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The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40–90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging. A diffusion MRI study suggests that redundancy could mitigate the effects of age-associated changes in brain topology to support the average controllability of brain networks middle and old age. [ABSTRACT FROM AUTHOR]

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing. [ABSTRACT FROM AUTHOR]

The steer-by-wire (SbW) system is a promising system in the realm of automotive engineering. It substitutes the mechanical connection between the steering wheel and the front road wheels with an electronic signal-based functional connection. The SbW system offers several advantages over conventional steering systems, including weight reduction, reduced vibration, and enhanced steering functionality configuration. However, the absence of a mechanical linkage in the SbW system gives rise to certain challenges. The SbW system requires endowing adequate steering feel such as damping and reaction force using feedback motor, and the road wheel needs robust control of pinion motor for normal load variation by passengers and self-aligning torque as external disturbance. The SbW system is composed of the steering feedback module (SFM) and the road wheel module (RWM). This paper proposes a control approach to generate steering feel for SFM, in which steering feel is generated using an admittance model based on velocity control. A disturbance observer is applied to ensure robustness of velocity control. The steering wheel torque versus steering wheel angle (T–A) curve is used to analyze steering feel characteristic and evaluate steering feel. The proposed steering system is validated through experiments that confirm its ability to provide satisfactory steering feel for vehicles. This work may offer a novel solution for the design of advanced steering systems in the field for the future mobility such as an autonomous driving. [ABSTRACT FROM AUTHOR]

This paper compares various angle-tracking algorithms to balance the performance and noise for a steering-by-wire (SBW) system. Direct and quiet steering experiences can improve drivers' acceptance of the SBW system. Linear quadratic regulator (LQR) control, robust control, and conventional cascade proportional–integral (PI) control have been developed and compared both theoretically and experimentally. To avoid the risky and time-consuming parameter-tuning process, a high-fidelity steering resistance model, which comprises a linear two-degree-of-freedom vehicle model and a dynamic LuGre friction model is established. Step and sine wave tests are simulated in a Matlab/Simulink environment to determine the reasonable parameter region for various methods. Then, the three types of algorithms are implemented on a prototype SBW vehicle and compared under the same scenarios. Finally, the simulated and experimental results are illustrated in detail. According to the indicators of control bandwidths, steady-state errors, cockpit sounds, and current waveforms, it is clear that LQR and robust control can achieve faster response and more acceptable noise, with uncertain and relatively larger tracking errors. Cascade PI control, in comparison, can realize smaller steady-state errors and gentler current waveforms, with slight noise and slower response. [ABSTRACT FROM AUTHOR]

This article aims at designing a robust wide-area damping controller (WADC) to address the problems associated with communication failures. The proposed controller is a state feedback-based controller, which uses generators' states as the controller inputs. The structure of the proposed controller's feedback gain matrix is a combination of two feedback gain matrices to achieve adequate robust performance over different disturbances and communication losses. These two feedback gain matrices are; full-scale feedback gain matrix and decentralized scale feedback gain matrix. Both models are designed by considering all generators' states are available at the controller input side. However, both matrices' structures are different from each other. The conventional LQR state feedback control technique is used to design the full-scale matrix. On the other hand, the decentralized scale matrix is computed using a structurally constrained H 2 -norm optimization technique to acquire the required structure. The full-scale model gives robust performance under different faults. Whereas, the decentralized scale matrix will result robust performance against communication failures. Therefore, to achieve superior performance under all conditions, the proposed controller can be used. To show the efficacy of the proposed controller, it is compared with different existing controllers and the results are illustrated. Simulation studies are conducted on well-known IEEE 39-bus and 68-bus test systems to validate the robustness of the proposed controller considering different faults and communication failures. [ABSTRACT FROM AUTHOR]

During the last two decades, parallel robots have become more ubiquitous, employed in a great variety of sectors, from food to aerospace industries. In fact, they are much more efficient than their serial counterparts in terms of performing fast motions and consuming less energy. However, due to their mechanical complexity, they present a highly complex non-linear dynamics, which makes the modelling and control tasks difficult. Aiming to improve the performance and robustness of the control laws already used to control this type of mechanisms, previously, the authors proposed two novel laws of hybrid control, implemented in the joint space, in order to improve the dynamic behavior of parallel robots when performing fast motion tasks. Among the goals of the current work, one can mention to adapt the two laws of hybrid control, proposed in the previous work, by implementing them in the task space. Additionally, the peculiarities related to the dynamic formulation and the tuning of controller gains are also shown. Furthermore, a comparison of the performances of the pure and hybrid control techniques, implemented in both joint and task spaces, is presented as well, by executing the same paths and using adequate metrics. In the selected paths, experiments revealed that the hybridization process of pure control laws in the task space provides a significant reduction of the path-tracking and steady-state errors. [ABSTRACT FROM AUTHOR]

This paper proposes a novel, robust flight control system using adaptive nonlinear sliding mode control for a quadrotor UAV in the presence of parametric uncertainties and aerodynamical disturbances. The proposed control system is based on a proportional derivative sliding surface combined with a non-singular fast terminal sliding mode control to enhance the tracking accuracy and reduce the chattering influence. Moreover, an adaptive mechanism is proposed to approximate the unknown upper limit of external disturbances/uncertainties. The Lyapunov criteria is used to prove the closed-loop stability and calculate the adaptive mechanism. The proposed adaptive PD-NFTSMC (APD-NFTSMC) can cope with the negative influence of modeling uncertainties, external disturbances, and measurement noise, allows null error in the steady state, and solves the chattering problem. Moreover, intensive simulation experiments under various external conditions are carried out to highlight the sovereignty of the developed control approach. Finally, comparisons with some well-known control techniques are performed to show the usefulness, smoothness, and robustness of the proposed APD-NFTSMC strategy. [ABSTRACT FROM AUTHOR]

