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Today, hydrogen fuel cells (HFCs) have become very popular in various applications because of their ability to be a clean energy source. One of the difficulties associated with HFCs is their sluggish response to variations in the load. This paper presents a nested control strategy based on a type-2 neuro-fuzzy controller (T2NFC) to improve the HFC's dynamic response in a hybrid stand-alone power network using HFC and battery. A system model is constructed using Matlab-Simulink. An interleaved converter is used to draw the maximal power from the HFC and reduce the ripple in the HFC's current. Two T2NFCs control DC voltage and battery charge/discharge current. The robustness of the T2NFC is evaluated for input disturbance, output disturbance, and both disturbances. The results show that the proposed control strategy is robust against input and output disturbances. Also, it provides improved dynamic response of the HFC, lower ripple in HFC current, and less overshoot or undershoot in DC voltage both in transient and steady-state. For step reference input, the proposed controller improves settling time of 24.06 % and overshoot of 59.55 % compared to the conventional PI controller. The results verify the effectiveness of the proposed controller under different operating conditions of hybrid stand-alone power network with fuel cell and battery. • The response of HFC is enhanced, leading to faster and more accurate power generation. • The hydrogen fuel cell's current is experienced a significant reduction in ripples. • The hydrogen fuel cell is now being operated at its maximum power point. • A Type-2 Neuro-Fuzzy Controller is developed to provide robust control of the hydrogen fuel cell's output voltage. [ABSTRACT FROM AUTHOR]

In this paper, an anti-unwinding adaptive robust backstepping control law is proposed to solve the attitude control problem of rigid spacecraft with external disturbances and inertia uncertainties. A new variable based on hyperbolic sine functions is designed to prevent the unwinding phenomenon, and then a novel robust backstepping controller is developed. Initially, a nonlinear tracking law is designed to obtain the desired angular velocity and to ensure that the attitude maneuvering system could have two equilibrium points. Subsequently, an adaptive robust control law is devised, which incorporates the adaptive estimation method to update the inertial parameters in real-time. Moreover, the bounded external disturbances are taken into account in the design of the control law, and thus the robustness of the attitude control system is improved. Furthermore, the global asymptotic stability of the attitude control system is verified by the Lyapunov-Krasovskii theorem. Finally, numerical simulations are carried out to demonstrate the effectiveness and robustness of the proposed control law, as well as the anti-unwinding characteristics are perfectly validated during the attitude maneuver of rigid spacecraft. [ABSTRACT FROM AUTHOR]

This study focuses on enhancing the control precision and efficiency of a two-degree-of-freedom (2-DOF) space manipulator used for active space debris removal. The unpredictable space environment introduces large uncertainties, which introduces unique challenges beyond the capabilities of a standalone computed torque controller and degrades control performance. To address this problem, a robust controller is developed, integrating traditional techniques such as sliding mode and computed torque control with a Neural Network framework. This synergy leverages both methods' strengths—conventional controls' accuracy and Neural Network's adaptability. The integration of Neural Network-based sliding mode control complements the robustness of computed torque control by actively mitigating uncertainties and disturbances inherent in the space environment. The 2-DOF manipulator's state variables model the system dynamics, necessitating accurate relative motion estimation between the manipulator and debris. The global asymptotic stability of the developed algorithm is demonstrated through the Lyapunov theorem, guaranteeing error convergence to zero. The convergence, stability, precision, tracking errors, and responsiveness of the controller have been analysed and validated by the MATLAB Simulink simulations. The novel approach's performance effectiveness is substantiated by numerical simulations and a comparative analysis with conventional computed torque control. Outcomes highlight the superior precision and efficiency in manipulator tracking the trajectory, validating the integrated controller's potential for successful active space debris removal. • Neural network-based sliding mode computed torque control (NSMCTC) approach ensures precise space manipulator position tracking. • NSMCTC reduces chattering and improves convergence for enhanced trajectory control in dynamic space environments. • Lyapunov stability theorem validates uniform and ultimate boundedness, ensuring global asymptotic stability. • Self-learning capability and low computational complexity distinguish NSMCTC from traditional computed torque control methods. • Potential applications extend to spacecraft manipulator attitude control, offering adaptability for safe debris capture in multidimensional dynamics. [ABSTRACT FROM AUTHOR]

The proton exchange membrane fuel cell (PEMFC) air supply system is a complex nonlinear system with strong coupling of air flow and pressure. To avoid oxygen starvation and unstable output performance during rapid load changes, the oxygen excess ratio (OER) and air pressure need to be controlled effectively. To cope with the nonlinearity, coupling and uncertainty of the PEMFC air system, this paper puts forward a coordinated controller regulating OER and air pressure based on the input-output linearization and sliding mode methods. Firstly, a control-oriented third-order model is proposed to describe the dynamic behavior of the air supply system. Secondly, this paper decouples the flow and pressure of the PEMFC air system by using an input-output linearization method to obtain two independent linear subsystems. Moreover, a sliding mode method, effectively coping with the uncertainty of the system model, is designed to regulate the OER and air pressure of the PEMFC, which enhances the robustness of the control system. The overall simulation outcomes show that the proposed controller regulates OER and cathode pressure with smaller root mean square error (RMSE) and mean absolute percentage error (MAPE) compared with the combined proportional integral (PI) and feedforward controller. • A modified model oriented to control of air supply system is proposed. • Input-output linearization and sliding mode are used to develop coordinated control strategies. • The outcomes illustrate that the air supply affects the output performance of the fuel cell. • Control performance in the presence of model uncertainty is verified. [ABSTRACT FROM AUTHOR]

• We construct a multidimensional poverty index for chinese counties. • We establish a staggered DID model that controls for selection bias. • Highways significantly reduce county poverty. • The poverty-reduction effect of highways is conditional. • The poverty-reduction effect of highways diminishes with increasing altitude. This paper establishes a staggered difference-in-differences (DID) model to estimate the impact of highways on regional poverty using county-level panel data from China. We construct a multidimensional poverty index and use satellite-monitored night light brightness as a proxy indicator. The results demonstrate that highways significantly reduce county poverty and that the poverty reduction effect becomes increasingly pronounced over time. This result remains robust after controlling for non-random highway route selection. Furthermore, the poverty reduction effect of highways is conditional, with a significant poverty reduction effect in the less economically-developed western regions and non-municipal counties, and no significant poverty reduction effect in the economically-developed eastern and central regions and municipal districts. Moreover, the poverty reduction effect of highways gradually decreases as the average altitude of counties increases. Our tests provide empirical evidence for effective road investments in developing countries that incorporate poverty alleviation targets. [ABSTRACT FROM AUTHOR]

