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Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs. [ABSTRACT FROM AUTHOR]

The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro–mechanical devices that are constructed in the semblance of humans and have the ability to sense their environment and take actions accordingly. The control of humanoids is broken down to the following: sensing and perception, path planning, decision making, joint driving, stability and balance. In order to establish and develop control strategies for joint driving, stability and balance, the triple inverted pendulum is used as a benchmark. As the presence of uncertainty is inevitable in this system, the need to develop a robust controller arises. The robustness is often achieved at the expense of performance. Hence, the controller design has to be optimized based on the resultant control system's performance and the required torque. Particle Swarm Optimization (PSO) is an excellent algorithm in finding global optima, and it can be of great help in automatic tuning of the controller design. This paper presents a hybrid H∞/sliding mode controller optimized by the PSO algorithm to control the triple inverted pendulum system. The developed control system is tested by applying it to the nominal, perturbed by parameter variation, perturbed by external disturbance, and perturbed by measurement noise system. The average error in all cases is 0.053 deg and the steady controller effort range is from 0.13 to 0.621 N.m with respect to amplitude. The system's robustness is provided by the hybrid H∞/sliding mode controller and the system's performance and efficiency enhancement are provided by optimization. [ABSTRACT FROM AUTHOR]

Nonlinear control theory applied to unmanned aeronautical vehicles is an engineering topic that has received higher and higher popularity during the last decade. Model-based control approaches have shown increased performance in flight control accuracy and robustness compared to model-free proposals based on parameter adaptation and estimation. However, model-based structures need more computational efforts in terms of spatial and temporal variables. To avoid these constraints, the latest drone flight controls are based on quaternion models, ensuring more advanced computational performances. To this aim, this paper deals with a flight control algorithm of a quadrotor, in which the mathematics model of the plant is defined in terms of quaternions. Additionally, when aerial vehicles are used in specific applications such as slung load transportation and agriculture fields, among others, the variation of the mass receives high importance since it could make the entire system unstable. In the same line of ideas, this paper presents a H ∞ strategy, combined with a Super-Twisting Sliding-Mode Control, ensuring the control objective of the mass variations identification, and trajectory tracking, to be solved. The stability analysis of the proposed control approach is also discussed, and the quality and performances of the presented control strategy are tested by simulations, in an interesting case in which mass variations and external perturbations cannot be negligible. [ABSTRACT FROM AUTHOR]

To enhance frequency and active power control performance, this research proposes a decentralized robust optimal tuning approach for power grid frequency regulation support using energy storage systems (ESSs) as the primary source of grid-forming (GFM) converters. The proposed approach employs the robust Kharitonov theory to find a family of stabilizing sets of a proportional-integral (PI)-based supplementary controller, which is used in the outer control layer of the GFM control system. A family of stabilizing parameter sets is found in the presence of system uncertainties and disturbances that are common in power grid operation. Then, using a developed Bayesian optimization algorithm, an optimal set of parameters is determined among the mentioned family member sets. The proposed sophisticated combination of a robust control theorem and an optimization algorithm provides a promising solution for the robust and optimal tuning of control system parameters in ESS-based GFM converters. The efficacy of the proposed method is demonstrated via simulation and laboratory real-time experiment results for a given detailed case study. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control strategy for robot arm manipulators, utilizing an adaptive sliding mode control with stochastic gradient descent (ASMCSGD). The ASMCSGD controller significant improvements in robustness, chattering elimination, and fast, precise trajectory tracking. Its performance is systematically compared with super twisting algorithm (STA) and conventional sliding mode control (SMC) controllers, all optimized using the grey wolf optimizer (GWO). Simulation results show that the ASMCSGD controller achieves root mean squared errors (RMSE) of 0.12758 for θ 1 and 0.13387 for θ 2 . In comparison, the STA controller yields RMSE values of 0.1953 for θ 1 and 0.1953 for θ 2 , while the SMC controller results in RMSE values of 0.24505 for θ 1 and 0.29112 for θ 2 . Additionally, the ASMCSGD simplifies implementation, eliminates unwanted oscillations, and achieves superior tracking performance. These findings underscore the ASMCSGD's effectiveness in enhancing trajectory tracking and reducing chattering, making it a promising approach for robust control in practical applications of robot arm manipulators. [ABSTRACT FROM AUTHOR]

Aiming at the issues of control delay and circuit parameter mismatch in three-phase modular multilevel converters (MMCs), this paper proposes a model assisted extended state observer-based deadbeat predictive current control (MAESO-based DPCC) strategy to regulate the AC-side current and internal circulating current. The model assisted ESO (MAESO) is employed to estimate the predicted values of the d- and q-axis components of the AC-side current, the internal circulating current, and system disturbance caused by the other certain and uncertain factors (including circuit parameter changes) of MMC at the time instant k + 1, and the required control input at the time instant k + 1 is then calculated based on the deadbeat control principle. The proposed control strategy not only maintains excellent steady-state performance and fast dynamic response characteristics similar to those of the traditional deadbeat predictive current control (DPCC) strategy but also has stronger robustness in the case of circuit parameter changes. The proposed control strategy was ultimately compared with the traditional DPCC strategy via experiments, and the experimental results verify the feasibility and effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions. [ABSTRACT FROM AUTHOR]

