Search

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]

This article presents a robust finite control set predictive scheme for a stand-alone squirrel cage induction generator (SCIG) drive. This technique is considered an alternative to the drive system due to the inclusion of system nonlinearities and fast dynamic response. The control objective in the distributed generation environment is to fix the output voltage to follow the stand-alone requirement. The strategy establishes optimized switching instants for cost function minimization for both source and load converter control and diminished cross-coupling amid active and reactive power during transient scenarios. The scheme is designed to achieve the minimal effect caused by the parameter uncertainties. During source and load changes, this work will also address the maintenance of dc-link voltage, machine, and load variables at the set value, supported by machine and load-end converter control to achieve stand-alone load objectives. In addition, the presented scheme is also tested with random variation of speed to check the efficacy of the control configuration. The drive performance is evaluated by simulation using MATLAB/Simulink environment. Comprehensive real-time findings obtained from a scaled laboratory test bench using dSPACE-1104 are provided to verify the feasibility of the predictive solution. • Distinct design approach of predictive control for SCIG based drive system feeding stand-alone load. • The developed generator side converter strategy controls the active power flow from the SCIG by regulating the dc-link voltage. • The performance of the control scheme is found to be much superior under load transients, and input power variation. • The uncertainty in the values of the machine parameters is also tested and system offer better dynamic performance. • Demonstration of MATLAB Simulation and, then validation with Real-Time results. [ABSTRACT FROM AUTHOR]

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H ∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism. [Display omitted] • A multi-event driven mechanism (MEDM) based joint non-fragile control scheme is derived. • The wind-integrated power system model based on MEDM is established. • The MEDM is integrated into the joint non-fragile controller design under DoS attacks. • The MEDM based joint non-fragile H ∞ automatic generation control strategy is proposed. • It provides stronger robustness and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a tube-based model predictive control strategy for linear systems with bounded disturbances and input delay to ensure input-to-state stability. Firstly, the actual disturbed system is decomposed into a nominal system without disturbances and an error system. For the nominal system, solving an optimization problem, where the delayed control input is set as an optimization variable, yields a nominal control law that enables the nominal state signal to approach to zero. Then, for the error system, the Razumikhin approach is used to identify a robust control invariant set. Using the set invariance theorem, an ancillary control law is developed to confine the error state signal in the invariant set. Combining the two results, we obtain a control law that enables the state signal to remain within a robustly invariant tube. Finally, the effectiveness of the developed control strategy is validated by simulations. • An ancillary control is used to ensure the state errors stay in the invariant set. • A constraint on the delayed input is added to ensure feasibility of the algorithm. • By introducing a state error term, a novel Lyapunov function is proposed. [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]

This study presents a novel hybrid control strategy for single-link flexible-joint robot manipulators, addressing inherent uncertainties and nonlinear dynamics. By integrating nonlinear reduced-order active disturbance rejection control (NRADRC) with backstepping control, the proposed method effectively estimates and mitigates nonlinear dynamics and external disturbances. Utilizing a nonlinear reduced-order extended state observer (NRESO) enhances resilience to internal and external uncertainties. The global stability of the proposed controller is rigorously established using the Lyapunov approach. Numerical comparisons with state-of-the-art nonlinear control methods demonstrate the superior efficiency and robustness of the proposed approach, especially under varying payloads and disturbances, advancing robotic control solutions. • Proposes a robust high-efficiency controller for single-link flexible-joint manipulators. • Innovates by integrating backstepping and Active Disturbance Rejection Control. • Features a reduced-order observer independent of the system's mathematical model. • Ensures global asymptotic stability with Lyapunov recursive methods. [ABSTRACT FROM AUTHOR]

The field of large numerical aperture microscopy has witnessed significant advancements in spatial and temporal resolution, as well as improvements in optical microscope imaging quality. However, these advancements have concurrently raised the demand for enhanced precision, extended range, and increased load-bearing capacity in objective motion carrier (OMC). To address this challenge, this study introduces an innovative OMC that employs a ball screw mechanism as its primary driving component. Furthermore, a robust nonlinear motion control strategy has been developed, which integrates fast nonsingular terminal sliding mode, experimental estimation techniques, and adaptive radial basis neural network, to mitigate the impact of nonlinear friction within the ball screw mechanism on motion precision. The stability of the closed-loop control system has been rigorously demonstrated through Lyapunov theory. Compared with other enhanced sliding mode control strategies, the maximum error and root mean square error of this controller are improved by 33% and 34% respectively. The implementation of the novel OMC has enabled the establishment of a high-resolution bio-optical microscope, which has proven its effectiveness in the microscopic imaging of retinal organoids. • Novel SMC Strategy: To address the limitations of traditional SMC, characterized by high gains and pronounced chattering, a novel SMC strategy is introduced. • Online Estimation and Compensation: The utilization of TDE techniques enables online estimation and real-time compensation of unknown terms. • Biophotonic Microscope Development: The research culminates in the development of a biophotonic microscope, wherein the objective motion carrier plays a crucial role in achieving focal plane movement. [ABSTRACT FROM AUTHOR]

