PI component and a nonlinear PD component. The gains of the PD controller are dynamically adjusted using nonlinear gains to optimize its performance. In addition, a comprehensive analysis has been conducted to examine the effects of geometric dimensions, distribution of reinforcement, weight fractions of nanofillers, parameters of agglomeration, porosity coefficient, porosity pattern, and boundary conditions on the natural frequencies and dynamic response of smart porous nanocomposites. Numerical simulations demonstrate the efficacy of the proposed controller in significantly reducing vibration amplitudes compared to velocity feedback. The velocity feedback controller decreases deflection from 24.39μm to 8.37μm, whereas the proposed controller achieves 3.66μm, representing a 56.27% improvement in performance. [ABSTRACT FROM AUTHOR]

Microparticles are increasingly employed as drug carriers inside the human body. To avoid collision with environment, they reach their destination following a predefined trajectory. However, due to the various disturbances, tracking control of microparticles is stilla challenge. In this work, we propose to use an Adaptive Nonlinear PID (A-NPID) controller for trajectory tracking of microparticles. A-NPID allows the gains to be continuously adjusted to satisfy the performance requirements at different operating conditions. An in-vitro study is conducted to verify the proposed controller where a microparticle of 100μm diameter is put to navigate through an open fluidic reservoir with virtual obstacles. Firstly, a collision-free trajectory is generated using a path-planning algorithm. Secondly, the microparticle dynamic model, when moving under the influence of external forces, is derived, and employed to design the A-NPID control law. The proposed controller successfully allowed the particle to navigate autonomously following the reference collision-free trajectory in presence of varying environmental conditions. Moreover, the particle could reach its targeted position with a minimal steady-state error of 4μm. A degradation in the performance was observed when only a PID controller was used in the absence of adaptive terms. The results have been verified by simulation and experimentally. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic quadrotor unmanned aircraft vehicle driven by bidirectional electronic speed controllers is proposed to enhance maneuverability and stability. Bidirectional electronic speed controllers are applied to achieve rapid deceleration of motors during flight. To match with bidirectional electronic speed controllers, fractional order Proportional-Integral-Derivative (PID) controllers are considered to attain better rapidity compared to PID controllers, and an innovative control allocation matrix with direction symbols is developed. The model, controllers, and allocation methods have been proven an effective scheme in simulations of attitude and position tracking. [ABSTRACT FROM AUTHOR]

Among available techniques for lateral tracking in autonomous driving, none possesses the capability to learn from past behaviors and progressively reduce lateral errors. In contrast, our proposed iterative learning control (ILC) scheme significantly enhances tracking performance in vision-based classical control, particularly in challenging environmental conditions. Our vision-based control system incorporates real-time semantic segmentation using the ENet model for unconstructed road area extraction. Linear regression estimates steering adjustments, while a visual PID controller maintains the vehicle’s position at the road’s centerline. This control system’s performance varies with lighting conditions, notably in dense shade, where the vehicle tends to deviate from the desired path. To assess ILC’s potential in error reduction over successive trials, we examined various ILC structures and compared them with a pure PID controller. Despite challenges in directly comparing PID and ILC designs due to changing lighting conditions during experiments, ILC consistently reduced tracking errors and improved path alignment with each iteration. Notably, the degree of error reduction became more pronounced with a greater number of learning gains in the ILC design. Our experimental results underscore the overall effectiveness of ILC in tracking the desired path in autonomous driving scenarios, particularly in varying environmental conditions. [ABSTRACT FROM AUTHOR]

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller's effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources. [ABSTRACT FROM AUTHOR]

A two-axis semi-strapdown stabilized platform is a device designed to eliminate aircraft disturbances and ensure the stability of the sensor's orientation. A traditional two-axis semi-strapdown stabilization platform for aircraft can effectively control disturbance in pitch and yaw channel, but it cannot achieve ideal disturbance control in the roll channel. In order to solve this problem, an adaptive control method based on disturbance transformation and LWOA-PID is proposed. Disturbance transformation is the process of integrating the angular position disturbance of the roll from the previous moment into the combined disturbance of the pitch and yaw at the current moment. This is followed by decoupling the combined disturbance of the pitch and yaw at the current moment, thereby eliminating the disturbance caused by the roll from the previous moment. This process is repeated to achieve the goal of eliminating roll channel disturbances. To ensure the line of sight (LOS) pointing accuracy stability in the two-axis semi-strapdown stabilized platform system for aircraft, a whale optimization adaptive proportional–integral–derivative (LWOA-PID) controller based on Latin hypercube sampling is designed. It is then compared with the classical PID controller in Matlab/Simulink. The simulation results indicate that the disturbance conversion module proposed in this paper can eliminate the impact of roll axis disturbances on the LOS pointing accuracy of the two-axis semi-strapdown stabilized platform for aircraft. Compared to the classical PID controller, the LWOA-PID controller reduces tracking errors for step and sinusoidal signals by 50% and 75%, respectively. It also shortens optimization time by 37.5% compared to the WOA-PID while maintaining the same level of accuracy. Furthermore, when combined with the conversion module, the tracking error is reduced by an additional order of magnitude. [ABSTRACT FROM AUTHOR]