Assessments of action semantics consistently reveal markers of Parkinson's disease (PD). However, neurophysiological signatures of the domain remain under-examined in this population, especially under conditions that allow patients to process stimuli without stringent time constraints. Here we assessed event-related potentials and time-frequency modulations in healthy individuals (HPs) and PD patients during a delayed-response semantic judgment task involving related and unrelated action-picture pairs. Both groups had shorter response times for related than for unrelated trials, but they exhibited discrepant electrophysiological patterns. HPs presented significantly greater N400 amplitudes as well as theta enhancement and mu desynchronization for unrelated relative to related trials. Conversely, N400 and theta modulations were abolished in the patients, who further exhibited a contralateralized cluster in the mu range. None of these patterns were associated with the participants' cognitive status. Our results suggest that PD involves multidimensional neurophysiological disruptions during action-concept processing, even under task conditions that elicit canonical behavioral effects. New constraints thus emerge for translational neurocognitive models of the disease. • We examined EEG markers of action concept relatedness in Parkinson's disease (PD). • Patients and controls were faster to process related than unrelated action trials. • Underlying N400 and theta effects were robust in controls and absent in patients. • Accompanying mu rhythms were contralateralized in patients. • Key neural signatures of action relatedness and grounding are altered in PD. [ABSTRACT FROM AUTHOR]

The slant-axis telescope has a novel structure, and its unique structural design is more suitable for extreme environment such as the South Pole. However, there is a lack of research on the dynamic modeling and the controller design of slant-axis telescopes at home and abroad. This paper proposes a dynamic modeling and robust control method for slant-axis telescope. Firstly, the dynamical analysis of the slant-axis telescope is carried out. The 2-degree-of-freedom rigid body model of the telescope is established by the Lagrange method. Next, combining the flexibility of the driver system and disturbance, a mathematical model of the rigid-flexible coupling system of the slant-axis telescope is completed. Then, according to the established mathematical model, a sliding mode controller based on the disturbance observer is designed to suppress the disturbance, and realize the robust control of the slant-axis telescope. Finally, the simulation results show that the disturbance observer based sliding mode controller gets better dynamic performance and anti-disturbance characteristics than the traditional Proportion-Integration-Differentiation (PID) controller in the case of considering nonlinear external interference of the model. [ABSTRACT FROM AUTHOR]

To address the escalating demand and supply constraints for semiconductor devices, manufacturing techniques such as thermal atomic layer etching (ALE) have been utilized, which require robust temperature control systems. Radiative heating with lamps is a promising method for achieving fast and precise temperature control. However, there is limited research in analyzing the dynamic control of radiative lamp heating systems. In this study, a transient, two-dimensional (2-D) radiative heating model in a cross-flow thermal ALE reactor is constructed through Ansys Fluent. A sparse identification (SINDy) modeling approach is applied to build a reduced-order dynamic model for the prediction of system states in the control system. A model predictive controller (MPC) is then developed to bring the wafer surface temperature to the target temperature while preserving the uniformity of the temperature on the wafer surface. Control is implemented by adjusting the powers of three groups of heating lamps independently. Notably, a novel feedback-based time-varying steady-state penalty approach is applied with the MPC in this study, which enables the system to reach the target temperature range within 10 s while maintaining temperature uniformity for 1000 s. • Computational fluid dynamics modeling of wafer temperature profile. • Sparse model identification using spatio-temporal temperature data. • Model predictive control using nonlinear sparse model. • Implementation of MPC on CFD model of the process. [ABSTRACT FROM AUTHOR]

A pressure control method that incorporates parameter adaptation has been devised for the pressure control system of pintle solid rocket motors, which exhibit notable nonlinearities and uncertainties in parameters. Firstly, the mathematical relationship between pressure and pintle displacement is derived using the zero-dimensional interior ballistic model., and the state space equation is deduced by incorporating the dynamics of motor inertia load. Secondly, by capitalizing on the robustness of robust control, the parameter identification capabilities of adaptive control, and the rigor of Lyapunov stability theory, the proposed approach enables precise control of the pintle position while effectively addressing uncertainties, thereby enhancing the overall performance and stability of the system. Finally, the aforementioned theoretical framework has been validated through rigorous numerical simulations. The findings demonstrate a notable enhancement in the pressure response speed of the adaptive robust controller, with improvements of 10.01 % and 13.16 %, 0.03 % and 1.54 %, 0.06 % and 22.39 % in different orders of magnitude of free volume as compared to the robust controller and PID controller, respectively. Moreover, it is evident that the adaptive robust controller relies on the accuracy of the model. The applied controller demonstrates notable advantages in terms of stability, robustness, and accuracy, thereby substantially enhancing the overall performance of the pressure control system. • The state space equation for the pressure of the pintle solid rocket motors is derived. • The pressure dynamics equation and the motor inertial load equation are combined. • An adaptive robust controller with respect to pressure is designed. • The controller response laws for various free volumes, pressure index and characteristic velocity are obtained. • The adaptive robust controller exhibits a higher dependence on model accuracy is observed. [ABSTRACT FROM AUTHOR]

In this paper, a delayed distributed proportional control is developed for a class of multiple-input-multiple-output systems in order to reduce the effects of external disturbances and uncertain dynamics. The system under consideration is a three degrees-of-freedom planar robot, consisting of an active joint driving the first link and passive flexible joints for the second and third links. The control challenge for this system is to drive the flexible arm around the desired setpoint or trajectory by controlling the first link. Actually, by using a specific Lyapunov-Krasovskii storage functional associated with a constrained optimization problem, the robustness of the proposed proportional retarded control action is achieved. This analysis, together with the attractive ellipsoid concept allows to conclude with Ultimately-Uniformly-Bounded stability. The closed-loop control algorithm is tested in numerical simulation and a comparative study with some existing recent control schemes based on the performance error is presented. • The proposed control scheme belongs to a class of multivariable proportional time delay distributed controller. • After finite time the trajectories of a flexible manipulator stay within reduced size attractive ellipsoid. • Ultimate Uniformly Bounded Stability of the controlled system is demonstrated by using a Lyapunov-Krasovskii functional. • The proposed algorithm can also be adapted for a class of nonlinear uncertain/disturbed multivariable output systems. • A sufficient condition to guarantee the feasibility of specific Bilinear Matrix Inequality is presented. [ABSTRACT FROM AUTHOR]

—Currently, most vehicles are generally equipped with roll stability control system or electronic stability control system to improve stability. However, when encountering emergency obstacle avoidance and similar conditions, roll stability control system may compete with path tracking control system for vehicle control, resulting in the inability to balance path tracking and roll stability. Therefore, a closed-loop feedback Nash game collaborative control method is proposed to solve the collaborative problem between the two control systems. Firstly, a fifth-order vehicle model with coupled yaw-roll dynamics is presented, which was extended to a vehicle-road closed-loop model based on lateral position, yaw angle and road preview information. Secondly, the path tracking control system and roll stability control system are regarded as two agents in the dynamic game control process. On this basis, a Nash game collaborative control strategy is designed by fully considering the control interaction between two game agents. Finally, considering the influence of uncertain disturbances, a finite-time minimax robust control strategy is proposed by using minimax optimization theory. Simulation and hardware in loop experiments show that the minimax robust regulator based on Nash game controller can effectively improve the path tracking and roll stability performance of vehicles. Moreover, the regulator improves the robustness of the system when it is subjected to bounded uncertain lateral disturbances. [ABSTRACT FROM AUTHOR]