This paper presents a two-loop control framework for robotic manipulator systems subject to state constraints and input saturation, which effectively integrates planning and control strategies. Namely, a stability controller is designed in the inner loop to address uncertainties and nonlinearities; an optimization-based generator is constructed in the outer loop to ensure that state and input constraints are obeyed while concurrently minimizing the convergence time. Furthermore, to dramatically the computational burden, the optimization-based generator in the outer loop is switched to a direct model-based generator when the tracking errors are sufficiently small. In this way, both a high tracking accuracy and fast dynamic response are obtained for constrained robotic manipulator systems with considerably lower computational burden. The superiority and effectiveness of the proposed structure are illustrated through comparative simulations and experiments. • A general and systematic constrained control scheme for robotic manipulators. • Nonlinearities, uncertainties, state and input constraints are considered. • Adaptive robust controller is designed for high tracking accuracy. • Optimization-based generator is designed for fast dynamic response. • Experiments on Franka robotic manipulator reveal the effectiveness. [ABSTRACT FROM AUTHOR]

This paper investigates the stabilization problem with a fixed-time approach for a flexible spacecraft subject to vibrations of flexible modes, unknown bounded disturbance, and inherent uncertainty. To estimate the modal variables of a flexible spacecraft which are often unmeasurable in practice, an observer with guaranteed fixed-time convergence is designed. Using the estimated modal variables, a fixed-time non-singular sliding mode controller is designed so that the desired attitude can be reached before a pre-specified time threshold regardless of the spacecraft's initial attitude. By incorporating the estimated modal variables in the control design, significant reduction in the steady-state error of the system response is achieved. The proposed control system is further enhanced with an adaptive law to increase robustness against unknown external disturbances and uncertainties. Stability analysis based on Lyapunov theory guarantees the convergence of observer estimation error and spacecraft attitude error to a pre-determined set before a fixed threshold. Simulation results validate the promising performance of the proposed control system, highlighting its effectiveness in achieving accurate and robust attitude control for flexible spacecraft. • A fixed-time observer with guaranteed convergence is designed. • A fixed-time non-singular sliding mode controller is developed to guide the spacecraft's attitude towards a desired target. • To further enhance the robustness of the control system, an adaptive law is integrated into the proposed controller. • The stability of the closed-loop system is rigorously analyzed using Lyapunov theory. [ABSTRACT FROM AUTHOR]

In this study, a linear quadratic integral differential game approach is applied to regulate and track the Euler angles for a quadrotor experimental platform using two players. One produces commands for each channel of the quadrotor and another generates the worst disturbance based on the mini-maximization of a quadratic criterion with integral action. For this purpose, first, the attitude dynamics of the platform are modeled and its parameters are identified based on the Nonlinear Least Squares Trust-Region Reflective method. The performance of the proposed controller is evaluated for regulation and tracking problems. The ability of the controller is also examined in the disturbance rejection. Moreover, the influence of uncertainty modeling is studied on the obtained results. Then, the performance of the proposed controller is compared with the classic Proportional Integral Derivative, Linear Quadratic Regulator, and Linear Quadratic Integral Regulator. The results demonstrate the effectiveness of the Game Theory on the Linear Quadratic Regulator approach when the input disturbance occurs. [Display omitted] • Implementation of a linear quadratic controller based on differential game theory. • Introducing an action integral to the controller to eliminate steady-state errors. • Proof of optimality of the proposed controller. • Evaluation of the controller's performance on a quadrotor experimental setup. • Comparison of the controller's performance with other robust control methods. [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]