Hummingbirds demonstrate remarkable robustness to wing defects by maintaining stable hovering during the molting process. However, mimicking these capabilities in flapping-wing robots presents significant challenges. To investigate the effectiveness of various controllers in maintaining the attitude of a hummingbird-mimetic flapping-wing robot against external disturbances under different wing defects, this study implemented a PD controller, a PID controller, and a three-loop feedback controller with a disturbance observer (3L-DOB controller). The flapping-wing robot features a pair of wings and controls its body's yaw, pitch, and roll rotations by modulating the tension of wing membranes and the neutral positions of wing torsion. The performance of these controllers was evaluated through semi-tethered experiments on a gimbal, employing three Wing Sets: intact wings, one wing was defective, and both wings were defective. The defective wing emulated hummingbird wing during molting, in which the wing area was cropped by 14.1%. As a result, the 3L-DOB controller showed the best performance in terms of responsiveness and accuracy across all Wing Sets, while the PID controller also achieved comparable performance. [ABSTRACT FROM AUTHOR]

The primary objective of a power system is to provide safe and reliable electrical energy to consumers. This objective is achieved by maintaining the stability of the power system, a multifaceted concept that can be divided into three distinct classes. The focus of this study is on one of these classes, voltage stability. A critical component in maintaining voltage stability is the automatic voltage regulator (AVR) system of synchronous generators. In this paper, a novel control method, the sigmoid-based fractional-order PID (SFOPID), is introduced with the aim of improving the dynamic response and the robustness of the AVR system. The dandelion optimizer (DO), a successful optimization algorithm, is used to optimize the parameters of the proposed SFOPID control strategy. The optimization process for the DO-SFOPID control strategy includes a variety of objective functions, including error-based metrics such as integral of absolute error, integral of squared error, integral of time absolute error, and integral of time squared error, in addition to the user-defined Zwee Lee Gaing's metric. The effectiveness of the DO-SFOPID control technique on the AVR system has been rigorously investigated through a series of tests and analyses, including aspects such as time domain, robustness, frequency domain, and evaluation of nonlinearity effects. The simulation results are compared between the proposed DO-SFOPID control technique and the fractional-order PID (FOPID) and sigmoid-based PID (SPID) control techniques, both of which have been tuned using different metaheuristic algorithms that have gained significant recognition in recent years. As a result of these comparative analyses, the superiority of the DO-SFOPID control technique is confirmed as it shows an improved performance with respect to the other control techniques. Furthermore, the performance of the proposed DO-SFOPID control technique is validated within an experimental setup for the AVR system. The simulation results show that the proposed DO-SFOPID control technique is highly successful in terms of stability and robustness. In summary, this study provides comprehensive evidence supporting the effectiveness and superiority of the DO-SFOPID control technique on the AVR system through both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Aiming at the rich bifurcation and chaotic characteristics in the inverter with proportion integral derivative (PID) controller, the discrete iterative model of such an inverter was derived based on the stroboscopic mapping theory; the nonlinear evolution and the cause of instability in this inverter are analyzed. It was observed that the low‐frequency oscillation following instability was due to the Hopf bifurcation, which will decrease the power supplied quality by the inverter. To address the potential issue of the system instability caused by nonlinear behaviors, an improved exponential time‐delay feedback control scheme was proposed. The controlled object's output current first subtracted its own delay a period of time to form a difference term, which was subsequently fed into an exponential link to make difference with the constant 1. The resulting value was then fed into a proportional link to obtain the control term, which was applied to the PID controlled inverter in a feedback manner. Moreover, the range of the feedback proportional coefficient was solved via the Jury criterion. Finally, the effectiveness of this scheme was verified through the comparative simulations, demonstrating that this scheme can not only increase the stability domain for each parameter by more than 50% in the PID controlled inverter but also stabilize the quasi‐periodic behavior due to the low scale oscillation caused by the Hopf bifurcation at the switching frequency. [ABSTRACT FROM AUTHOR]