• A novel active control method is proposed to suppress the milling chatter of thin-walled workpieces. • The non-smooth properties, time delays, multi-modal participation, and varying dynamic characteristics are synthesized together in controller design for the first time. • The combination of robust control and time delay controller enhances the control effect and reduces the control voltage to the single robust control. Chatter phenomena commonly plague the milling process of flexible workpieces, leading to undesirable fluctuations in accuracy and diminished machine efficiency. Limited by complex dynamics of this process, the non-smooth properties, time delays, multi-mode participation, and varying dynamic characteristics have never been considered together in active control. This paper proposes a novel active control method to suppress milling chatter of flexible workpieces by synthesizing these factors together in controller design for the first time. The dynamic model for milling processes of a cantilevered plate, which is regarded as an examined example of flexible structure, is transformed from a time delay differential equation with varying coefficients to a real-time differential equation with constant coefficients by uncertainty transformation. A robust combined time delay control method is tailored for the uncertainty system via combining a robust controller with synthesis method and an active time delay controller. Both simulations and experiments validate the effectiveness and performance advantages. Following the implementation of experiments, the maximum stable milling depth experiences a notable improvement from 0.1 mm to 0.8 mm after control, and is larger than that of 0.6 mm under single robust control. Additionally, under the given milling conditions, the control voltages of robust combined time delay control exhibit a 25 % and 11 % reduction compared to those of the robust control method. The novel combination of a robust controller and time delay controller enhances the control effect and reduces the control voltage. [Display omitted] [ABSTRACT FROM AUTHOR]

Robust control of Unmanned Surface Vehicles (USVs) is crucial for their safe and reliable navigation. The control methods of USV generally regard the results of pose estimation (such as GNSS/INS) as an accurate reference, but GNSS/INS is usually fluctuating and uncertain in the open water, which easily leads to large tracking errors or even control failure of USV. To this end, a robust sliding mode control of USV considering the active compensation of pose estimation uncertainty is innovatively presented, which can ensure the stability and accuracy of USV's control. The distinctive features of the proposed method are twofold: (i) To improve the pose estimation accuracy and obtain accurate uncertainty evaluation, an improved adaptive Kalman filtering considering the time-varying solution state is presented, which utilizes the convergence characteristics of INS and the time-varying features of GNSS solution state; (ii) By integrating pose estimation uncertainty assessment and active compensation, a Robust Finite-Time Sliding Mode Control (RFT-SMC) for a twin-propeller unmanned surface vehicle is developed to enhance tracking accuracy and anti-disturbance capability, and its convergence is established using a Lyapunov function, ensuring reliable operation of the USV even in the presence of uncertain pose estimation. Finally, verification experiments are offered to verify the effectiveness of the presented RFT-SMC method. • An innovative integrated control-perception scheme of USV is proposed. • An improved adaptive Kalman filtering considering the time-varying solution state is proposed. • Robust finite-time sliding mode control with pose estimation uncertainty assessment and compensation is developed. [ABSTRACT FROM AUTHOR]

The influence of wave motion can cause undesired motion in the spreader during the use of cranes for marine replenishment operations, potentially leading to significant impacts on the efficiency and safety of such operations. A novel rope-driven wave motion compensation device (R-WMCD) has been developed to achieve wave motion compensation for marine cranes in roll, pitch, and heave directions. Firstly, the attitude forward/inverse solution models of R-WMCD are established using Newton-Raphson iteration method, and the kinematic model is derived based on the vector closure method. Secondly, the dynamic model of R-WMCD is established using Newton-Euler method, considering the uneven loading condition. Furthermore, to enhance the precision of wave motion compensation, a robust proportional-derivative (R-PD) controller based on the nominal model is formulated, and the stability of the controller is demonstrated. The feasibility and validity of the scheme are ultimately confirmed through simulation and experimentation, demonstrating R-WMCD's capability to achieve wave motion compensation. The research presented in this paper offers a novel approach to the design of the structure and theoretical analysis of 3-degree-of-freedom (3-DOF) wave motion compensation device. • A novel 3-DOF wave motion compensation device is proposed for marine cranes. • The mathematical model of the R-WMCD is established in a non-inertial frame. • The danger area of the barycenter position of the spreader is analyzed. • A robust PD controller based on the nominal model is designed. [ABSTRACT FROM AUTHOR]

In this paper, a robust H ∞ / H 2 controller is proposed for the uncertainty problem of the riser recoil model caused by the degradation of the mechanical properties of the tensioner and the change of the installation inclination. First, an incremental riser recoil discrete-time state-space model with the uncertainty of the controller input matrix is established by considering the tensioner installation inclination change and the mechanical property degradation as permissible perturbations. Then, a discrete-time H ∞ / H 2 controller is designed to simultaneously improve the anti-disturbance performance and the dynamic performance of the tensioner. The state weight matrix and input weight matrix of the linear quadratic controller are used to construct the performance output equation, establish the linear matrix inequality constraints to satisfy the robust H ∞ performance index, minimize the upper bound of the quadratic performance index, obtain the gain matrix, and realize the robust H ∞ / H 2 guaranteed performance control. Finally, based on the field data, the control effects of the robust H ∞ / H 2 controller and the LQR are simulated and compared. The results verify the effectiveness and superiority of the proposed controller, showing that the tensioner has good dynamic and steady-state characteristics with the introduction of perturbations. • A novel discrete-time robust H ∞ /H 2 control strategy is presented for managing riser recoil in deepwater drilling operations to improve safety and efficiency. • Address the challenges of equipment performance loss or failure and extreme marine environments by improving the robustness and responsiveness of tensioner systems under model uncertainty. • Demonstrate the excellent anti-disturbance performance and dynamic performance of the proposed control strategy. • Validate the effectiveness of Discrete Robust H ∞ /H 2 Best Guaranteed Performance Control (DRHGPC) through simulation and emphasise its potential for practical application in improving the safety of deepwater drilling. [ABSTRACT FROM AUTHOR]

This paper is concerned with anti-disturbance output feedback control of uncertain systems via dynamic guaranteed cost triggering mechanism. An output disturbance observer is constructed to attenuate matched disturbances and the mismatched one is attenuated by the H ∞ approach. A new dynamic event-triggered mechanism is established in terms of guaranteed cost triggering condition and triggering threshold. A composite controller is built on an observer based state feedback controller and an output disturbance compensator. By employing the Lyapunov stability method, a sufficient condition is presented in the framework of linear matrix inequality to ensure the asymptotic stability of the closed-loop system with the prescribed cost. Comparative simulation studies are supplied to verify the effectiveness of the proposed composite control scheme. • Compared with the state DOB [25] , an output DOB is established firstly to reconstruct matched disturbances in input channels. • A new dynamic ETM is put forward and contains [36] as a special case. • A composite controller is constructed by an observer-based state feedback controller and a disturbance compensator. • A structural separation technique is utilized to tackle the controller gain and the observer gains. [ABSTRACT FROM AUTHOR]