Due to its attractive features of bidirectional power flow, zero-voltage switching (ZVS), high power density, and electric isolation, the current source-based isolated dual-active-bridge (IDAB) dc–dc converter is widely employed in various renewable energy applications. Efficacy and sublime dynamic performance are two primary control objectives for IDAB-dc–dc converter. Recently a single-phase shift-based fast current controller (SPS-FCC) was proposed to enhance the dynamic performance of the IDAB-dc–dc converter. However, this control technique suffers from high current stresses when the voltage ratio between two bridges deviates from 1, which can affect the overall efficiency of the converter. A typical extended-phase shift (EPS) control scheme can substantially improve the converter efficiency by minimizing these current stresses, but it cannot meet the robust dynamic response requirements. To overcome the drawbacks of SPS-FCC and EPS, this study proposes a novel EPS-based fast current controller for current source-based IDAB-dc–dc converters (EPS-FCC). The proposed control strategy combines the high efficiency of EPS and the robust dynamic performance of FCC. Thus, the IDAB-dc–dc converter implemented with the proposed control technique has the advantages of excellent dynamic response and high efficiency under light load conditions. Moreover, the proposed controller is cost-effective compared to SPS-FCC as for the same transmission power, the electric equipment with low current ratings can be selected. To confirm the effectiveness of the proposed control scheme, a 300 W IDAB-dc–dc converter prototype is developed. Different test cases are designed, and the proposed control technique is compared in detail with SPS-FCC and EPS control schemes under these test cases. The experimental results have validated the effectiveness of the proposed control technique. [ABSTRACT FROM AUTHOR]

The coupled systems with numerous interdependent variables pose considerable control and stability issues in commercial chemical processes. These systems exhibit complex interactions, time delays, parameter uncertainties and disturbances, which can lead to poor performance and reduced productivity. Conventional single-loop controllers struggle to effectively manage the interactions between variables, leading to poor disturbance rejection and tracking accuracy. A more robust and efficient control strategy is essential to tackle these limitations. Hence, a robust decentralized PID controller design is proposed to address the shortcomings of coupled industrial chemical processes. The aim is to enhance control system performance by achieving better disturbance rejection and tracking accuracy, thereby improving process efficiency and product quality. The complementary sensitivity function is exploited to attain this, and a novel graphical tuning method is proposed for obtaining optimal PID controller parameters. The use of the H ∞ robust criterion ensures robust stability and minimizes the influence of external disturbances. The proposed approach offers an efficient means to design robust decentralized controllers for complex chemical processes. The simulation results demonstrate the effectiveness of the method compared to existing controllers. Numerically, the proposed controller is able to attain almost a 20 % reduction in integral absolute error and a 30 % decrease in the settling time for all cases compared to existing controllers. The graphical tuning method enables efficient parameter optimization, resulting in faster response times and reduced settling time. This work represents a significant step toward achieving enhanced control system performance and stability in coupled industrial chemical processes. [ABSTRACT FROM AUTHOR]

A vehicle system motion decoupling control method was proposed to address challenges in controlling articulated heavy vehicles (AHVs). The method, based on differential geometry theory, focused on distributed electric drive AHVs. Its objective was to separate the highly nonlinear and strongly coupled dynamics system into two relatively independent subsystems: longitudinal and lateral motions. Additionally, a robust controller was designed to improve the vehicle's resistance to external disturbances like side winds. Simulation tests using a TruckSim model of a distributed electric drive AHV show significant improvements compared to vehicles without decoupling control. The rearward amplification (RA) is reduced by 4.5%, the longitudinal velocity deviation by 67.5%, and the yaw rate deviation by 69.7%. The vehicle also demonstrates enhanced stability when subjected to strong breeze disturbances. To validate the control performance in real-time systems, the hardware-in-the-loop tests were conducted, which confirms the effectiveness of the proposed control approach in practical applications. [ABSTRACT FROM AUTHOR]

The paper presents a new internal model control (IMC) based control technique for lateral trajectory tracking of autonomous vehicles. The controller's proposed structure employs a robust, fault-tolerant nonlinear internal servo control with optimal reference generation concerning vehicle yaw stability and physical limitations. The presented inscription of the reference generation creates a convex optimization task that can be used in real-time applications. Improvements in yaw-rate stability of vehicle motion control are first shown through simulation results performed in a Simulink environment. The controller structure was also implemented in a real-time model and was examined in a Mercedes C-Class vehicle. In this article, the simulation results and the real-time measurements are presented. The results show that the proposed controller has high efficiency in disturbance rejection and lower sensitivity towards parameter changes compared to a model predictive control (MPC) structure. [ABSTRACT FROM AUTHOR]

This paper investigates the non-fragile robust H ∞ control problem of nonlinear uncertain neutral stochastic Takagi–Sugeno (T–S) fuzzy systems with mixed time-delays. The uncertainties are norm-bounded and time-varying. Sufficient conditions for H ∞ performance analysis results of the delay-dependent condition is established via the Lyapunov–Krasovskii functional (LKF) technique and linear matrix inequality (LMI) method. Based on the H ∞ performance result, a desired fuzzy control is designed by solving LMIs. Finally, an example is provided to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