A high‐speed railway system is one of the sustainable alternatives to other modes of transportation and may connect the most congested urban cities with minimum carbon emissions. However, the vibration intensity increases as the train's operating speed increases, resulting in deteriorated ride comfort and stability. Hence, this article investigates a 27‐degree‐of‐freedom (DOF) dynamic model of railway vehicles with an improved active suspension system. The decentralized control structure performs the controlling action with five optimized Proportional Integral Derivative (PID) controllers that suppress the vehicle body's vertical, lateral, pitch, roll, and yaw motions. Further, to optimize the PID parameters, three metaheuristic optimization techniques, Genetic algorithm (GA), Grey Wolf Optimization (GWO), and Flower Pollination Algorithm (FPA), are utilized, and their simulated results are compared with the passive system as well as other conventional tuning technique. Moreover, the performance of the proposed control strategy is evaluated in the frequency domain under random track irregularities, and the results are characterized in terms of power spectral densities (PSDs). The simulated results show that among the proposed metaheuristic algorithms, FPA outperforms with a significant reduction in vehicle vibration compared to other tuning methods. The percentage reduction of the vertical, lateral, pitch, rolls, and yaw accelerations is 71.4%, 35.1%, 52.8%, 48.1%, and 38.2%, respectively, ensuring enhanced vehicle ride comfort. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are computational techniques based on the collective behavior of swarms and the study of organisms acting in communities. These algorithms involve different types of organisms. Finding controller values for nonlinear systems is a challenging task using classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multi-input multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems to be more appropriate than using classical methods. In this study, different types of metaheuristic algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm (GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm, and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS. The experimental results show that when the rise and settlement times of the TRMS are compared in terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in this context. [ABSTRACT FROM AUTHOR]

A model predictive control (MPC) method based on Q-learning algorithm, named QMPC, is proposed for weakly damped, nonlinear and open-loop unstable magnetic levitation platform (MLP) systems. In addition, the design of MPC controller for the MLP system, the state space of the MLP system airgap, the action space of the predictive horizon and control horizon, the reward and punishment function are also included in this research. Based on the Simscape and MATLAB/Simulink, the joint simulation of the MLP control system is realized. Compared with PID controller and traditional MPC controller, the simulation results show that QMPC controller has better disturbance rejection ability and tracking performance under six working conditions. [ABSTRACT FROM AUTHOR]