The new railway mobility paradigm Virtual Coupling, recognized as one of the most promising solutions to face the ever-increasing demand in railway transportation and the saturation of network capacity, enables two or more trains to virtually couple in a single convoy, hence reducing the headway among them. Within this context, the design of an effective and robust Virtual Coupling control strategy for uncertain nonlinear heterogeneous train convoys, able to simultaneously cope with uncertain nonlinearities and unexpected/unpredictable external factors, is an open challenge in the railway field. This is very crucial for High-Speed Trains, where, due to the high-speed operating ranges, uncertain factors have a stronger impact on Virtual Coupling performance. To deal with this issue, this work exploits the ability of Deep Reinforcement Learning based strategies in defining an optimal control policy via an iterative exploration of the surrounding unknown environment and without detailed knowledge of the plant dynamics. Specifically, we propose a novel Deep Deterministic Policy Gradient based control strategy to coordinate and manage the High-Speed Trains convoy such that this latter can autonomously adapt its behaviour to all the encountered driving scenarios. The effectiveness of the proposed approach is evaluated via simulation analyses, carried-out via an ad-hoc implemented Virtual Coupling Train System simulation platform. After verifying the efficiency of the training process in ensuring the fulfilment of the Virtual Coupling control objectives, extensive non-trivial simulations, also involving cooperative manoeuvres, are performed for the validation phase. Results confirm how the proposed model-free Deep Deterministic Policy Gradient approach guarantees the Virtual Coupling for nonlinear heterogeneous High-Speed Train convoys despite the co-presence of uncertainties and unknown external factors. Finally, the advantages and the benefits of the proposed data-driven control are disclosed via a comparison analysis against model-based control strategies. [ABSTRACT FROM AUTHOR]

—Currently, most vehicles are generally equipped with roll stability control system or electronic stability control system to improve stability. However, when encountering emergency obstacle avoidance and similar conditions, roll stability control system may compete with path tracking control system for vehicle control, resulting in the inability to balance path tracking and roll stability. Therefore, a closed-loop feedback Nash game collaborative control method is proposed to solve the collaborative problem between the two control systems. Firstly, a fifth-order vehicle model with coupled yaw-roll dynamics is presented, which was extended to a vehicle-road closed-loop model based on lateral position, yaw angle and road preview information. Secondly, the path tracking control system and roll stability control system are regarded as two agents in the dynamic game control process. On this basis, a Nash game collaborative control strategy is designed by fully considering the control interaction between two game agents. Finally, considering the influence of uncertain disturbances, a finite-time minimax robust control strategy is proposed by using minimax optimization theory. Simulation and hardware in loop experiments show that the minimax robust regulator based on Nash game controller can effectively improve the path tracking and roll stability performance of vehicles. Moreover, the regulator improves the robustness of the system when it is subjected to bounded uncertain lateral disturbances. [ABSTRACT FROM AUTHOR]

The new railway mobility paradigm Virtual Coupling, recognized as one of the most promising solutions to face the ever-increasing demand in railway transportation and the saturation of network capacity, enables two or more trains to virtually couple in a single convoy, hence reducing the headway among them. Within this context, the design of an effective and robust Virtual Coupling control strategy for uncertain nonlinear heterogeneous train convoys, able to simultaneously cope with uncertain nonlinearities and unexpected/unpredictable external factors, is an open challenge in the railway field. This is very crucial for High-Speed Trains, where, due to the high-speed operating ranges, uncertain factors have a stronger impact on Virtual Coupling performance. To deal with this issue, this work exploits the ability of Deep Reinforcement Learning based strategies in defining an optimal control policy via an iterative exploration of the surrounding unknown environment and without detailed knowledge of the plant dynamics. Specifically, we propose a novel Deep Deterministic Policy Gradient based control strategy to coordinate and manage the High-Speed Trains convoy such that this latter can autonomously adapt its behaviour to all the encountered driving scenarios. The effectiveness of the proposed approach is evaluated via simulation analyses, carried-out via an ad-hoc implemented Virtual Coupling Train System simulation platform. After verifying the efficiency of the training process in ensuring the fulfilment of the Virtual Coupling control objectives, extensive non-trivial simulations, also involving cooperative manoeuvres, are performed for the validation phase. Results confirm how the proposed model-free Deep Deterministic Policy Gradient approach guarantees the Virtual Coupling for nonlinear heterogeneous High-Speed Train convoys despite the co-presence of uncertainties and unknown external factors. Finally, the advantages and the benefits of the proposed data-driven control are disclosed via a comparison analysis against model-based control strategies. [ABSTRACT FROM AUTHOR]

Utilizing parallel learning technology and an event-triggering mechanism, we introduce an adaptive security tracking control approach for nonlinear systems with both matched and mismatched uncertainties. The methodology commences with an overview of the constraints imposed by the control barrier function (CBF) on the system state, followed by the selection of a suitable CBF. An augmented system is constructed by amalgamating the system state and tracking error, with the mismatched uncertainty being decomposed to introduce an auxiliary system. Incorporating the CBF into the performance metrics ensures that the system state remains within a secure region. Subsequently, the paper employs an event-triggering mechanism to derive an event-based secure Hamiltonian–Jacobi–Bellman (HJB) equation. An adaptive single-critic network, designed using parallel learning technology, approximates the solution to the event-based secure HJB equation. Lastly, the Lyapunov method is employed to demonstrate the uniform ultimate boundedness (UUB) of both the tracking error and weight error. The efficacy of the proposed method is validated through two nonlinear examples. [ABSTRACT FROM AUTHOR]

The goal of robust motion planning consists of designing open-loop controls which optimally steer a system to a specific target region while mitigating uncertainties and disturbances which affect the dynamics. Recently, stochastic optimal control has enabled particularly accurate formulations of the problem. Nevertheless, despite interesting progresses, these problem formulations still require expensive numerical computations. In this paper, we start bridging this gap by leveraging statistical linearization. Specifically, through statistical linearization we reformulate the robust motion planning problem as a simpler deterministic optimal control problem subject to additional constraints. We rigorously justify our method by providing estimates of the approximation error, as well as some controllability results for the new constrained deterministic formulation. Finally, we apply our method to the powered descent of a space vehicle, showcasing the consistency and efficiency of our approach through numerical experiments. [ABSTRACT FROM AUTHOR]

This paper is dedicated to a kind of stochastic two-stage optimal control problem (TSOC) under G -expectation. The system's state switches at a given time point and the cost function involves stages on two time horizons in order to achieve two-stage management. A motivation of Problem TSOC is presented. A novel sufficient optimality condition (SOCD) is given via a sequence of adjoint processes coupled by jump boundary conditions under a maximal reference probability, which is more concise and applicable compared to previous results. Analytical results in two-stage linear-quadratic (TSLQ) case are presented, where a feedback optimal control (OC) is designed and a maximal reference probability is determined. Moreover, some interesting impacts of probability uncertainty and additional cost term to Problem TSLQ are analyzed, which are meaningful to practical risk management. Numerical simulations are presented to illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