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator. [ABSTRACT FROM AUTHOR]

In power systems, nonlinear loads cause harmonic distortion, adversely affecting sensitive equipment such as induction motors, power electronics, and variable-speed drives. This paper presents a novel control strategy that integrates with existing hierarchical control systems to mitigate voltage imbalances and harmonic disturbances in AC-islanded microgrids. The proposed method utilizes selective harmonic order filtering through multiple second-order generalized integrators (MSOGI) to extract negative, positive, and harmonic order components. The distributed generation (DG) unit control mechanism is designed to immediately correct voltage imbalances and harmonic disruptions, distributing the compensatory load evenly to rectify real and reactive power imbalances and harmonic disturbances. The microgrid's control architecture primarily includes droop controllers for real and reactive power of positive sequences, voltage and current regulation inner control loops, an additional loop for correcting imbalances and harmonics, and secondary controllers to maintain voltage magnitude and frequency at nominal levels, ensuring high-quality voltage at the point of common coupling (PCC). The effectiveness of this approach is demonstrated through simulation results on the MATLAB/Simulink platform, proving its ability to effectively mitigate voltage imbalances and harmonic issues with the total harmonic of voltage reduced to approximately THDv = 0.5% and voltage unbalance factor (VUF) within approximately 0.1%. [ABSTRACT FROM AUTHOR]

Alkyl nitrites ("poppers") are a diverse class of volatile chemical compounds with a varied legal and medical history. Though once commonly prescribed to treat angina, popper use is now almost exclusively recreational. Currently, poppers are widely available and sold legally under labels like "solvent cleaner", despite marketing suggesting they are meant to be consumed. As a result, there is little incentive for producers to implement robust quality controls to protect users. In this study, nine common popper brands were analyzed using hydrogen-1 and carbon-13 nuclear magnetic resonance spectroscopy to assess the presence of impurities. Physical labels on all nine samples indicated the contents were "pure" isobutyl nitrite, despite contradictory online marketing in several cases. Spectral results showed isobutyl nitrite was present in all popper samples. However, there was evidence that various unlabeled compounds were also present in all samples. The identity and concentration of these contaminants were not clear, but the seemingly ubiquitous presence of impurities and lack of consistency in the tested samples are concerning and may represent a threat to users' health. We hope the results of this study draw attention to the potential dangers of recreational popper use and the need to reassess how these compounds are regulated. [ABSTRACT FROM AUTHOR]

This study addresses the problem of fractional-order nonlinear containment control of heterogeneous multi-agent systems within a leader–follower framework, focusing on the impact of False Data Injection (FDI) attacks. By employing adaptive mechanisms and fuzzy logic, the suggested method enhances system resilience, ensuring reliable coordination and stability even in the presence of deceptive disturbances. To deal with these uncertainties, our controller makes use of interval type-II (IT2) fuzzy sets, and we create matrix equalities and inequalities to account for the asymmetry of Laplace matrices. Also, we use the Lyapunov functions for the stability analysis of our system. Lastly, we explain the numerical simulations for the effectiveness of our theoretical results, and these simulated examples are used to verify the effectiveness of our approach and designed model. [ABSTRACT FROM AUTHOR]

This paper proposes a second-order terminal sliding mode (2TSM) approach to the trajectory tracking of the differential drive mobile robot (DDMR). Within this cascaded control scheme, the 2TSM dynamic controller, at the innermost loop, tracks the robot's velocity quantities while a kinematic controller, at the outermost loop, regulates the robot's positions. In this manner, chattering is greatly attenuated, and finite-time convergence is guaranteed by the second-order TSM manifold, which involves higher-order derivatives of the state variables, resulting in an inherently robust as well as fast and better tracking precision. The simulation results demonstrate the merit of the proposed control methods. [ABSTRACT FROM AUTHOR]