Boost converters often face challenges such as sluggish dynamic behavior, inadequate voltage regulation, and variations in input voltage and load current. These issues necessitate the need for closed-loop operation. Nature-inspired optimization algorithms (NIOA) have demonstrated their effectiveness in delivering enhanced solutions for various engineering problems. Several studies have been conducted on the use of proportional-integral-derivative (PID) controllers for controlling boost converters, as documented in the literature. Some studies have shown that using fractional order PID (FO-PID) controllers can lead to better performance than traditional PID controllers. Nevertheless, implementing FO-PID can be quite complex. Considering the widespread use of commercial PID controllers in industrial settings, this study focuses on finding the best tuning for these controllers in DC-DC boost converters. The approach used is particle swarm optimization (PSO) based on integral performance criteria. Simulation results indicate that the proposed controller achieves superior performance, evidenced by the lowest settling time, overshoot, integral absolute error (IAE), and integral squared error (ISE) values under varying input voltage and load current conditions, compared to both PID and FO-PID controllers. These findings have been confirmed through hardware implementation, which demonstrates the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The role of pumped storage in global energy structure transformation is becoming increasingly prominent. This article introduces a flexible excitation system based on fully controlled device converters into pumped storage units (PSUs). It can address the issues of insufficient excitation capacity and limited stability associated with traditional thyristor excitation systems. The study focuses on linear active disturbance rejection control (LADRC) for the flexible excitation control system of pumped storage units and utilizes intelligent optimization algorithms to optimize the controller parameters. This addresses the inherent problem of traditional PID controllers, which are unable to alleviate the trade-off between response speed and overshoot. At the same time, the robustness and anti-interference of the control system are improved, effectively enhancing the performance of the pumped storage flexible excitation control system. Simulation verifies the feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The hydraulic leveling system plays an important role in the crane load-bearing platform (Referred to as CBP). In recent years, a lot of work has been done to improve the performance of the leveling system. However, all these efforts are focused on control techniques and methods, while ignoring other comprehensive improvement options. This article takes the hydraulic legs subjected to the synchronous drive system of CBP (Referred to as CBP) as the research object, a set of hydraulic synchronous leveling system used for the valve-controlled cylinder was designed, and the mathematical model of transfer function for the valve-controlled cylinder and the theoretical solutions of main parameters in hydraulic oil circuit are obtained, then establish a mechanical model suitable for the attitude position of CBP. After that, a composite leveling strategy applied to the fuzzy PID (Proportional Integral Differential) synchronous leveling system with hydraulic legs driven synchronously was proposed, and a fuzzy PID controller was mapped out independently. Meanwhile, the above-designed sync-drive system coupling synchronous leveling process suitable for the CBP legs was carried out by AMESim and MATLAB/Simulink software. In addition, the correctness and robustness of the simulation model were verified by the leveling experiment, and the validity and reliability of the previously designed oil circuit scheme was proved. The results proposed in this paper improve the hydraulic leveling accuracy and response speed to optimize the performance of the hydraulic leveling system. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model derived from an equivalent electrical circuit to describe the dynamic behavior of the high-voltage terminal of a tandem Van de Graaff accelerator. Two approaches are presented for modeling the transit time of the current flowing through the corona needles. The first one considers an equivalent self-inductance in the corona triode, whereas, in the second one, the transit time is represented by a delay in the corona current. The validation of the proposed models was carried out through experimental tests developed at the National Nuclear Research Institute of Mexico. Furthermore, two strategies for controlling the slow variations of the terminal voltage limited by the slow response of the control loop based on corona discharge are evaluated: a Proportional–Integral–Derivative controller and a sliding mode controller. The Root-Mean-Squared Error calculation leads to the conclusion that both control strategies are suitable for regulating the voltage at the accelerator potential terminal. However, the sliding mode controller leads to an overshoot-free response and a shorter settling time. [ABSTRACT FROM AUTHOR]

This work investigates the design and implementation of a sliding mode controller for a non-isolated dual-input step-up DC–DC converter for electric vehicle applications. The non-isolated dual-input step-up DC–DC converter comprises one inductor, one capacitor, two switches with anti-parallel diodes, and two switches without diodes. The converter analysis is carried out using state space averaged model. The converter with the sliding mode controller is analysed by varying both the input voltage and the load. The performance characteristics of the dual-input DC–DC converter are compared against the conventional PID controller, and it is found that the non-isolated dual-input step-up DC–DC converter with sliding mode controller shows improved performance. The converter thus considered is dynamic and exhibits a high-speed step-up conversion ratio. The sliding mode controller is designed, analysed and it is simulated. The simulation results are validated by developing a prototype model. The converter is simple in construction, highly reliable, and efficient. Further, the converter exhibits less input current ripple and low electromagnetic interference which makes it the best choice for electric vehicle applications. [ABSTRACT FROM AUTHOR]

A hybrid compensation scheme for piezoelectric ceramic actuators (PEAs) is proposed. In the hybrid compensation scheme, the input rate-dependent hysteresis characteristics of the PEAs are compensated. The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model, while the feedback controller is a proportion integration differentiation (PID) controller. In the proposed inverse model, an input rate-dependent auxiliary inverse operator (RAIO) and output of the hysteresis construct the expanded input space (EIS) of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping, and the wiping-out, rate-dependent and continuous properties of the RAIO are analyzed in theories. Based on the EIS method, a hysteresis neural network inverse model, namely the dynamic back propagation neural network (DBPNN) model, is established. Moreover, a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis. Finally, the proposed method, the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii (MRPI) model are all applied in the experimental platform. Experimental results show that the proposed method has obvious superiorities in the performance of the system. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) technology is developing and requires effective and adaptive controllers to enhance performance and be the best sustainable solution to the population concerns caused by oil from other vehicles. This paper proposes a nonlinear Proportional Derivative (PD) controller to control motor speed in EV. The controller's optimal gains are tuned using Gorilla Troops Optimization (GTO). Two classical controllers (PID, PD) are compared with nonlinear PD controller, all tuned controller variables are determined using the GTO algorithm, and the Integral Time Absolute Error (ITAE) fitness function is adopted to maintain the system performance. To simulate the system response, MATLAB (Platforms for m-file and Simulink) is used. The achieved results are compared with the classical controllers (PD &PID) to demonstrate the efficacy of the suggested nonlinear PD controller. The benefits of using the nonlinear hyperbolic function effect on system behavior can be seen in its quick settling time, which is 9.6% faster than the traditional PID controller and 47% faster than the traditional PD controller. It also became apparent in its stable response, which was free of noise and overshoot while the PID controller overshoots with a value of (8.8). While in rise time, the proposed controller with 0.0083s outperforms the PID controller 0.4905s, and the PD controller 0.1315s. According to the simulation results, the nonlinear PD controller settling time is faster and more smoothly (0.0187s) than the PID (0.18s) and PD (0.88s) controllers, which are also optimized with the same GTO tuning algorithm. The suggested controller and the intelligent tuning algorithm guided the system's response to produce the best outputs. The simulation results demonstrated a steady behavior and an effective response in tracking the intended speed value; transient analysis is utilized to explain the performance of the suggested controller. [ABSTRACT FROM AUTHOR]