Surge is a form of dynamic instability created as an unstable pattern in the flow of fluid and can severely affect centrifugal compressor performance by causing fluctuations in flow and pressure parameters. Due to the heavy and costly damage that the surge may cause in various industrial processes such as petrochemical plants, it is necessary to design an appropriate control system to reduce the effect of this phenomenon. The problem of active surge control of a centrifugal compressor using the throttle control valve (TCV) in the presence of compressor parametric uncertainties as well as large demands on upstream and downstream loads is investigated in this work. The control objective was to design a robust control system that can stabilize the compressor over a wide operating range without knowing the upper bound for the uncertainties and load demand. The controller should also react quickly by generating a smooth control signal without saturating the control input. These objectives are achieved by designing a sliding mode controller along with a nonlinear disturbance observer. The performance of the proposed disturbance observer-based controller is evaluated under various operational and load conditions and the results are compared against fuzzy type 1, conventional sliding mode, and wavelet-based neural network robust adaptive controllers. The results show that the proposed method can tolerate large disturbances without any knowledge on the upper bound of the incident disturbance, both on the downstream pressure and upstream mass flow which is highly desirable in practice. The comparative study proves the efficacy of the proposed method using various performance measures. The study also confirms the superior robust performance and stability of the proposed method in front of matched and mismatched disturbances as well as model uncertainties especially close to the instability boundary. Although choosing a TCV actuator has made the control system design easier, the sensitivity of the control valve to flow coefficient and zero calibration under different operating ranges of the compression system is studied carefully and some recommendations for the users are provided. • Active surge control of a compressor in the presence of uncertainty as well as the external disturbances is investigated. • A disturbance observer-based sliding mode controller is proposed to stabilise the compressor system in the surge area. • The nonlinear feedforward disturbance observer is designed to estimate the abrupt changes taking place in the system. • The proposed disturbance observer-based technique is compared with other techniques under various operational conditions. • The robust performance and stability of the method especially close to the instability boundary is proven numerically. [ABSTRACT FROM AUTHOR]

This paper proposes a tube model predictive control method considering uncertainties of renewable energy generation in the low-voltage distribution network. For relatively low dependence on communication networks, a decentralized voltage control framework is urgently needed to be developed for voltage regulation in the low-voltage distribution network. Opposed to the traditional voltage control techniques, a novel decentralized tube model predictive voltage control (TMPC) strategy is proposed. First, this strategy entails the formulation of a discrete model encompassing distributed photovoltaic units and energy storage systems intertwined with the distribution grid model based on voltage sensitivity. Then, considering the worst case of voltage deviations caused by uncertain and stochastic power fluctuation, we specifically discuss a robust positively invariant to determine the control section of controllers. Further, the strategy employs tube model predictive controllers encompassing nominal and auxiliary controllers in distribution areas. The nominal controller generates a central path for different voltage operating points of each distribution area, and the auxiliary controller makes all trajectories lie in a bounded neighborhood called a tube. The effectiveness of the proposed control strategy is investigated on a test system containing renewable energy resources. • A novel decentralized control structure is proposed to serve voltage control for distribution networks, which can cooperate for accurate voltage control with random and fluctuant renewable energy. • In the nominal controller, operation points of DGs are generated considering the generation and load scheduling. In the auxiliary controller, detailed control objectives and constraints are proposed for suppressing disturbances of DGs. • To balance the conservation and performance, a worst-case-based robust positively invariant is developed to quantify the range of tubes to cope with the randomness. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DER) are integrated into a microgrid through dc-dc power electronic converters. The bidirectional dc-dc converter regulates charging and discharging operations of ESS. Model predictive control (MPC), is a high-performance control technique for these converters, but it is limited in robustness to parameter mismatch, model uncertainties and sensor measurement noise. Therefore, in this paper, an improved hybrid cascade-parallel extended state observer (CP-ESO) is proposed, for model-free predictive control, to guarantee both robustness to parameter/model uncertainties and measurement noise suppression. A novel structurally-adaptive ESO scheme is also proposed to improve the disturbance rejection of CP-ESO during transient response. These results are supported by analysis and design guidelines for the selection of optimal sub-frequencies. Experimental results, for a bidirectional dc-dc boost converter, validate the effectiveness of the proposed methods against model uncertainties, external disturbances from variable input voltage and load, as well as measurement noise. • Analysis and design guidelines for multi-frequency extended state observers (ESO). • Improved cascade-parallel extended state observer with better noise suppression. • Novel structurally-adaptive ESO that gives better transient disturbance rejection. • Experimental validation with a bidirectional dc-dc boost power converter. [ABSTRACT FROM AUTHOR]

The ship-borne Stewart platform compensates for the six-degree-of-freedom motion induced by the ship, thereby enhancing operational reliability, safety, and expanding the operational window. Firstly, this paper investigates the dynamical characteristics during the motion compensation process. It reveals that the platform's motion generates an inertia force that counteracts the inertia force generated by the ship's motion, then only the gravity is left to compensate for each leg. Subsequently, the nonsingular terminal sliding mode control law of the platform is derived based on the dynamical characteristics to achieve robust control performance, followed by a comprehensive analysis of Lyapunov stability. To improve the accuracy of the robust controller, an adaptive estimation method utilizing time-delay estimation and Kalman filtering is employed to estimate the time-varying gravity. This approach significantly enhances the precision of the controller. Finally, to validate the compensation effect of the proposed method under conditions of parameter uncertainty, measurement uncertainty, and disturbances, a simulation model of the Stewart platform is established. Comparative analysis with the PID(Proportional-Integral-Derivative) control method and RBSMC(Recursive Backstepping Sliding Mode Control) demonstrates that the proposed method effectively improves the compensation rate. [ABSTRACT FROM AUTHOR]

Linear control-oriented models are important to represent canal dynamics for designing controllers. This study focuses on hydraulic control structure (gate) modelling to address the complex interdependent behavior inherent in irrigation canals. A comprehensive mathematical model that incorporates the water level with gate-opening to model discharge is introduced for single and multiple canal pool scenarios. The proposed model captures the hydraulic coupling within and among canal pools, a key finding. The model is evaluated extensively under uniform and non-uniform flows across three distinct canals, highlighting the model's applicability to various systems. The uncertainty inherent within the nominal model is also assessed for varying operating conditions and hydraulic parameters. The proposed model is compared with the existing and the Saint-Venant (SV) model, showing improved accuracy in water-level predictions. This advancement in hydraulic modelling contributes to adaptable canal models essential in developing robust controllers to enhance water management in irrigation canals. • A mathematical canal model is developed incorporating water level in hydraulic control structure. • A generalised model structure applicable to an arbitrary number of pools is developed. • The proposed model outperforms existing models in estimating water levels by capturing hydraulic coupling. • The model is evaluated across diverse canal configurations under uniform and non-uniform (backwater) flow conditions. • The model's uncertainty is assessed for varying operational conditions and the roughness coefficient. [ABSTRACT FROM AUTHOR]