This paper seeks to investigate how earnings quality is affected by related party transactions (RPTs). The research also examines the impact of ownership structure as a moderating variable on this relationship. Panel data with the firm fixed effects model are utilized in the paper. A sample of 91 non-financial companies listed on the Saudi Stock Exchange between 2018 and 2022 were included, resulting in 429 observations of company performance over that time period. This paper finds that there is a negative association between RPTs and earnings quality. Furthermore, the study found that the adverse effect of RPTs on earnings quality is intensified when there is managerial ownership and institutional ownership as moderating variables. The study's conclusions are robust and reliable, as the sensitivity analysis results reinforce those of the basic analysis. To the authors' knowledge, there is relatively little available evidence on the connection between RPTs and their correlation with earnings quality, particularly in the context of ownership structure acting as a moderating variable. Moreover, the study's findings hold important implications for enhancing earnings quality in developing economies. To the authors' knowledge, no studies have been conducted in Saudi Arabia thus far to investigate the impact of ownership concentration, institutional ownership, managerial ownership, foreign ownership, and state ownership on the association between RPTs and earnings quality. Therefore, this paper expands the literature by modeling how the interaction between ownership structure and related party transactions may influence earnings quality. In this way, the authors contribute to the body of knowledge by unveiling a more robust control mechanism, particularly in developing economies with ineffective markets for corporate control. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric impact of fund active management style on the performance of ESG funds. Unlike conventional measures of synchronicity, we propose new measures that capture the asymmetric patterns in a fund's management style in upside and downside market conditions. Our data includes 170 equity funds that are identified as socially responsible, with a period spanning from 2010 to 2022. Our proposed methodology allows us to capture the asymmetric patterns in the fund management styles under different market conditions while mitigating the challenge of outliers, which is crucial when assessing funds' active management activities. We find that while ESG funds promote sustainability, their active management is only beneficial during periods of market downturns. Our results are robust after controlling for different funds characteristics, for several active management proxies, and across various model specifications. This paper thus provides crucial guidelines for fund managers since it shows that their success is greatly influenced by their time-varying skills and management style in changing market conditions. Our findings incentivize ESG fund managers to pursue information acquisition activities during market downturns, as these activities improve market informational efficiency while aligning with their sustainability goals. [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]

Nowadays, the demand for high-precision devices is becoming more intense, thanks to the growing complexity and sophistication of modern technology and applications, including microscopy, nanomeasurement and analysis instruments. However, challenges arise because environmental factors such as vibration negatively affect their performance. An active vibration isolation system (AVIS) is one of the most recent solutions for that, but the high degree-of-freedom (DoF) nature results in it strongly suffering from unwanted interactions. Hence, in this paper, a robust decoupling controller is designed to handle the mentioned disadvantages. The system model is first presented, followed by the proposed decoupling techniques, and then, a feedback controller is designed by applying the mixed-sensitivity H ∞ control framework. Experimental studies are conducted to investigate the effectiveness of the proposed AVIS and compare it with other systems, namely, a passive vibration isolation system, a proportional–derivative (PD)-controlled AVIS, and a robust controlled AVIS. The robustness and decoupling performance of the proposed controller are guaranteed by suppressing external vibrations and isolating interactions, and, therefore, stabilizing the system. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: Automatic control of electrical motors and robotic systems actuated with electrical motors are the main featured applications of this study but are not the limit. Any closed-loop automatic control system with known plant dynamics can be a featured application of this study. In this study, a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, the design of an optimal neural network-based controller is performed automatically. Several common reference types are studied including step, square, sine, sawtooth, and trapezoid functions. Expected reference–disturbance pairs are used to train the system for finding optimal neural network controller parameters. A separate test set is used to test the system for unexpected reference–disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. The real DC motor's parameters are estimated using a particle swarm optimization (PSO) algorithm. Initially, a proportional–integral (PI) controller is designed using a PSO algorithm to find the simple controller's parameters optimally and automatically. Starting with the neural network equivalent of the optimal PI controller, the optimal neural network controller is designed using a PSO algorithm for training again. Simulations are conducted with estimated parameters for a diverse set of training and test patterns. The results are compared with the optimal PI controller's performance and reported in the corresponding section. Encouraging results are obtained, suggesting further research in the proposed direction. For low-disturbance scenarios, even simple controllers can have acceptable performance, but the real quality of a proposed controller should be shown under high-amplitude and difficult disturbances, which is the case in this study. The proposed controller shows higher performance, especially under high disturbances, with an 8.6% reduction in error rate on average compared with the optimal PI controller, and under high-amplitude disturbances, the performance difference is of more than 2.5 folds. [ABSTRACT FROM AUTHOR]

The article discusses the importance of research in the field of flight dynamics, control, and simulation for advancing aeronautical sciences. It emphasizes the need for a deep understanding of flight dynamics to enhance aircraft performance, safety, and efficiency. The integration of advanced control strategies and the use of simulation play a crucial role in optimizing aircraft behavior and reducing costs. The article also provides an overview of 14 papers published in a special issue of the Aerospace journal, covering various topics such as hybrid electric propulsion, aerodynamic parameter identification, and autonomous flare control. The research presented in the papers contributes to the development of new technologies and innovations in aviation. [Extracted from the article]