In the realm of autonomous robotics, navigating differential drive mobile robots through unknown environments poses significant challenges due to their complex nonholonomic constraints. This issue is particularly acute in applications requiring precise trajectory tracking and effective obstacle avoidance without prior knowledge of the surroundings. Traditional navigation systems often struggle with these demands, leading to inefficiencies and potential safety risks. To address this problem, our studies propose an algorithm that integrates machine learning and control concepts, especially through the synergistic software of a Q-learning set of rules and a (PID) controller. This technique leverages the adaptability of Q-learning pathfinding and the precision of PID control for actual-time trajectory adjustment, aiming to beautify the robotics' navigation skills. Our comprehensive technique includes growing a country-area version that integrates Q-values with the dynamics of differential power robots, employing Bellman's equation for iterative coverage refinement. This version enables the robotics' capacity to dynamically adapt its navigation techniques in reaction to instant environmental feedback, thereby optimizing efficiency and protection in actual time. The effects of our full-size simulations exhibit a marked improvement in trajectory-tracking accuracy and impediment-avoidance competencies. These findings underscore the capability of combining machine learning algorithms with traditional methods to increase autonomous navigation technology in robotic systems. Our effects, derived from full-size simulations, suggest that the integration of Q-learning with PID controller markedly improves trajectory tracking accuracy, reduces tour times to targets, and complements the robotics' ability to navigate round barriers. This incorporated method demonstrates a tremendous advantage over conventional navigation systems, providing a sturdy way to the challenges of autonomous robot navigation in unpredictable environments. [ABSTRACT FROM AUTHOR]

For improving the energy efficacy and control performance, integration of swarm optimization with controller design could successfully reach this objective. In this study, a comparative analysis has been conducted between two decentralized control structures based on optimized Proportional-Integral-Derivative (PID) and PID-Proportional (PID-P) controllers for optimal controlling of heating system in multi-zone building. Based on the energy balance equation, the mathematical dynamics model of the heating system is established in the building. In order to enhance and optimize the performances of both controllers, their design parameters are tuned based on Whale Optimization Algorithm (WOA). Two objectives have been considered in the optimization process of heating system. The first objective is to minimize the error in temperature, between the desired and real temperatures, based on IAE (Integral of Absolute Error) index, while the second objective is the minimization of the heat energy consumption. The normalization method has been used to adjust between the two differently-scaled objectives. Simulation results based on MATLAB reveal that the PID-P controller achieved better performance in terms of providing comfort indoor temperature with energy savings as compared to the PID controller. [ABSTRACT FROM AUTHOR]

At present, the controllable parameters of micro turbojet engines in engineering applications are mainly speed-fuel flow (hereinafter referred to as flow) control, in which closed-loop proportional–integral–derivative (PID) control is mostly used to achieve a stable control of engine speed under slow engine conditions. For the optimal adjustment of PID parameters, this paper designs an improved evolutionary strategy for the self-tuning of control parameters in the engine speed and flow control system and formulates an improved PI controller based on a neural network. The simulation experimental results show that the method can realistically achieve stable and fast control of the engine under above slow conditions. [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]

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]