This article investigates the robust control problem of affine nonlinear systems with both additive and multiplicative uncertainty. Different from existing actor-critic (AC) algorithms for adaptive dynamic programming (ADP), we introduce an uncertainty estimator and propose an actor-critic-estimator (ACE) algorithm. The proposed algorithm alternates between the value evaluation, uncertainty estimation, and policy update to generate the adaptive robust control law without knowing the system dynamics. Especially, during the step of uncertainty estimation, we approximate the uncertainty by a radial basis function neural network (RBFNN) and design the appropriate utility function accordingly instead of using the supremum of the uncertainty as in existing studies. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. We further demonstrate that the affine nonlinear systems with uncertainty is uniformly ultimately bounded (UUB) stable when the learned adaptive robust control law is adopted. The performance of the proposed algorithm is demonstrated through a torsion pendulum system and an inverted pendulum system. [ABSTRACT FROM AUTHOR]

The field of advanced therapies has witnessed unprecedented growth, presenting unique challenges in quality management. We explore the multifaceted aspects of ensuring quality throughout the lifecycle, from rapid development through the crucial transition to full-scale cGMP compliance using phase-appropriate risk management. The rapid pace of development in advanced therapies introduces a challenge in maintaining robust quality standards. Balancing speed and quality requires a dynamic approach that addresses the intricacies of research and development while adhering to stringent regulatory requirements. This demands a proactive risk management system that anticipates and mitigates risks associated with accelerated timelines. Strategies for material qualification, supplier validation, and risk mitigation must be implemented to ensure a seamless transition from development to manufacturing. Manufacturing space poses another challenge, particularly as therapies progress towards commercialization. Scaling up production necessitates careful consideration of facility design, equipment validation, and process scalability. Maintaining product consistency across different manufacturing scales requires an integrated quality management approach that aligns with regulatory expectations. The ultimate goal of commercialization is achieving full-scale cGMP compliance, and marks a pivotal phase which demands adherence to rigorous regulatory standards. Developing and implementing robust quality control measures, process validation protocols, and comprehensive documentation are imperative to ensure product safety, efficacy, and consistency. Leveraging risk management to inform decision making at early stages of the lifecycle is an opportunity for efficiency and streamlining the path to commercialization and operational success. The solution to these challenges lies in the proactive implementation of risk management at all levels of the organization. This includes embedding risk assessments in the facility design, material selection, process controls, release requirements, as well as change management. A comprehensive and adaptable risk management framework enhances the ability to identify, assess, and mitigate potential risks, ensuring the sustained quality and compliance of advanced therapies throughout their lifecycle. [ABSTRACT FROM AUTHOR]

We present a novel artificial intelligence approach that encompasses both predictive and prescriptive aspects for the challenging task of model-based control of industrial wood planers. These sophisticated lumber finishing machines are known for the complexity of their operation, and the available data pertaining to the planing process exhibits complex, non-linear patterns. First, we leverage an ensemble of Gaussian Processes with a specialized weighting scheme named Automatic State Matching , achieving a 39% reduction in prediction error for the thickness of the outgoing board compared to conventional industry methods, as corroborated by real-world data. Subsequently, the predictive strategy is utilized in a novel robust control strategy which exploits the properties of Gaussian Processes to prescribe settings for wood planers. An empirical evaluation on simulated data demonstrated the viability of our prescriptive method, resulting in an 83% reduction in deviation from a predetermined target dimension. [ABSTRACT FROM AUTHOR]

This article presents a novel adaptive contour control scheme for dual-linear-motors-driven gantry stages (DLMDGSs) with time-varying model parameters and disturbances, aiming to realize high-precision contour control. Specifically, a novel adaptive contour controller based on a coupled model is proposed, incorporating powerful projection adaption laws, to achieve the synchronization of multi-axis motors, regression of adaptive parameters, and high-precision contour performance. Additionally, a contour error estimation (CEE) method with four-order convergence rate is designed to ensure estimation accuracy, enhance estimation robustness, and reduce estimation time. Moreover, considering the periodicity of common control contour tasks, an iterative learning control (ILC) reference trajectory compensation structure is adopted to further improve the contour control effect. Finally, the stability and convergence of the closed-loop system are rigorously proven, and comparative experiments are conducted on a DLMDGS platform to demonstrate the superiority of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the synthesis of multiple resonant controllers in the voltage regulation loop of uninterruptible power supplies (UPSs) operating in parallel with droop control. The resonant controller gains are calculated through a convex optimization problem aiming to attenuate the rms value of circulating current between the UPSs. Linear matrix inequalities (LMIs) constraints ensure the robust closed-loop robust stability and performance under the insertion or removal of UPSs and load variations. Experimental results from two 3.5 kVA UPSs illustrate the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces theoretical conditions for the computation of a novel type of state-feedback controller that makes use of Linear Parameter Varying approaches for synthesis. The novelty of this new State-Feedback controller lies on the fact that the controller has a fixed structure with constants matrix gains. However, the controller gains are affine on a parameter dependent basis function, which allows the controller to self-schedule according to real-time changes of the varying parameters. This type of controller is conceived with a focus on implementation, as in contrast with most other LPV approaches. The implementation of this Parameter-Dependent State-Feedback controller does not require any online interpolation or matrix inverse operations, independently of the number of varying parameters. The performance of the proposed approach is validated in a Scaled Autonomous Vehicle for steering control in a path tracking application. [ABSTRACT FROM AUTHOR]