The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery. [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 study presents a predefined-time control strategy for rigid spacecraft, employing dynamic predictive techniques to achieve robust and precise attitude tracking within predefined time constraints. Advanced predictive algorithms are used to effectively mitigate system uncertainties and environmental disturbances. The main contributions of this work are introducing adaptive global optimization for period updates, which relaxes the original restrictive conditions; ensuring easier parameter adjustments in predefined-time control, providing a nonconservative upper bound on system stability; and developing a continuous, robust control law through terminal sliding mode control and predictive methods. Extensive simulations confirm the control scheme reduces attitude tracking errors to less than 0.01 degrees at steady state, demonstrating the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a new adaptive modification of active disturbance rejection control (ADRC) with parameter estimation based on a recursive least-squares (RLS) method. The common ADRC used in many applications relies on the simple approach, which assumes the simplification of the object into an integral chain form. However, this model-free ADRC does not guarantee the stability of a closed-loop system in the presence of noticeable modeling uncertainties, so it is compared in this paper to another approach, in which the linear part of the system is included in the ADRC framework (generalized ADRC). This incorporation of the model is examined in the paper for a wide range of model and controller parameters, considering also the presence of external disturbances as well as parameter uncertainties, pointing out the limitations of fixed-gain algorithms. Then, the adaptive modification of the model-based ADRC is proposed, which is equipped with a real-time estimation of model parameters by means of the RLS method in continuous time. The stability conditions of the proposed modification of the algorithm in the closed control loop are also analyzed. It can be concluded that, under appropriate conditions, the inclusion of information about known plant parameters into the ADRC can noticeably improve the conditions of the control system. The proposed adaptive model-based approach enables quality improvement during the control process even with initially unknown parameters, for time-varying parameters, and in the presence of parametric uncertainties and external disturbances. The tests were performed on a real plant—the task of controlling the angular velocity of the direct current (DC) motor was considered. [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]

This paper expands a recently proposed peak current-mode (PCM) control method for a power factor correction (PFC) boost converter to include the totem-pole converter and solves the controller's compatibility problem with the totem-pole converter by proposing three input current sensing methods. Using MATLAB/Simulink 2023b, simulation experiments on a 2 kW totem-pole converter utilizing the PFC PCM controller were carried out to assess the performance of the controller with the proposed sensing methods. The findings indicate that under steady-state conditions, all three proposed sensing methods performed input current shaping successfully and yielded nearly identical THD% of about 4.4% in the input current waveform. However, it is noteworthy that method 2, referred to as the memory method, exhibited a sluggish and less robust transient response in comparison to the swift and resilient responses observed with method 1 and method 3. Additionally, the third proposed method, which involves a single current sensor positioned across the input inductor, emerged as the optimal and cost-effective sensing solution. This method achieved the same desirable attributes of fast and robust control while utilizing only a single current sensor, a notable advantage over method 1, which employs two current sensors. [ABSTRACT FROM AUTHOR]

As the power unit of an aircraft, the engine's primary task is to provide the demanded thrust, making research on direct thrust control crucial. However, being a complicated multivariable system, effective multivariable direct thrust control methods are currently lacking. The main content of this paper is threefold. First, it presents a dual-loop multivariable μ-synthesis direct thrust control scheme for mixed-exhaust low-bypass turbofan engines, which is a typical rotationally symmetric machine. The scheme adjusts fuel flow for thrust control and nozzle area to control the turbine pressure ratio, ensuring thrust tracking while maintaining the engine's key parameters within safe limits. Second, a fast, accurate thrust estimation algorithm based on aerodynamic thermodynamics and component characteristics is introduced. At last, considering the model uncertainties between off-design and design points, a weight function frequency shaping μ-synthesis control design method is proposed to address internal loop coupling and external disturbance suppression. Nonlinear simulations within the flight envelope show that μ-synthesis direct thrust control achieves robust servo tracking and disturbance rejection, with a maximum steady-state thrust error of no more than 0.1%, and the key parameters are not over their safety boundaries. [ABSTRACT FROM AUTHOR]

This research investigates the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) to enhance sustainable energy generation, focusing on addressing dynamic complexities and uncertainties inherent in such systems. The novelty of this study lies in its dual approach, which integrates regressive modeling with an aero-hydro-elasto-servo-mooring coupled system with a deep data-driven network and implements a proportional-integral-derivative (PID) control mechanism to improve system stability. By employing Artificial Neural Networks (ANNs), the study circumvents the challenges of real-time closed-loop control on FOWT structures using the OpenFAST simulation tool. Data-driven models, trained on OpenFAST datasets, facilitate real-time predictive behavior analysis and decision-making. Advanced computational learning techniques, particularly ANNs, accurately replicate the dynamics of FOWT-OWC numerical models. An intelligent PID control mechanism is subsequently applied to mitigate structural vibrations, ensuring effective control. A comparative analysis with traditional barge-based FOWT systems underscores the enhanced modeling and control methodologies' effectiveness. In this sense, the experimental results demonstrate substantial reductions in the mean oscillation amplitude, with reductions from 5% to 35% observed across various scenarios. Specifically, at a wave period from 20 s and a wind speed of 5 m/s, the fore-aft displacement was reduced by 35%, exemplifying the PID control system's robustness and efficacy under diverse conditions. This study highlights the potential of ANN-driven modeling as an alternative to managing the complex non-linear dynamics of NREL 5 MW FOWT models and underscores the significant improvements in system stability through tailored PID gain scheduling across various operational scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we present a vision-based leader–follower strategy for formation control of multiple quadrotors. The leaders use a decoupled visual control scheme based on invariant features. The followers use a control scheme based only on bearing measurements, and a robust control is introduced to deal with perturbations generated by the unknown movement of the leaders. Using this formulation, we study a geometrical pattern formation that can use the distance between the leaders to scale the formation and cross constrained spaces, such as a window. A condition is defined for which a formation has rigidity properties considering the constrained field of view of the cameras, such that invariance to translation and scaling is achieved. This condition allows us to specify a desired formation where the followers do not need to share information between them. Results obtained in a dynamic simulator and real experiments show the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This paper presents a new dilated linear matrix inequality (LMI) representation to design a state feedback controller and a dynamic feedforward disturbance compensator for linear parameter-varying (LPV) systems. The improved LMIs are convex and finite-dimensional without any iterative approach. The designs are based on a new proposed equivalent bounded real lemma (BRL) by means of matrix dilation for LPV systems and uncertain linear systems under time-varying parametric uncertainties (TVPUs). This dilated BRL provides lower conservative results than existing methods in terms of robust stability. Accordingly, a dynamic disturbance compensator is designed in addition to a state feedback controller. This paper mainly focuses on the design of compensators against disturbances in addition to the design of state feedback controllers. The dynamic matrices of the compensator change with the time-varying parameters of the LPV or uncertain system during operation, assuming that the disturbances and the parameters are measurable or observable. The compensator can be designed to attenuate the disturbances/noises or to improve reference tracking. Finally, numerical and simulation outcomes are presented to prove both the effectiveness and lower conservativeness of the proposed LMIs. [ABSTRACT FROM AUTHOR]