To achieve high precision and robust control of switched reluctance machines (SRMs), this paper proposes a novel rotor position estimation method based on gain-optimized sliding mode observer (SMO) with neural networks and whale optimization algorithm. For the SMO, this paper uses the easily measured phase current to constitute the error function of the SMO, which requires less pre-stored data. Using the neural networks' powerful nonlinear mapping capability, the relationship between the sliding mode gains and speed estimation error and the position estimation error is obtained, and then the whale optimization algorithm is used to find the optimal sliding mode gains in the restricted range. The simulation and experiment results show that the accuracy of the SMO control system is significantly improved. [ABSTRACT FROM AUTHOR]

Malicious attacks are often inevitable in cyber-physical systems (CPS). Accuracy in Cyber physical system for position tracking of servos is the major concern now a days. In high precision industrial automation, it is very hard to achieve accuracy in tracking especially under malicious cyber-attacks, control saturations, parametric perturbations and external disturbances. In this paper, we have designed a novel predefined time (PDT) convergence sliding mode adaptive controller (PTCSMAC) for such kind of cyber physical control system. Main key feature of our control is to cope these challenges that are posed by CPS systems such as parameter perturbation, control saturation, and cyber-attacks and the whole system then upgrade to a third-order system to facilitate adaptive control law. Then, we present an adaptive controller based on the novel PDT convergent sliding mode surface (SMS) combined with a modified weight updated Extreme Learning Machine (ELM) which is used to approximate the uncertain part of the system. Another significant advantage of our proposed control approach is that it does not require detailed model information, guaranteeing robust performance even when the system model is uncertain. Additionally, our proposed PTCSMAC controller is nonsingular regardless of initial conditions, and is capable of eradicating the possibility of singularity problems, which are frequently a concern in numerous CPS control systems. Finally, we have verified our designed PTCSMAC control law through rigorous simulations on CPS seeker servo positioning system and compared the robustness and performance of different existing techniques. [ABSTRACT FROM AUTHOR]

Many researchers have shown interest in profile monitoring; however, most of the applications in this field of research are developed under the assumption of normal response variable. Little attention has been given to profile monitoring with non-normal response variables, known as general linear models which consists of two main categories (i.e., logistic and Poisson profiles). This paper aims to monitor Poisson profile monitoring problem in Phase II and develops a new robust control chart using support vector regression by incorporating some novel input features and evolutionary training algorithm. The new method is quicker in detecting out-of-control signals as compared to conventional statistical methods. Moreover, the performance of the proposed scheme is further investigated for Poisson profiles with both fixed and random explanatory variables as well as non-parametric profiles. The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. A diagnostic method with machine learning approach is also used to identify the parameters of change in the profile. It is shown that the proposed profile diagnosis approach is able to reach acceptable results in comparison with other competitors. A real-life example in monitoring Poisson profiles is also provided to illustrate the implementation of the proposed charting scheme. [ABSTRACT FROM AUTHOR]

High-fidelity and robust quantum control is essential for large-scale quantum information processing. The stimulated Raman transition that utilizes second-order coupling effect is a valuable and conventional technique for manipulating states in multi-level quantum systems, but its accuracy is limited by the driving-induced Stark shift. Here, we propose a new parameter-modulated method to effectively compensate the Stark-shift effect, so that we are able to realize high-fidelity and robust stimulated Raman transition with optimal control. Additionally, its robustness against different systematic errors can be further improved via optimization its average fidelity under these specific errors. Besides, our method has potential applications for high-fidelity and robust quantum control in high-order coupling scenarios. [ABSTRACT FROM AUTHOR]

The existence of load disturbance can lead to the instability of permanent magnet synchronous motor (PMSM) speed control systems. Therefore, a non-singular fast terminal sliding mode control (NFTSMC) strategy based on a disturbance observer is proposed in this paper. First, a new exponential reaching law is presented to relate the rate of convergence to the state error and switching function, which achieves an improvement of the system convergence speed while reducing system jitter. At the same time, to eliminate system jitter, a new smooth saturation function is introduced in place of the symbolic function. Second, a non-singular fast terminal (NFT) sliding surface is generated by combining a nonlinear function with a linear function, which makes the state error converge rapidly at different stages. Finally, a disturbance observer is built to boost the robustness of the control system by observing the outside disturbance value and feeding it back to the speed controller. The dependability and effectiveness of the proposed control strategy are confirmed by simulation experiments with MATLAB. [ABSTRACT FROM AUTHOR]

This paper deals with the issue of finite-time observer-based H ∞ control design of networked switched systems (NSS) subject to external disturbances via an event-triggered observation strategy. This triggering strategy is used to decide when the output measurements are sent to the observer over the communication network. By incorporating digital networks, the closed-loop system is modeled as a switched delayed system due to network-induced delays. By using the average dwell time (ADT) switching method with the Lyapunov–Krasovskii functional, several sufficient linear matrix inequalities (LMIs) conditions for the observer-based control synthesis are developed to guarantee the finite-time boundedness and the disturbance attenuation control performance of the resulting switched delayed system. Finally, an example is given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This study presents a new framework for obtaining personalized optimal treatment strategies targeting aberrant signaling pathways in esophageal cancer, such as the epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) signaling pathways. A new pharmacokinetic model is developed taking into account specific heterogeneities of these signaling mechanisms. The optimal therapies are designed to be obtained using a three step process. First, a finite-dimensional constrained optimization problem is solved to obtain the parameters of the pharmacokinetic model, using discrete patient data measurements. Next, a sensitivity analysis is carried out to determine which of the parameters are sensitive to the evolution of the variants of EGF receptors and VEGF receptors. Finally, a second optimal control problem is solved based on the sensitivity analysis results, using a modified pharmacokinetic model that incorporates two representative drugs Trastuzumab and Bevacizumab, targeting EGF and VEGF, respectively. Numerical results with the combination of the two drugs demonstrate the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