• Hybrid microgrid is an emerging and exciting research field in power engineering. • Presents systematic review on various control strategies for hybrid microgrid. • Comparison between control strategies satisfying various control objectives. • Discussion on research challenges in use of effective and robust control scheme. • Discussion on emerging trends in hybrid microgrid's operation and control. Presently, there is huge development in conventional power systems due to the evolution of modern smart grids, wherein interconnected microgrids with a high level of energy storage and renewable energy penetration are gaining popularity in the modern distribution network. Future electrical distribution systems, or even transmission structures that combine AC and DC sources and loads are thought to be most promising when it comes to hybrid AC-DC systems. Using a combined operation of both AC and DC microgrids through an interfacing converter, hybrid AC-DC microgrids are advanced and benefitted with the use of both AC and DC topologies. Power management techniques for these microgrids are among the most important operational aspects. This paper provides a systematic review on numerous schemes to control hybrid AC-DC microgrids. Basically, microgrid control strategies are categorized as local control and coordinated control. Coordinated control is further divided in three control strategies as, distributed, centralized, and decentralized control. All these control strategies are employed to satisfy many control objectives, like bus voltage and frequency regulation, power sharing, and management, power quality, stability and reliability improvement, energy management, and economic dispatch. In this paper, various aspects associated with these control objectives and limitations in their control strategies are discussed. Also, future prospects on control of hybrid AC-DC microgrids and an effective and robust control strategy for power management possibly to be employed, are presented in the concluding section of this paper. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in optimizing the shape of complex solids is the requirement to maintain a reasonable mesh quality not only at the boundary but also for the bulk discretization of the interior. Thus, additional regularization and, in many cases, re-meshing of the structure during the iterative process is unavoidable with a Lagrangian description. By tracking the shape update using an Eulerian representation, embedded boundary methods are a promising technique for eliminating mesh distortion problems. This work consistently combines the unique features of implicit Vertex-Morphing and embedded boundary methods, facilitating the node-based shape optimization of solids with industrial complexity. One of the crucial elements for solving the primal problem on a fixed background grid is an efficient and robust quadrature scheme. To this end, we incorporate the open-source C++ framework QuESo (https://github.com/manuelmessmer/QuESo) developed for the numerical integration of arbitrarily complex embedded solids defined by oriented boundary meshes, e.g., in STereoLithography (STL) format. Meanwhile, applying the Helmholtz/Sobolev-based (implicit) filter to the vertices of the embedded boundary mesh not only exploits the extensive design space of node-based optimization but also ensures robust control over the feature size. To realize the above methodology, this work introduces a novel sensitivity analysis that yields mesh-independent shape gradients with respect to the immersed boundary. Through a specific sensitivity weighting, we recover a continuous gradient field from discrete values calculated at the nodes of the background grid. In addition, the presented workflow ensures robust enforcement of challenging geometrical constraints, including minimum wall thicknesses and design space limitations. We critically assess our approach with benchmarks and structures of industrial relevance. In all examples, trivariate B-Spline bases span the background grid, providing highly accurate finite element solutions and shape sensitivities at every iteration. Moreover, the elimination of mesh distortion problems enables a successful termination at the local optimum, even for large shape modifications. • Extension of Vertex-Morphing to embedded boundary methods. • Rigorous elimination of mesh distortion problems. • Discretization-independent sensitivity analysis. • Robust enforcement of design space constraints. • Application to solid structures with industrial complexity. [ABSTRACT FROM AUTHOR]

With the widespread application of unmanned aerial vehicles, harsh environments require higher control performance. Numerous existing works offer solutions for handling external disturbances during flight; however, the timeliness of the disturbance compensation is rarely discussed. This study focuses on trajectory tracking under external wind disturbances, specifically under two types of interference: average wind and wind shear. After compensating for the disturbance, a novel antidisturbance sliding mode control is designed based on reference model, to realize accurate, robust, and chatterless trajectory tracking under wind disturbance. Firstly, we addressed the limitations of conventional extended state observers, nonderived forms, and inadequate use of information by introducing state compensation function observers to enhance estimation accuracy for states and disturbances that are challenging to measure directly. Moreover, the disturbance variation is estimated using tracking differentiator and then compensated in the controller to enhance antidisturbance sensitivity. In addition, the effectiveness and stability of the proposed observer and controller are analyzed. Finally, a two-step verification is presented, including simulation with input disturbances and actual flight under two wind field types; multiple industrial fans are used to generate the average wind and wind shear during the experiment. Two robust control methods, state-of-the-art active disturbance rejection control and sliding mode control, are also implemented for comparison, and the results indicate that more than 61.2% accuracy improvement over comparison methods is achieved in simulation and actual experiments. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for controlling an industrial process that exhibits uncertainty and significant nonlinear features. The proposed method utilizes a virtual unmodeled dynamic and data-driven nonlinear robust predictive control strategy. The representation of a controlled object involves a composite state space model that combines both linear and high-order nonlinear elements. Moreover, a robust model predictive controller is developed using the linear component. In addition, the notion of one-step optimal feedforward is used in combination with a compensating controller to handle the high-order nonlinear factor specifically. Subsequently, a compensation controller with incremental characteristics is developed for a modified version of the high-order nonlinear term. Furthermore, the stability conditions of the closed-loop system are derived, and an analysis is conducted on the stability and convergence of the proposed approach. The TTS20 three-capacity water tank was utilized in both simulations and practical scenarios. The study demonstrated that the suggested approach successfully reduces system output variations and enhances overall performance in response to unpredictable changes in the process's dynamic features. • Adding a feed-forward branch to the control system raises the stability margin, which is better than the RMPC. • This study explores the integration of unmodeled dynamics using a real-time predictive compensation technique. • The effective use of implicit data has the potential to enhance the control system's ability to adapt and adjust itself. [ABSTRACT FROM AUTHOR]

This paper presents the effect of a Battery Energy Storage System (BESS) on the power system inter-area oscillations under changing load conditions. The dynamic interaction between the BESS control modes and synchronous generators, along with BESS control parameters, influence the damping of prominent inter-area oscillations under different loading conditions. Furthermore, H ∞ mixed sensitivity scheme-based Wide-Area Damping Controller (WADC) is proposed for BESS. This controller is designed using the Linear Matrix Inequality (LMI) framework to mitigate the prominent inter-area oscillation of the power system. Sensitivity to control parameter variation and the geometric measure of the observability are utilized to select the location and feedback signal for the WADC. The performance of the proposed WADC is compared with an Energy Storage System-based stabilizer and WADC proposed in an existing work on a modified New England ten-machine benchmark system. The Real-Time Digital Simulator (RTDS) platform is used to assess the real-time viability of the proposed WADC. The simulation results demonstrate that the proposed WADC can effectively diminish several inter-area modes. Additionally, it offers robust performance under varying load conditions, contingencies and with communication delays in feedback signals. The proposed controller is also effective under the system integrated with Doubly Fed Induction Generator-based Wind Turbine System. • The impact of BESS on the power system inter-area oscillation damping is investigated. • The impact of dynamic control mode interaction between SGs and BESS on the system oscillation modes under various operating conditions is investigated. • A H ∞ mixed sensitivity scheme-based WADC is designed for BESS to alleviate the power system inter-area oscillations. • The H ∞ mixed sensitivity scheme-based WADC is implemented in the RTDS to assess its feasibility in a real-time environment. [ABSTRACT FROM AUTHOR]

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. Operational control is carried out through speed control of the vehicles and bus holding at the terminal. We propose a hierarchical control framework to solve this integrated problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules. • Charging scheduling for electric buses traditionally excludes operational control. • A robust control strategy can be obtained by combining these aspects. • Formulation of the bus network control problem as an MILP. • A scalable solution procedure is obtained with Lagrangian relaxation. • Better service regularity and lower charging costs are obtained. [ABSTRACT FROM AUTHOR]

• Developed a robust cooperative control strategy (RCC) strategy to improve the performance of CAV platoon against uncertain vehicle dynamics induced by vehicle sensor error and control errors. • Designed an efficient and globally convergent solution algorithm for the minimax problem. • Develop an effective method to estimate the ranges of the sensor errors and control errors. In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment. [ABSTRACT FROM AUTHOR]