In several applications, the accuracy and robust performance of the control method for the speed of permanent magnet synchronous motors (PMSMs) is critical. Model uncertainties, caused by inaccurate model identification, decrease the accuracy of PMSM control. To solve this problem, this paper presents a super robust control structure for the speed control of PMSMs. In the proposed method, the model uncertainties with Lipschitz condition together with disturbances are considered during the PMSM modeling, and their effects are handled using a robust state feedback control. To be more specific, the Lyapunov stability proof is performed in such a way that the model uncertainty effects are eliminated. Before that, the Lyapunov stability criteria have been selected in such a way that the Hꝏ conditions are considered and guaranteed. This issue helps to eliminate the effects of the disturbances. In addition, this paper considers another option to make the whole control structure robust against sudden load changes. To solve this problem, a fuzzy adaptive sliding mode observer (FASMO) is presented to determine the load torque and use it in the control signal generation. In this observer, the switched gain of the sliding mode observer (SMO) is adapted using a fuzzy system to eliminate the chattering phenomena and increase the estimation accuracy. In fact, the proposed method is called super robust because it resists model uncertainties, disturbances, and sudden load changes during three stages by robust state feedback control, Hꝏ criterion, and load estimator, respectively. The performance of the proposed approach is validated through a set of laboratory tests, and its superiority is shown compared to other methods. [ABSTRACT FROM AUTHOR]

Body weight support (BWS) systems are crucial in gait rehabilitation for individuals incapacitated due to injuries or medical conditions. Traditional BWS systems typically employ either static mass–rope or dynamic mass–spring–damper configurations, which can result in inadequate support stiffness, thereby leading to compromised gait training. Additionally, these systems often lack the flexibility for easy customization of stiffness, which is vital for personalized rehabilitation treatments. A novel BWS system with online variable stiffness is introduced in this study. This system incorporates a drive mechanism governed by admittance control that dynamically adjusts the stiffness by modulating the tension of a rope wrapped around a drum. An automated control algorithm is integrated to manage a smart anti-gravity dynamic suspension system, which ensures consistent and precise weight unloading adjustments throughout rehabilitation sessions. Walking experiments were performed to evaluate the displacement and load variations within the suspension ropes, thereby validating the variable-stiffness capability of the system. The findings suggest that the online variable-stiffness BWS system can reliably alter the stiffness levels and that it exhibits robust performance, significantly enhancing the effectiveness of gait rehabilitation. The newly developed BWS system represents a significant advancement in personalized gait rehabilitation, offering real-time stiffness adjustments and ongoing weight support customization. It ensures dependable control and robust operation, marking a significant step forward in tailored therapeutic interventions for gait rehabilitation. [ABSTRACT FROM AUTHOR]

Permanent magnet linear synchronous motors (PMLSMs) with stator segmented structures are widely used in the design of high-power propulsion systems. However, due to the inherent delay and segmented structure of the systems, there are parameter disturbances in the inductance and flux linkage of the motors. This makes the deadbeat predictive current control (DPCC) algorithm for a current loop less robust in the control system, leading to a decrease in control performance. Compensation methods such as compensation by observer and online estimation of parameters, are problematic to apply in practice due to the difficulty of parameter adjustment and the high complexity of the algorithm. In this paper, a robustness-improved incremental DPCC (RII-DPCC) method—which uses incremental DPCC (I-DPCC) to eliminate flux linkage parameters—is proposed. The stability of the current loop was evaluated through zero-pole analysis of the discrete transfer function. Current feedforward was introduced to improve the stability of I-DPCC. The inductance stability range of I-DPCC was increased from 0.8–1.25 times to 0–2 times the actual value, and the theoretical stability range was increased more than 4 times, effectively improving the robustness of the predictive model to flux linkage and inductance parameters. Finally, the effectiveness of the proposed method was verified through numerical simulation and experiment. [ABSTRACT FROM AUTHOR]