The stability and dynamic performance of the system are impacted by the uncertainties present in the converter model and controller parameters. This paper introduces a robust non-fragile control approach that addresses the influence of these uncertainties. The approach involves developing a control law that achieves quadratic stabilization of the closed-loop system while also satisfying the H∞ norm constraint for disturbance attenuation. Based on this concept, a robust non-fragile PI controller is designed explicitly for voltage mode control of a dc–dc buck converter. The effectiveness of the proposed control technique is evaluated from two perspectives: (i) its ability to withstand significant perturbations in plant parameters and load disturbances and (ii) its resilience in the presence of variations in controller parameters. The effectiveness of the proposed control scheme is validated using simulation and experimental results. [ABSTRACT FROM AUTHOR]

Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controller parameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf Optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at https://youtu.be/aJMq8ROW51g. [ABSTRACT FROM AUTHOR]

The Vedde Ash, originating from the Katla central volcano, Iceland, and taken to be dispersed across the North Atlantic and Europe at ~ 12 ka BP, is widely used as a geochronological marker. However, distal tephra layers with compositions like the Vedde Ash but of younger ages question the reliability of Vedde-like tephra layers as robust age control. Vedde-like tephra layers are rare in Icelandic sedimentary sequences and, where present, lack firm age control. Providing well-constrained local records of Early Holocene Katla layers is therefore critical to assess uncertainties related to the use of the Vedde Ash. Here we report three visible and stratigraphically separated Early Holocene Katla tephra layers from Torfdalsvatn, a lake in north Iceland, each with chemistry similar to the Vedde Ash. Using high-resolution 14C chronologies, we provide ages (± 1σ) for these tephra layers of 11,315 ± 180, 11,295 ± 195, and 11,170 ± 195 cal a BP. These observations reinforce that multiple explosive eruptions of Katla occurred over a 1000-year interval in the Early Holocene and challenge the precision of some paleoclimate records using the Vedde Ash as a geochronometer where age control is equivocal. This may lead to a re-evaluation of age models for some Early Holocene North Atlantic records. [ABSTRACT FROM AUTHOR]

Consumption of probiotics, which are beneficial live microorganisms, has received a lot of attention because of their potential to improve health and wellness. Robust quality control measures are necessary to ensure the safety of probiotics and maximize their health effects. This review delves into the topic of quality management in probiotics, highlighting the significance of sticking to strict guidelines from manufacture to storage to distribution. Probiotic quality standards, Good Manufacturing Practices (GMP) implementation, quality control and testing techniques, and documentation and traceability systems are all discussed in detail. The importance of taking precautions to avoid microbial contamination, meeting all applicable regulations, and clearly marking and packaging probiotic products is also emphasized. In addition, it reviews the clinical evidence supporting the possible health advantages of probiotics and investigates the processes through which probiotics enhance health. The review continues by stressing the significance of educating and informing consumers about probiotics and their proper use in order to maximize health benefits. Probiotics' potential health benefits can be maximized and consumer faith in these helpful microbes can be bolstered by adopting thorough quality management measures to ensure their safety, efficacy, and consistency. [ABSTRACT FROM AUTHOR]

In the complex ocean environment, the autonomous underwater vehicle (AUV) is subject to the multiple uncertainties including deterministic uncertainties and stochastic uncertainties, which greatly degrade the control performance. To solve this problem, a novel robust control strategy using combined uncertainty and disturbance estimator (UDE) and unscented Kalman filter (UKF) is proposed for the path-following control (PFC) of the underactuated AUV under multiple uncertainties in this paper. First, the UDE technique is adopted to handle the deterministic uncertainties, including deterministic components of environmental disturbances, parameter uncertainties, and unmodeled dynamics, etc. Second, the uncertainty of unactuated lateral channel, which cannot be dealt with by the UDE, is treated as an unknown parameter. Also, to tackle the stochastic uncertainties, which is often ignored in previous studies, including stochastic components of environmental disturbances, measurement noise, and the estimation errors of UDE which are treated as the process noise, the augmented UKF technique is adopted to jointly estimate the system states and the lateral channel unknown parameter. Finally, extensive numerical simulations and comparative analyses are presented to verify the efficiency and robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents an end-to-end framework for robust structure/control optimization of an industrial benchmark. When dealing with space structures, a reduction of the spacecraft mass is paramount to minimize the mission cost and maximize the propellant availability. However, a lighter design comes with a bigger structural flexibility and the resulting impact on control performance. Two optimization architectures (distributed and monolithic) are proposed in order to face this issue. In particular the Linear Fractional Transformation (LFT) framework is exploited to formally set the two optimization problems by including parametric uncertainties. Large sets of uncertainties have to be indeed taken into account in spacecraft control design due to the impossibility to completely validate structural models in micro-gravity conditions with on-ground experiments and to the evolution of spacecraft dynamics during the mission (structure degradation and fuel consumption). In particular the Two-Input Two-Output Port (TITOP) multi-body approach is used to build the flexible dynamics in a minimal LFT form. The two proposed optimization algorithms are detailed and their performance are compared on an ESA future exploration mission, the ENVISION benchmark. With both approaches, an important reduction of the mass is obtained by coping with the mission’s control performance/stability requirements and a large set of uncertainties. [ABSTRACT FROM AUTHOR]