The primary goal of this research is to lessen the high vibration that the model causes by using an appropriate vibration control. Thus, we begin by implementing various controller types to investigate their impact on the system's reaction and evaluate each control's outcomes. The controller types are presented as proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers. We employed PID control to regulate the torsional vibration behavior on a dynamical system. The PID controller aims to increase system stability after seeing the impact of P and PI control. This kind of control ensures that there are no unstable components in the system. By using the multiple time scale perturbation (MTSP) technique, a first-order approximate solution has been obtained. Using the frequency response function approach, the stability and steady-state response of the system at the primary resonance scenario ( Ω 1 ≅ ω 1 , Ω 2 ≅ ω 2 ) are considered as the worst resonance and addressed. Additionally examined are the nonlinear dynamical system's chaotic response and the numerical solution for various parameter values. The MATLAB programs are utilized to attain simulation outcomes. [ABSTRACT FROM AUTHOR]

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero. [ABSTRACT FROM AUTHOR]

The contact behavior greatly influences the damping performance of frictional interfaces. Numerous experimental studies on friction and fretting wear have investigated the evolution of contact parameters. An in-house friction and wear test rig has been developed to obtain hysteresis loops at certain normal forces. However, the test rig lacks load control and is thus unable to ensure precise stabilization at a preset normal force, which affected the hysteresis behavior. In this paper, we developed a frequency-domain PID controller to ensure the stable application of a target normal force with constant (0–300 N) and harmonic (0–50 N) components. Compared to the commonly used time-domain strategy, the control signal error is reduced from 6.30% to 0.54% at 50 Hz. With a 3% error as the standard, the controller enables stabilized control of signals with frequencies up to 300 Hz. Friction experiments on various typical materials are conducted using this improved test rig. The results indicate a general tendency for contact stiffness to increase with a rising normal force, while the relationship between the friction coefficient and the normal force does not exhibit a clear pattern. The contact stiffness is not sensitive to the relative displacement or vibration frequency. [ABSTRACT FROM AUTHOR]

Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship's flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training. [ABSTRACT FROM AUTHOR]

In this paper, we propose a double air chambers artificial muscle to eliminate the hysteresis in the extension and contraction movement of pneumatic artificial muscles. In this paper, the basic structure of the double air chambers artificial muscle is a rubberless artificial muscle with a particularly large hysteresis loop. The double air chambers artificial muscle aims to eliminate hysteresis by directly pressurizing the inside and outside of the air chamber and actively deforming the air chamber. The hysteresis is reduced by the pressure outside of the air chamber (external pressure). Since the appropriate external pressure varies depending on the contraction force and amount of contraction, we proposed a method to regulate the appropriate external pressure by feedback control. The experimental results show that hysteresis was eliminated in the static characteristics. It was also found that the output gain decreased, and the phase lag increased as the target frequency increased. The output gain did not change with increasing load. The phase lag tended to improve with the PID controller compared to the PI controller. These results suggest that the combination of double air chambers artificial muscle and external pressure-regulated feedback control can achieve non-hysteresis position control, and it is useful as an actuator in mechatronic systems. [ABSTRACT FROM AUTHOR]

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

This paper investigates the stability and reliability analysis of a non‐isolated high‐gain DC‐DC converter. The proposed DC‐DC converter produces a high gain output voltage with a lower duty ratio and reduced voltage stress on the semiconductor switch. A reliability analysis is performed to predict the failure rate and lifetime of the individual components using the military handbook (MIL‐HDBK‐217F). A state space equation and small signal modeling of the proposed converter are derived to elucidate the performance of the proposed converter through stability analysis. A closed‐loop using PID controller is incorporated to attain the constant regulated output voltage during sudden parameter changes. The validation of the closed‐loop using the PID controller is verified with the wide range of variations in the different parameters, such as input voltage, load value, and reference voltage value. The laboratory‐based prototype is developed for the proposed converter and validated the performance with 250 W. The power density and efficiency of the proposed converter are 1.360 kW/L and 93%, respectively. [ABSTRACT FROM AUTHOR]