This paper introduces a current control method for permanent magnet synchronous motors (PMSMs) using a disturbance sliding mode observer (DSMO) in conjunction with a linear quadratic regulator (LQR). This approach enhances control performance, streamlines the tuning of controller parameters, and offers robust optimal control that is resistant to system disturbances. The LQR controller based on state feedback is advantageous for its simplicity in parameter adjustment and achieving an optimal control effect easily under specific performance indicators. It is suitable for the optimal control of strong linear systems that can be accurately modeled. However, most practical systems are difficult to model accurately, and the time-varying system parameters and existing nonlinearity limit the engineering application of LQR. In the PMSM current control loop, there is strong nonlinear disturbance manifesting as the nonlinearity of its dynamic model. Additionally, substantial noise and variations in system parameters within actual motor circuits hinder the linear quadratic regulator from attaining optimal performance. A disturbance sliding mode observer is proposed to enhance the LQR controller, enabling superior performance in nonlinear current loop control. Simulation and actual hardware experiments were conducted to verify the performance and robustness of the control scheme proposed in this paper. Compared with the widely used PI controller in engineering and sliding mode control (SMC) specialising in disturbance rejection, it offers the advantage of straightforward parameter tuning and can swiftly achieve the robust and optimal control performance that engineers prioritize. [ABSTRACT FROM AUTHOR]

Aimed at addressing the strong nonlinearity and strong external disturbances that cause flight control issues in conventional guided projectiles, as well as the slow response and structural vibrations that often occur in sliding mode control systems, which have a detrimental impact on the control effect and ultimate hit precision, a new type of fast and robust control algorithm with a unidirectional mode has been designed. The objective is to design an optimized aerodynamic shape for the projectile and to establish a dynamic model of the roll channel and a motion model of the entire trajectory. The dynamics of a new global terminal sliding mode are proposed, and an adaptive parameter term is realized by calculating the state of the critical sliding mode surface, which ensures that the tracking error converges within a finite time. Its combination with an adaptive approaching law is used to further speed up convergence while damping the structural vibration of the system. The bias error of the roll angle is constructed as the controller and simulation calculations are conducted on the basis of the aforementioned framework. The stability and time convergence of the control system are demonstrated through Lyapunov theory. The results indicate that, in comparison to the conventional terminal sliding mode controller, the designed controller exhibits a markedly rapid convergence rate and stronger robustness in tracking the command signal. Moreover, it also maintains a stable motion attitude of the projectile throughout the entire process. The superior control effect under different guidance schemes and the strong external disturbances also further reflect the anti-jamming capability and tracking performance of the system. [ABSTRACT FROM AUTHOR]

Trajectory optimization is an essential tool for the high-fidelity planning of missions in aerospace engineering in order to increase their safety. Robust optimal control methods are utilized in the present study to address environmental or system uncertainties. To improve robustness, holistic approaches for robust trajectory optimization using sensitivity minimization with system feedback and predicted feedback are presented. Thereby, controller gains to handle uncertainty influences are optimized. The proposed method is demonstrated in an application for UAV trajectories. The resulting trajectories are less prone to unknown factors, which increases mission safety. [ABSTRACT FROM AUTHOR]

This paper presents an integrated framework for aircraft engines, which consists of three phases: modeling, control, and experimental testing. The engine is formulated as an uncertain T–S fuzzy model. By a hierarchical dynamical parameter clustering, the number and premise variables of fuzzy rules are optimized, which keeps the engine's prime and representative dynamics. For each fuzzy rule, a global stability-guaranteed method is developed for the identification of the consequent uncertain dynamic model. The resulting stable T–S fuzzy model accurately approximates the actual engine dynamics in the operation space. Based on this fuzzy model, a new robust control is constructed with hierarchical compensators. The control parameters take advantage of the fuzzy blend of engine prime dynamics and uncertainty thresholds. Extensive hardware-in-loop (HIL) experimental tests in the flight envelope and a flight task cycle demonstrate the effectiveness and real-time performance of the proposed control. The settling times and overshoots of engine response are suppressed to be under 2.5 s and 10%, respectively. [ABSTRACT FROM AUTHOR]

We present a proportional–integral–derivative-based controller plus an adaptive slide surface to solve the trajectory tracking control problem for a fully actuated vessel with unknown parameters perturbed by slowly varying external unknown dynamics. The controller design assumes that the vessel moves at low speed and frequency, its physical parameters are unknown, and its state is measurable. The control approach ensures error tracking convergence toward a small vicinity at the origin. We conduct the corresponding stability analysis using the Lyapunov theory and saturation functions. We tested the controller through two numerical experiments—a turning ellipse maneuver and a rest-to-rest maneuver—where the vessel parameters were unknown, and we obtained satisfactory results. [ABSTRACT FROM AUTHOR]