To improve the suspension performance and disturbance rejection ability of a bearingless induction motor (BL-IM), a backstepping control (BC) based on the linear extended state observer (LESO) strategy is proposed. Firstly, the vector control method is combined with two backstepping controllers introduced in the control system. Then, the LESO is applied to estimate the total disturbances composed of uncertainties and unknown external interference in the system, which simplifies the bandwidth parameters according to the pole-placement method. The BC law is continuously updated to reduce the impact of the total disturbances. Additionally, the stability and convergence of the BL-IM are proven based on the Lyapunov theory. Finally, the proposed strategy is compared with the PID control as well as robust backstepping control (RBC) through simulation analysis and experimental verification. The results indicate that the proposed backstepping control method in this paper can both achieve good disturbance rejection performance and fast speed response, and well levitation ability of the BL-IM. [ABSTRACT FROM AUTHOR]

This paper concludes a robust optimal tracking control law for a class of nonlinear systems. A characteristic of this paper is that the designed controller can guarantee both robustness and optimality under nonlinearity and mismatched disturbances. Optimal controllers for nonlinear systems are difficult to obtain, hence a reinforcement learning method is adopted with two neural networks (NNs) approximating the cost function and optimal controller, respectively. We designed weight update laws for critic NN and actor NN based on gradient descent and stability, respectively. In addition, matched and mismatched disturbances are estimated by fixed-time disturbance observers and an artful transformation based on backstepping method is employed to convert the system into a filtered error nonlinear system. Through a rigorous analysis using the Lyapunov method, we demonstrate states and estimation errors remain uniformly ultimately bounded. Finally, the effectiveness of the proposed method is verified through two illustrative examples. [ABSTRACT FROM AUTHOR]

We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations: adding a tiny amount of carefully crafted noises into the original legitimate examples will cause the classifiers to make incorrect predictions at a notably high confidence level. Through the lens of activation maps, we find that some important underlying physical principles and symmetries remain to be adequately captured for classifiers with even near-perfect performance. This explains why adversarial perturbations exist for fooling these classifiers. In addition, we find that, after adversarial training the classifiers will become more consistent with physical laws and consequently more robust to certain kinds of adversarial perturbations. Our results provide valuable guidance for both theoretical and experimental future studies on applying machine learning techniques to condensed matter physics. [ABSTRACT FROM AUTHOR]

The Takagi–Sugeno (T–S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T–S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T–S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T–S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method. [ABSTRACT FROM AUTHOR]

High-fidelity preparation of quantum states in an interacting many-body system is often hindered by the lack of knowledge of such states and by limited decoherence times. Here, we study a quantum optimal control (QOC) approach for fast generation of quantum ground states in a finite-sized Jaynes-Cummings lattice with unit filling. Our result shows that the QOC approach can generate quantum many-body states with high fidelity when the evolution time is above a threshold time, and it can significantly outperform the adiabatic approach. We study the dependence of the threshold time on the parameter constraints and the connection of the threshold time with the quantum speed limit. We also show that the QOC approach can be robust against control errors. Our result can lead to advances in the application of the QOC to many-body state preparation. [ABSTRACT FROM AUTHOR]

Since annotating fine-grained labels requires special expertise, label annotations often lack quality for many real-world fine-grained image classifications (FGIC). Due to the effectiveness of noisy labels, training deep fine-grained models directly tends to have an inferior recognition ability. To address this problem in FGIC, a robust classification approach combining "active–passive–loss (APL)" framework and multi-branch attention learning is proposed. First, in order to learn discriminative regions for classification effectively, the multi-branch attention learning framework that consists of raw, object, and part branch is introduced. These three branches are connected by attention mechanism, which enables the network to learn fine-grained features of different parts from different scales including raw, object and part levels. Second, treating noisy labels as anomalies, the novel loss framework APL that can guarantee robustness and sufficient learning is adopted to achieve robust predictions in each branch. Third, in determining the final predictions, the outputs from global and object branches are combined to achieve higher classification performance. Several experiments on fine-grained image datasets show that the proposed approach is noise-robust and can achieve excellent classification performance in the presence of noisy labels in FGIC. [ABSTRACT FROM AUTHOR]

Proportional reactive power sharing among the distributed generators (DGs) is still a challenge in an islanded microgrid. The disparity in output voltage of each DG is the main cause of the error in reactive power sharing in DGs. The output voltage difference of DGs is caused by unequal line impedances and connected local loads. In order to reduce the error in reactive power sharing among the DGs, a Robust Volt-VAr control scheme (RVVCS) with an adaptive nominal voltage is proposed in this study. Since the suggested strategy is entirely decentralized, it does not need knowledge of feeder impedance or network configurations. Also, it does not require any communication link or optimization technique. The technique proposed is tested on several network configurations, including various loads, radial networks, reconfigured networks, and mesh networks, and is found satisfactory in each case. In one of the many cases presented in this study, with n q = 3 e - 4 V/VAr, β = 0.2, β add = 0.002 and local as well as common load is connected, the suggested RVVCS minimises the error in reactive power output of D G 1 , D G 2 and D G 3 to (42.68%, - 8.92%, - 33.82%), respectively, compared to the error obtained in RDCS (72.21 %, - 15.61%, - 56.6%), SADCS (72.6%, - 16%, - 56.6%) and CDCS (243.74%, - 69.87%, - 173.87%). A similar reduction pattern in reactive power output is also observed in all the case studies performed in this research. The stability of the proposed strategy is investigated in terms of eigenvalue analysis. The practicability of the proposed technique is validated in time-domain simulation in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