The H ∞ control method is one of the most widely used methods of robust control theory, in which system stability can be guaranteed by using linear matrix inequalities (LMIs) and Lyapunov theory. The presence of various uncertainties such as parametric uncertainty, time delay, sensors/actuators faults and failure is inevitable in the active control of the building structures. This paper focuses on the uncertain structural control system, in which the controller synthesis process is complicated by a set of uncertainties and the generation of large and sparse matrices. Synthesizing a sub-optimal controller for this uncertain structural control system is done according to the new approach and to validate it, a comparison with the controller of critically damped conditions(CD) as an ideal state is performed. In order to compare the two mentioned controllers, two 3- and 8-story shear structures excited by synthetic earthquake are considered to assess the responses. The simultaneous presence of various types of uncertainties is examined and their effects on the results are investigated. The results indicate that the sub-optimal designed H ∞ robust controller displays good robustness against time delay; however, parametric uncertainty was evaluated as an unfavorable effect for the allowable time delay. Despite the negative impact of the parametric uncertainties, it was illustrated that the nominal sensors/actuators faults do not affect the allowable time delay. • Active control algorithm for uncertain systems. • Critically damped state in active control. • Sensitivity analysis for mass, stiffness, and damping parameters. • Different fault ranges of active control system sensors are compared with each other. [ABSTRACT FROM AUTHOR]

This work is concerned with solving neural network-based feedback controllers efficiently for optimal control problems. We first conduct a comparative study of two prevalent approaches: offline supervised learning and online direct policy optimization. Albeit the training part of the supervised learning approach is relatively easy, the success of the method heavily depends on the optimal control dataset generated by open-loop optimal control solvers. In contrast, direct policy optimization turns the optimal control problem into an optimization problem directly without any requirement of pre-computing, but the dynamics-related objective can be hard to optimize when the problem is complicated. Our results underscore the superiority of offline supervised learning in terms of both optimality and training time. To overcome the main challenges, dataset and optimization , in the two approaches respectively, we complement them and propose the Pre-train and Fine-tune strategy as a unified training paradigm for optimal feedback control, which further improves the performance and robustness significantly. Our code is accessible at https://github.com/yzhao98/DeepOptimalControl. • Benchmarking offline vs. online learning in neural network control. • Main challenges: dataset for offline, optimization for online. • Offline learning outperforms online in optimality and speed. • Proposing "Pre-Train and Fine-Tune" paradigm for better, robust control. [ABSTRACT FROM AUTHOR]

In this study, we developed a saturated super-twisting algorithm for autonomous underwater vehicles to address the trajectory tracking problem. On the basis of Lyapunov arguments, we performed a stability analysis of the resulting closed-loop system under the proposed controller, considering the MIMO system dynamics, parametric uncertainties, and external disturbances. Finally, we conducted several real-time experiments to demonstrate the effectiveness and robustness of the proposed control solution as compared to the traditional super-twisting algorithm. • A saturated super-twisting algorithm is proposed for autonomous underwater vehicles. • Lyapunov-based stability analysis of the resulting closed-loop system is performed. • Real-time experiments demonstrate the effectiveness and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

This article considers the problem of asynchronous attack tolerant control for a type of Markov jump cyber-physical systems (MJCPSs) suffering hybrid cyber-attacks. In the measurement channel, the Denial-of-Service (DoS) attack and deception attack are cogitated simultaneously. Meanwhile, the impact generated by the deficiency of plant mode information due to cyber-attacks is described via the partial unknown mode transition probability and the asynchronization between the plant and the controller. On these bases, a detected-mode-dependent asynchronous dynamic controller with a certain capacity to resist malevolent cyber-attacks is proposed. Then, to guarantee the stochastic stability and reach the designated H ∞ performance for MJCPSs under hybrid cyber-attacks, sufficient conditions are given by means of some decoupling technologies. Finally, the effectiveness of the presented control tactic is demonstrated, which is supported by an application on a linearized F-404 engine system. • A dynamic control strategy depending on compromised output is proposed. • Imperfect mode information is considered in the design process. • Conditions with less conservativeness are deduced. [ABSTRACT FROM AUTHOR]

In this paper, we propose a saturated fuzzy logic system (FLS) based robust tracking control method for a marine manipulator. To handle time-varying output constraints, we introduce a tangent barrier Lyapunov function (TBLF). Additionally, we employ a FLS to approximate the model uncertainties and unknown disturbances, ensuring no estimation drift through the use of a smooth projection function. To accurately simulate real operation scenarios, we consider state delays and input saturation. The state delay is addressed using a Lyapunov–Krasovskii functional technique. Through the developed control strategy, it is guaranteed that the tracking errors converge to the neighborhood of zero as the time approaches infinity, while respecting output constraints and input saturation. The effectiveness of the proposed controller is validated through extensive simulations. • A fuzzy logic system based controller is proposed for a marine manipulator • Model uncertainties, disturbances, constraints, and input saturation are considered • Projections are designed to ensure estimated weights remain bounded • Barrier Lyapunov functions are used to guarantee adherence to output constraints • Lyapunov-Krasovskii functions are employed to handle state delays [ABSTRACT FROM AUTHOR]

This paper investigates the bipartite output consensus tracking problem for disturbed agent networks where the agents have different state dimensions. A prescribed time robust coordination control scheme is proposed, which consists of a leader-state observer and an output-regulation-based robust controller that both converge within a preset time. Time-scaling functions with preset parameters T 1 and T 2 respectively take effect in the observer and the controller, and by computing the output regulation matrix which turns the output errors of followers tracking the heterogeneous leader into their state errors, the model-mismatched agents could reach bipartite output tracking consensus within a total duration of T 1 + T 2. Moreover, neural networks are deployed to estimate and eliminate the unknown disturbances. Thorough Lyapunov-based derivations approve the preset-time convergence and robustness for the controller. Numerical simulations are performed to verify the efficacy of the proposed control scheme. • The bipartite output consensus tracking control problem of heterogeneous multi- agent systems subject to unknown disturbances is addressed. • The integration of the distributed prescribed time observer and the prescribed time controller can ensure that the observation errors and tracking errors converge within the preset time. • Neural networks are deployed to estimate and eliminate unknown disturbances, and the communication topology is extended to a structural balance signed graph. • By applying the output regulation method in the controller, all the follower agents are able to track a model-mismatched leader. [ABSTRACT FROM AUTHOR]

[Display omitted] • Exothermic CSTR shows the unit intensification process by cascade feedback. • Cascade feedback provides robustness for unit intensification of chemical reactors. • Together robust temperature control and Lagrange-based feedback for intensification. • Controversy on unit intensification can be solved by robust and optimal feedback. • Minimum time stabilization with robust temperature control for unit intensification. Process intensification has recently opened challenges to enhance production, performance and yielding by distinct variables related to stable operation, process configuration, and economical and environmental concerns. Indeed, the process intensification offers scientific opportunities and challenges onto processes operation to achieve the indicators of the sustainable development. Here, the intensification problem is formulated as a cascade feedback interconnection, where the temperature is robustly stabilized and the minim-time stabilization of the product concentration is simultaneously tackled. The contribution deals with a CSTR whose convergence time for stable steady-state operation can be minimized, which promotes: (i) minimizing the transitive operation to reduce the yielding deviation and (ii) enhancing the production performance along larger time with a given reaction volume. In this sense, the contribution shows that unit intensification of chemical reactors is feasible when reconfiguration, optimal design or other intensification alternatives are unavailable. [ABSTRACT FROM AUTHOR]