In this article, a new intelligent fault-tolerant control (FTC) is designed to control a flexible-link manipulator with uncertain dead-zone and intermittent actuator faults. Initially, a smooth dead-zone inverse model using a hyperbolic tangent function is introduced to handle dead-zone nonlinearity and suppress input chattering. An adaptive law is proposed to estimate an unknown coupling item, combining the upper bounds of compensation error and floating bias faults, achieving robust adaptive control of the system. A new FTC strategy is subsequently developed to address intermittent actuator faults. Finally, the bounded convergence of system state errors is proven using direct Lyapunov methods, and the effectiveness and superiority of the proposed controller are demonstrated through numerical simulation and experiment. [ABSTRACT FROM AUTHOR]

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system's bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system's resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems. [ABSTRACT FROM AUTHOR]

In this paper, a novel robust finite-time control scheme is specifically designed for a class of under-actuated nonlinear systems. The proposed scheme integrates a reaching phase-free integral backstepping method with an integral terminal fractional-order sliding mode to ensure finite-time stability at the desired equilibria. The core of the algorithm is built around proportional-integral-based nonlinear virtual control laws that are systematically designed in a backstepping manner. A fractional-order integral terminal sliding mode is introduced in the final step of the design, enhancing the robustness of the overall system. The robust nonlinear control algorithm developed in this study guarantees zero steady-state errors at each step while also providing robustness against matched uncertain disturbances. The stability of the control scheme at each step is rigorously proven using the Lyapunov candidate function to ensure theoretical soundness. To demonstrate the practicality and benefits of the proposed control strategy, simulation results are provided for two systems: a cart–pendulum system and quadcopter UAV. These simulations illustrate the effectiveness of the proposed control scheme in real-world scenarios. Additionally, the results are compared with those from the standard literature to highlight the superior performance and appealing nature of the proposed approach for underactuated nonlinear systems. This comparison underscores the advantages of the proposed method in terms of achieving robust and stable control in complex systems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nonlinear speed control method for permanent magnet synchronous motors that enhances their robustness and tracking performance. This technique integrates a sliding-mode disturbance observer and variable-gain fractional-order super-twisting sliding-mode control within a vector-control framework. The proposed control scheme employs a sliding-mode control method to mitigate chattering and improve dynamics by implementing fractional-order theory with a variable-gain super-twisting sliding manifold design while regulating the speed of the considered motor system. The aforementioned observer is suggested to enhance the control accuracy by estimating and compensating for the lumped disturbances. The proposed methodology demonstrates its superiority over other control schemes such as traditional sliding-mode control, super-twisting sliding-mode control, and the proposed technique. MATLAB/Simulink simulations and real-time implementation validate its performance, showing its potential as a reliable and efficient control approach for the system under study in practical applications. [ABSTRACT FROM AUTHOR]

This study introduces the design of a state-feedback controller for Linear Parameter Varying (LPV) systems in scenarios where exogenous parameters are not directly accessible, and the state vector is to be estimated. Instead of considering a static feedback gain, it proposes a method for estimating these parameters and synthesizing a parameter-dependent state-feedback gain that is robust against uncertainties in parameter estimation. The state vector used by the state-feedback controller, and some quantities required by the estimation law, are both obtained by a robust filter synthesized by LMI (Linear Matrix Inequalities). This paper outlines the estimation, filtering, and control laws, detailing the conditions necessary for ensuring convergence and stability. A numerical experiment and a 2 DoF torsional system application show the enhanced dynamic performance of the method when applied to uncertain dynamic systems. The findings highlight the effectiveness of the proposed approach in maintaining system stability and improving performance despite the inherent uncertainties in parameter estimation, offering a significant contribution to the field of robust control for LPV systems. [ABSTRACT FROM AUTHOR]

This paper presents a contraction-based learning control architecture that allows for using model learning tools to learn matched model uncertainties while guaranteeing trajectory tracking performance during the learning transients. The architecture relies on a disturbance estimator to estimate the pointwise value of the uncertainty, i.e., the discrepancy between a nominal model and the true dynamics, with pre-computable estimation error bounds, and a robust Riemannian energy condition for computing the control signal. Under certain conditions, the controller guarantees exponential trajectory convergence during the learning transients, while learning can improve robustness and facilitate better trajectory planning. Simulation results validate the efficacy of the proposed control architecture. [ABSTRACT FROM AUTHOR]

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H ∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H ∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H ∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios. [ABSTRACT FROM AUTHOR]

Beam pumping units play a key role in oil extraction. There is an increasing demand for optimal oil-extracting performance as operational environments are becoming more challenging and complex. Therefore, it is vital to create an acceptable mathematical model and develop robust control mechanisms. In the past decades, researchers have achieved fruitful results regarding modeling and control methods in this field. This article provides a comprehensive review of the research on modeling and control methods for beam pumping units, including mathematical models based on differential equations and boundary conditions, as well as the control strategies designed. Finally, future perspectives and recommendations are presented. [ABSTRACT FROM AUTHOR]