This paper exploits the design of a decentralized proportional integral derivative (PID) controller based on nonlinear optimization for a continuously stirred tank reactor system. The basic objective is to attain the design specifications by maintaining both the temperature and concentration. The continuously stirred tank reactor is modeled to a first-order plus dead time system by designing a decoupler. The proposed PID controller is designed on the basis of fundamentals of nonlinear optimization. Further, the overshoot is bounded with constraints on the maximum closed-loop amplitude ratio. The control algorithm is designed for decoupled systems to reduce the loop interactions and attain the servo response. The robust stability is analyzed by considering multiplicative input as well as output uncertainties, while stability is verified with the Kharitonov–Hurwitz theorem. A concise comparison is made between the proposed technique with existing methods. It is envisaged that the proposed control algorithm exhibits better servo and regulatory responses compared to the existing techniques. Furthermore, the efficacious nature of the proposed control scheme is validated by considering a wide range of closed-loop amplitude ratios. [ABSTRACT FROM AUTHOR]

Recently, synchronous reluctance motors have had a resurgence of benefit for many applications in the industry because of their simple framework, high effectiveness, low manufacturing cost, and high robustness. This research presents two inverter neutral-point-clamped topologies to improve the vector-controlled SynRM; these topologies offer interesting characteristics compared to the conventional structure-based two-level inverter. To get an inverter smooth switch command sequence, pulse width modulation has been modified to be adapted to the new topologies. The goal is to produce an output voltage vector which approaches the reference voltage vector as closely as possible. As a result, and contrary to the two-level inverter, that can only supply eight voltage vectors, the seven-level inverter can produce one 127 voltage vectors, and the nine-level inverter can produce 255 voltage vectors. Besides, an anti-windup structure is used in the speed loop regulation to ensure robust control with less static error. A comparison is done between the proposed techniques-based multilevel inverters and the conventional technique-based two-level inverter. Satisfactory results have been got by numerical simulation using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In this paper, the desired yaw orientation for an autonomous underwater vehicle is attained by acquiring a cascaded control structure based on a robust optimal control algorithm with a backstepping approach. A robust state feedback optimal control law is designed to control the yaw rate. Hence, the desired yaw rate is intended to be obtained by the backstepping controller by controlling the desired yaw orientation. The implementation of the proposed robust control algorithm is formulated by using semi-definite programming. A linear quadratic regulator in terms of linear matrix inequality is designed to address the control problem. The design of robust optimal control law in the steering plane is achieved by considering an uncertain polytopic AUV system. Realization of the proposed control algorithm is conducted in MATLAB/Simulink environment using the YALMIP tool. Robust behavior is ensured by the proposed control algorithm while tracking the desired yaw. The robustness analysis is extended by considering the various ranges of specific uncertain parameters to highlight the efficacies of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Using a novel panel dataset on recent immigrants in Switzerland, we study the relationship between the degree of skills utilization, the foreign-acquired education and its certification in the host country. We find that the relationship with foreign education is negative, especially when acquired in a non-EU country, in line with the literature documenting the imperfect international transferability of human capital. Obtaining a "Certificate of Equivalence" in Switzerland makes this relationship statistically non-significant: in other words, the certification enables immigrants to enjoy the same degree of skills utilization in the Swiss labour market as those with Swiss education. Additional results suggest that immigrants with a foreign but not Swiss-certified education keep the degree of skill utilization as high as it would be if they were Swiss educated when they obtain a job contract or job offer before migrating to Switzerland. These findings are robust to controlling for self-selection on unobserved characteristics. [ABSTRACT FROM AUTHOR]

The prevalence and genetic diversity of the protozoan pathogen Giardia duodenalis have been extensively studied worldwide. There is currently a lack of data regarding the genetic variability of the organism in eastern India. Understanding the circulating genotypes and associated risk factors is crucial for effective planning and implementing control measures. Therefore, the objective of the study was to conduct an epidemiological study to determine the prevalence and identify the various genotypes present. This survey adds to our knowledge on the occurrence and distribution of Giardia genotypes in the studied region. The overall prevalence was found to be 6.8%. This parasitic infection was significantly associated with two age groups, i.e., >0–5 years and >5–12 years. Using a multilocus genotyping method, we genotyped 52 human Giardia isolates that were obtained from diarrheal patients. Two distinct assemblages were found in the population—30.8% belonged to assemblage A; 63.5% belonged to assemblage B, prevalent in the population; and 5.7% belonged to a combined assemblage A+B. Sub-assemblage AII was found in 17.3% of the cases, followed by sub-assemblage AI (13.5%). High levels of genetic diversity were found within the population of assemblage B undergoing balancing selection. Overall, the high prevalence of the parasite observed, particularly among children, raises a major concern and necessitates implementation of robust control measures. Furthermore, we report the presence of numerous unique genotypes, circulating in this limited geographical boundary, which can be useful dataset for future studies. [ABSTRACT FROM AUTHOR]

This paper presents a study on the dynamic model and robust controller design for enhancing ride comfort in railway vehicles. An analytical model is introduced, encompassing the lateral, roll, and yaw motions of wheelsets, bogies, and the car body, while incorporating an efficient method for computing wheel-rail interactions. Rail irregularities and their impact on vibration excitation are also examined. A robust controller design methodology is proposed to address uncertainty and ride comfort quality, considering the significant variations in mass and moment of inertia experienced by railway vehicles. Through simulations, the designed controller consistently demonstrates an approximate 2 dB improvement in ride comfort across different levels of uncertainty. Future research directions include developing controller designs considering actuator response characteristics and identifying additional conditions for practical implementation. This study contributes to understanding railway vehicle dynamics and lays the foundation for effective control strategies to enhance ride comfort while accommodating uncertain operating conditions. [ABSTRACT FROM AUTHOR]