In this study, a novel ultra-local model (ULM) control structure using multi-agent system fuzzy Q learning (MAS-FQL) is proposed for the air-feed system of a polymer electrolyte membrane fuel cell (PEMFC). The primary aim of the control goal is to optimize the net power output of the fuel cell while also preventing oxygen starvation. This is achieved by effectively managing the oxygen excess ratio to maintain it at its optimal value, particularly during rapid load fluctuations. In this study, a new advanced control structure for PEMFCs is first presented to effectively manage the oxygen excess rate in the PEMFC system. This work uses an ULM technique in conjunction with an extended state observer (ESO) to effectively manage the control-related concerns connected with the PEMFC. Furthermore, the inclusion of the MAS-FQL has been used to dynamically manage the gains of the ULM controller in an online adaptive manner. The analysis findings demonstrate that the controller exhibits robustness and has satisfactory performance when subjected to load fluctuations. Across all scenario assessments, the proposed controller consistently exhibits an improvement in oxygen excess ratio regulation of more than 31.32% compared to the proportional integral derivative (PID) controller, more than 17.51% compared to the model-free sliding mode control (SMC) controller, and more than 11.40% compared to the fuzzy PID controller across different performance criteria. • A novel control strategy is proposed for the PEMFC air-feed system control. • The MAS-FQL is employed to dynamically regulate the gains of the ULM controller. • An ESO is included in PEMFC's ULM design. [ABSTRACT FROM AUTHOR]

Structural changes in electrical energy systems are occurring rapidly. Due to the development of technology and the increase in energy consumption, the importance and impact of nanogrids in the energy system is increasing. It is very difficult to sustain nanogrids as offgrid. Therefore, this study thoroughly examines the most efficient controller designs for nanogrids that use fuel cells as their primary source of energy. In addition, Dwarf Mongoose Optimization (DMO), a new effective optimization technique, is used to determine controller gains in nanogrid systems for the first time in the literature. Controller gains for different objective functions such as ISE, IAE, ITSE and ITAE are determined using classical PID controllers and two degrees of freedom (2 DOF) PID controllers. Moreover, sensitivity analysis is carried out for both variable and constant load scenarios. The constant load is selected as 0.25 pu, and variable load is chosen from values ranging from 0.15 pu to 0.75 pu. In addition, the response of the system to parameter changes is examined by increasing or decreasing the time constants in all load cases. The system parameters are changed from −20 % to +40 % in 0.05 intervals. It has been noted that the suggested controller topologies perform differently depending on the objective functions. Thus, a significant contribution has been made to researchers to indicate the effectiveness of various control structures in fuel cell powered nanogrids. [Display omitted] • Classical and 2 DOF PID controllers are investigated in fuel cell powered nanogrids. • DMO algorithm is proposed to determine the controller gains. • Sensitivity analyses are examined for different load cases. • The performances are analysed for different objective functions. • The controllers demonstrate different performance for different objective functions. [ABSTRACT FROM AUTHOR]

In industrial process control, the proportional–integral–derivative (PID) control scheme is well‐recognized and widely utilized. However, due to the distinctive characteristics of real systems, their control design primarily aims at achieving optimal production performance, constrained by uncertainty and variations. This paper initially discusses a database‐driven PID (DD‐PID) control scheme that was previously proposed. This scheme combines the DD‐PID with the cerebellar model articulation control to minimise computational and memory requirements for industrial application. Subsequently, a hydraulic system is introduced, detailing its characteristics and control necessities. Furthermore, both the DD‐PID and the proposed cerebellar model articulation control memory‐based DD‐PID control schemes are implemented and evaluated through experimental examples on a hydraulic system. Lastly, as a practical validation of the theoretical approach, a quantitative assessment compares the two methods, discussing the practicality and efficacy of the proposed scheme in reducing computation and memory consumption. [ABSTRACT FROM AUTHOR]

Motorcycles are efficient and flexible tools for short-trip transportation, but they feature static instability and lean while cornering. This characteristic increases the danger of overturning. This study proposes a system to brake a motorcycle safely in a turn. The optimal slip ratio decision model is used to generate the optimal value according to roll angle and vertical force. Given that the roll angle cannot be measured directly, a Kalman filter is used to estimate the roll angle via kinematic parameters, measured by inertial measurement unit. The PID controller adjusts the current slip ratio to follow the optimal slip ratio. Using the motorcycle dynamics model from BikeSim, a co-simulation platform is constructed in MATLAB/Simulink to verify the reliability of the designed brake system. The results show that, compared with a traditional brake controller, the proposed brake system can control the motorcycle braking process by autoregulating the optimal slip ratio in time, according to the kinematic parameters. Both brake performance and stability are well considered, which contributes to improving the safety of the motorcycle. This research work has certain reference value for the development of motorcycle active safety systems. [ABSTRACT FROM AUTHOR]