In view of the problem of the low-speed jitter of household lawn mowers driven by a permanent magnet synchronous motor (PMSM) at low speeds and high torque, and the complicated parameters of traditional non-linear active disturbance rejection controllers, a partially optimized linear active disturbance rejection control (LADRC) driving PMSM strategy is proposed. First, the linear extended state observer (LESO), which bears a significant burden in terms of speed and load estimation in active disturbance rejection control, is optimized by reducing its order to improve the anti-disturbance performance of the active disturbance rejection controller within a limited bandwidth. Secondly, the reduced-order parallel linear extended state observer (RPLESO) is obtained by optimizing the parallel structure of the order-reduced LESO, which improves the control precision and robustness of the system. Through a simulation and experimental verification, the optimized LADRC control of the PMSM system is shown to improve the parameter adjustability, speed estimation precision and system robustness. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) is one of the most important tools in power system control. LFC is an auxiliary service related to the short-term balance of energy and frequency of power systems. As such, it allows the acquisition of a central role in enabling electricity exchanges and providing better conditions. The classification of LFC can be carried out from different angles: we can enumerate, among others, the type of control used. The following types of control are presented in this review: classical, optimal, and robust control. More advanced controls can also be used for classification: fuzzy logic control, ANN control, genetic algorithms, PSO control, etc. The influence of renewables and power control tools like FACTS is also considered as a category to be analyzed. The last classifications are related to two important subjects—the influence of DC links on LFC efficiency and the dangers of cyberattacks on the LFC. [ABSTRACT FROM AUTHOR]

This paper introduces a robust fault-tolerant control (FTC) for a five-phase permanent magnet synchronous motor (FPPMSM) affected by the third harmonic under an open-circuit fault (OCF). Using field-oriented control, the proposed method demonstrates how to achieve optimal current references for torque decoupling under healthy and faulty conditions. The proposed speed and current loop controllers are based on sliding mode control (SMC), with a nonlinear extended state observer (NESO) that utilizes a hyperbolic tangent function (HTF) to provide feed-forward compensation to the controllers. The results analysis confirmed that the proposed control could enhance the tracking accuracy and robustness to disturbances under various conditions, substantially reducing torque ripples and speed fluctuations under a fault. [ABSTRACT FROM AUTHOR]

The paper introduces a robust adaptive fault-tolerant control system for the six-degree-of-freedom (six-DOF) dynamics of quadrotor unmanned aerial vehicles (UAVs), incorporating disturbances and abrupt actuator faults to represent real-world conditions. The proposed control scheme employs robust control terms to manage unknown disturbances. However, robust control performance may degrade due to sudden fault impacts. To handle this issue, we introduce adaptive laws to ensure continuous adaptation. The control architecture ensures the tracking system's stability by combining robust control using sliding-mode control (SMC) with adaptive control developed using the certainty equivalence principle. The sliding-surface error limits the adaptive laws, in which the convergence of estimated parameters to the actual unknown variables is not required as they fully rely on the convergence of the tracking error. We provide rigorous mathematics to validate the proposed control design. Furthermore, we conduct numerical simulations for a quadrotor UAV to showcase the effectiveness of the proposed scheme. The results demonstrate the efficacy of the proposed design in handling external disturbances and abrupt actuator faults. [ABSTRACT FROM AUTHOR]

As a flexible, autonomous and intelligent motion platform, underactuated surface vessels (USVs) are expected to be an ideal means of transport in dangerous and complex marine environments. The success and efficiency of maritime missions performed by USVs depend on their ability to accurately follow paths and remain robust against wind and wave disturbances. To this end, this paper focuses on accurate and robust path following control for USVs under wave disturbances. Model predictive control with a quasi-infinite horizon is proposed which converts the objective function from an infinite horizon to an approximate finite horizon, providing the convergence performance in long prediction horizons and reducing the computation load explicitly. To enhance robustness against disturbances, a fixed-time disturbance observer is applied to estimate the time-varying and bounded disturbances. The estimated value is provided to the controller input to form a robust control framework with disturbance feedforward compensation and predictive control feedback correction, which is substantially different from existing works. The convergence and optimality of the proposed algorithm are presented mathematically. Finally, we demonstrate the advantages of the algorithm in both theory and simulation. [ABSTRACT FROM AUTHOR]

Unmanned underwater vehicles (UUVs) possess impressive maneuverability and versatility, but controlling them during trajectory tracking can be challenging due to their susceptibility to external disturbances and perturbations in their model parameters. Additionally, the UUV has four degrees of freedom underwater, but only three control inputs, making it a typical underactuated system. To address these issues, this paper introduces a novel optimize sliding mode control (OPSMC) algorithm grounded in projected perpendicular guidance (PPG). The PPG algorithm transforms the three-degree-of-freedom path trajectory control into two-degree-of-freedom heading tracking control and surge velocity tracking control by designing virtual posture angles. Optimized sliding mode control, based on sliding mode control, improves control precision and reduces control input chattering by constructing optimization functions for control inputs. During trajectory tracking, UUVs are susceptible to external environmental disturbances and perturbations in system model parameters. Disturbance observers are employed to estimate these disturbances and perturbations. Finally, MATLAB/Simulink is used for numerical simulation experiments. The simulation results demonstrate that the PPG algorithm effectively enables underactuated UUVs to achieve trajectory tracking control. The designed optimized sliding mode controller and disturbance observer enhance the control precision and robustness of the system. [ABSTRACT FROM AUTHOR]