Present space robots such as planetary robots and flexible robots have structural flexibility in their arms and joints that leads to an error in the tip positioning owing to tip deflection. The flexible-link manipulator (FLM) is a non-collocated system that has unstable and inaccurate system performance. Thus, tip-tracking of FLM possesses difficult control challenges. The purpose of this study is to design adaptive intelligent tip-tracking control strategy for FLMs to deal with this control challenges of FLM. A vision sensor is utilized in conjunction with a traditional mechanical sensor to directly measure tip-position in order to address the aforementioned problem. Image-based visual servoing (IBVS), one of several visual servoing control techniques, is more efficient. However, the IBVS scheme faces numerous difficulties that impair the system's performance in real-time applications, including singularities in the interaction matrix, local minima in trajectory, visibility issues. To address the issues with the IBVS scheme, a novel adaptive intelligent IBVS (AI-IBVS) controller for tip-tracking control of a two-link flexible manipulator (TLFM) is designed in this study. In particular, this paper addresses the IBVS issues along-with retention of visual features in the field-of-view (FOV). First, in order to retain object within the camera FOV, an intelligent controller with off-policy reinforcement learning (RL) is proposed. Second, a composite controller for TLFM is developed to combine RL controller and IBVS controller. The simulation has been conducted to examine the effectiveness and robustness of the proposed controller. The obtained results show that the AI-IBVS controller developed here possesses the capabilities of self-learning and decision-making for robust tip-tracking control of TLFM. Further, a comparison with other similar approach is presented. [ABSTRACT FROM AUTHOR]

The competitive merits of permanent magnet synchronous motor (PMSM) in the industrial environment include its high efficiency and precision. The fact is that PMSM models show highly nonlinear and high-order with time-varied parameters which require the advanced controller available. The precise and fast control with less overshoot response plays crucial factors in designing modern PMSM control schemes. This paper proposes the advanced adaptive neural sliding mode controller (ANSMC) to ensure an accurate and robust speed control for PMSM driving system. The proposed ANSMC technique based on field oriented control (FOC) scheme demonstrates its robustness and obtains competitive control quality in comparison with standard SMC and FOC-PI control methods. The PMSM speed control using proposed control approach shows the superiority over other advanced control techniques. [ABSTRACT FROM AUTHOR]

Dead-time is the most important disturbance in a voltage-source inverter's operation. It introduces low-order harmonics at the inverter's output voltage. To compensate for the dead-time effects in three-phase grid-tied inverters, this paper proposes a Linear Quadratic Gaussian (LQG) multivariable control approach. The LQG multivariable control is known as a robust control approach while provides a high band-width for the closed-loop system. Therefore, it promises significant attenuations in the dead-time introduced harmonics. To achieve a high performance, we run the three-phase grid-tied inverter in the current-controlled mode. Based on the nominal multivariable model derived for the three-phase grid-tied inverter in a synchronous reference frame, the LQG controller is composed such that the closed-loop system exhibits robust stability while attenuates disturbances significantly. The dead-time introduced harmonics produce disturbances in the synchronous reference frame with the highest frequencies. This is the reason for considering the dead-time as the most important disturbance in an inverter's operation. For an experimental set-up manufactured for the three-phase grid-tied inverter, we developed a detailed model in MATLAB/Simulink. It is employed for the performance verifications of designed LQG controller. Extensive results are presented for different important scenarios, based on which, the excellent performance of proposed approach is proven. In fact, by employing the proposed approach, the dead-time introduced harmonics are significantly attenuated such that a Total Harmonics Distortions (THD) of about 5% is achieved for the injected currents to grid which meets the IEEE 1547 standard. [ABSTRACT FROM AUTHOR]

The Takagi–Sugeno (T–S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T–S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T–S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T–S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method. [ABSTRACT FROM AUTHOR]

A decentralized robust control algorithm for robotic manipulator in joint space is proposed, in which the linkage and its joint driver are treated as a module with a torque sensor installed on its driver shaft connecting the successive module. Due to the coupling between two connected dynamic modules can be measured by the torque sensor, the computation of the nonlinear coupling effect caused by the rest upper linkages is no longer needed, which means the complexity of the dynamic equations of the manipulator will be greatly reduced and the real-time compensation calculation of manipulator dynamics is not required in its control loop. Furthermore, the joint torque sensor enables the feedback of the external disturbance and gravity load acting on the linkage, which equips the control system with a strong anti-interference capability. Since there is no need to compute the feedforward compensation joint torque, the structure of the control system becomes simple and the joint controller can be independently designed. An integral adaptive compensator is specifically designed to deal with the compensation of the dynamic coupling from the low linkages because such coupling cannot be sensed by the joint torque sensor. A robust control strategy was proposed to compensate for the friction parameter variation between the modules of the manipulator and the uncertainty caused by the reconfiguration of the manipulator. The computer simulation is conducted to verify the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This paper is mainly concerned with the leaderless consensus problem for nonlinear multi-agent systems (MAS) with unknown mismatched nonlinear dynamics and external disturbances. First, a novel adaptive controller is designed to achieve bounded consensus with a linear feedback term, the RBF neural network (RBFNN) adaptive approximation term, a discontinuous feedback term, and a state constraint term. Furthermore, on this basis, an observer-based robust uniform control scheme against disturbance is proposed to achieve the leaderless consensus of MAS with unmeasurable states under the directed graph. Compared with the previous works, the proposed controller is less conservative. Finally, two simulation-based examples are provided to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper develops a novel unified approach to designing suboptimal robust control laws for uncertain objects with different criteria based on a priori information and experimental data. The guaranteed estimates of the γ0, generalized H2, and H∞ norms of a closed loop system and the corresponding suboptimal robust control laws are expressed in terms of solutions of linear matrix inequalities considering a priori knowledge and object modeling data. A numerical example demonstrates the improved quality of control systems when a priori and experimental data are used together. [ABSTRACT FROM AUTHOR]

This paper proposes a method to design a real-time wind turbulence simulator. The objective of such a simulator is to make dynamical models more accurate in order to develop more robust controls, especially in the case of mechanical systems operating outdoors such as tracking antennas. The models used to generate a random wind speed are based on the wind spectral characteristics rather than time domain ones. Indeed, turbulence is a stochastic and non-stationary process corresponding to the short-term component of wind and therefore is difficult to model in time domain. Wind spectral characteristics are described by the power spectral density whose approximation is used in the real-time simulator to reproduce wind behavior. The von Kármán model is the most commonly used model to approximate the power spectral density of wind turbulence. However, this model was originally designed for aircraft, and therefore for high altitudes and moving systems. In our case, tracking antennas are used under different conditions: low altitude and slow moving systems. This makes the von Kármán model less precise in middle and high range frequency. Consequently, there is a need in more accurately modeling wind turbulence under these specific conditions. As von Karman's model expression uses fractional calculus, other models with the same range of parameter number are proposed to better approximate the wind power spectral density by using Cole-Cole and Davidson-Cole fractional models. The idea is to use shaping filters issued from these more precise fractional models to generate realistic random wind speed turbulence from a random white noise input. These filters are approximated by rational functions and their parameters are tuned in the same way as those of the von Kármán model with the same inputs (mean speed, standard deviation and length scale). If a rational model is sought, a very high number of parameters (more than six times) is needed to have the same precision as our proposed fractional Cole-Cole model which has only four parameters. Finally, an accurate turbulence wind speed generator is proposed. [ABSTRACT FROM AUTHOR]

The anti-lock braking system (ABS) is a vehicle safety technology that prevents wheels from locking during braking by tracking the optimal reference slip. Due to the structural characteristics of hydraulic circuits, a brake system has a discontinuous pressure control input which is composed of apply and dump modes. Sliding mode control is a robust control technique that uses discontinuous control input and is being studied expensively. However, sliding mode control has a crucial chattering problem caused by high compensation gain, which leads to high wearing of the mechanical parts. This problem can be alleviated by compensating for unknown disturbances with estimated values. A nonlinear disturbance observer can estimate the system's uncertainty without mathematical model information. Therefore, sliding mode control based on a nonlinear disturbance observer is proposed here, and the simulation results with ABS are compared with the original sliding mode control. Simulations are conducted with high- and low- μ road surface scenarios, and three key performance indicators for evaluations are compared. In conclusion, the brake performance was enhanced and the chattering issue of the sliding mode controller was reduced by proposed method. [ABSTRACT FROM AUTHOR]

Lateral instability is a key problem that restricts vehicle motion safety of distributed electric vehicles (DEV). This paper designs a new lateral control system composed of subsystems of high, middle and low levels and brings forth an improved sliding mode controller (SMC) method. The similar reference longitudinal velocity generation system is designed via neural network observer and fuzzy logic controller to ensure the smooth and safe driving of the vehicle on low adhesion roads. Relevant working simulation also is carried out to verify the effectiveness of the control method above. The results show that the new control system based on the improved SMC method brings about good yaw rate and speed tracking performance of vehicles and strong robustness of the control system, and can still ensure excellent lateral driving stability on the low adhesion roads, or when making a turn or the friction coefficient is changed. [ABSTRACT FROM AUTHOR]

This paper presents a robust path tracking controller based on a model predictive control (MPC) with steady-state inputs for disturbance compensation. A conventional MPC-based path tracker has a possibility to diverge due to model uncertainty and disturbance. Particularly, the noise of the sensor measurement can cause a deterioration in path tracking performance. A conventional robust controller is used to compensate for the disturbance. However, the constraints for state and inputs are not explicitly reflected in the conventional robust controllers. Therefore, this study focused on the development of the MPC-based robust path tracker. The proposed controller introduces the steady state solution to improve the robustness of MPC. A double lane change scenario is used to evaluate the proposed algorithm by using the co-simulation environment of MATLAB/Simulink and CarSim. Simulation results show that the proposed method is more robust against an increase in disturbance than the sliding mode controller. [ABSTRACT FROM AUTHOR]

Robust convex constraints are difficult to handle, since finding the worst-case scenario is equivalent to maximizing a convex function. In this paper, we propose a new approach to deal with such constraints that unifies most approaches known in the literature and extends them in a significant way. The extension is either obtaining better solutions than the ones proposed in the literature, or obtaining solutions for classes of problems unaddressed by previous approaches. Our solution is based on an extension of the Reformulation-Linearization-Technique, and can be applied to general convex inequalities and general convex uncertainty sets. It generates a sequence of conservative approximations which can be used to obtain both upper- and lower- bounds for the optimal objective value. We illustrate the numerical benefit of our approach on a robust control and robust geometric optimization example. [ABSTRACT FROM AUTHOR]