The widespread use of wheeled mobile robots (WMRs) in many fields has created new challenges. A critical issue is wheel slip, which, if not accurately determined and controlled, causes instability and deviation from the robot's path. In this paper, an intelligent approach for estimating the longitudinal and lateral slip of wheels is proposed that can effectively compensate for the negative effects of slippage. The proposed algorithm relies on three regression networks to estimate the longitudinal slip ratio of the right and left wheels and sideslip angle on terrains with different friction coefficients. The datasets collected during tests on different surfaces with various maneuvers are used to train the artificial neural networks (ANNs). A developed dynamic model of a WMR considering wheel slip and modified traction force is presented. The adaptive robust controller, based on sliding mode control (SMC), is introduced to deal with the problems related to slipping, unknown uncertainties, and disturbances. The simulation results demonstrate that the presented controller has better performance than SMC in handling external disturbances and uncertainties, which leads to reduction in tracking error and faster convergence to zero. The proposed controller with an intelligent slip estimator, has been applied to a four-wheel mobile robot to demonstrate its effectiveness and feasibility. The high accuracy of slip estimation in the mentioned intelligent algorithm has resulted in the presented method being on average 26% more effective in reducing the tracking error than the control method without slip compensation in each test for circular trajectory. • Developing a dynamic model of WMR using modified traction force. • Proposing slip estimator based on an intelligent learning for both longitudinal and lateral slip. • Eliminating the time delay in slip estimation and compensating for its effect in the same time step by predicting the slip amount. • Improved robot performance in trajectory tracking on slippery surfaces. [ABSTRACT FROM AUTHOR]

To address the trajectory tracking issue of upper-limb rehabilitation exoskeleton with uncertainties and external disturbances, this paper proposes a fractional-order ultra-local model -based model-free finite-time robust controller (FO-FTRC) using predefined performance sliding surface. Different from previous model-free control strategies, a novel multi-input multi-output (MIMO) fractional-order ultra-local model which is a virtual model is proposed to approximate the complex uncertain nonlinear exoskeleton dynamics in a short sliding time window. This allows the design of controller to be independent of any exoskeleton model information and reduces the difficulty of controller design. The developed robust model-free control method incorporates a fractional-order quasi-time delay estimator (FO-QTDE), unknown disturbance estimator (UDE) as well as prescribed performance sliding mode control (PPSMC). The FO-QTDE is utilized to estimate the unknown lumped uncertainties which employs short time delayed knowledge only about the control input. However, the low-pass filter is always added for FO-QTDE when disturbances change fast, which leads to unavoidable estimation error. Then, UDE is designed to further eliminate the estimation error of FO-QTDE to enhance control performance. The PPSMC is constructed to converge sliding surface to zero in a finite time. Besides, the sliding surface is always limited in performance boundaries. After that, the overall system stability and convergence analyses are demonstrated by using the Lyapunov theorem. Finally, with the comparison to other methods of α -variable adaptive model free control (α -AMFC), time-delay estimation-based continuous nonsingular fast terminal sliding mode controller (TDE-CNFTSMC), time delay estimation (TDE)-based model-free fractional-order nonsingular fast terminal sliding mode control (MFF-TSM) and fractional-order proportion–differential (PD β), the co-simulation results on 7-degree-of-freedom (DOF) iReHave upper-limb exoskeleton virtual prototype and experiment results on 2-DOF upper-limb exoskeleton are obtained to illustrate the effectiveness and superiority of the proposed FO-FTRC method. • A fractional-order ULM is proposed to approximate complex exoskeleton to achieve MFC. • A QTDE and disturbance estimator is designed to eliminate the lumped uncertainties. • The paper achieves the FT stabilization under fractional-order ULM design framework. • The co-simulation experiments on 7-DOF iReHave exoskeleton and experiment on 2-DOF. [ABSTRACT FROM AUTHOR]

The issues faced by hybrid electric vehicles (HEVs) include locating and managing free energy to preserve resource dynamics and constraints while preserving prolonged autonomy. This study assessed a hybrid electric vehicle (HEV) equipped with a fuel cell (FC), battery, direct current generators (DCGs), and supercapacitor (SC) to meet the power needs of an automobile utilizing variable power converters. This study examines four HEV energy management strategies (EMSs), increasing clean environmental power penetration by utilizing the HEV's kinetic energy, as a new contribution. Strategies for Proportional-Integral (PI), State-Machine (SM), Artificial Neural Network (ANN), and Adaptive Neural Fuzzy Inference System (ANFIS) EMSs are discussed. In addition to implementing direct torque control with a space vector modulation-based ANFIS controller (ANFIS-DTC-SVM), this study proposes to insert DCGs in the front wheels of HEVs for free energy production. Simulations of EMSs yielded approximative findings, achieving a 22.2 (%) free-exploited kinetic energy. The ANN-based EMS surpassed the competition, yielding the highest energy efficiency 98.2 (%) and the lowest fuel consumption 48.68 (SI). As a result of maximizing battery utilization and limiting fuel consumption, the examined HEV's dependability and stability were confirmed and reached, highlighting the importance of kinetic energy. [Display omitted] • Creation of new energy without pollution. • The exploitation of EV's kinetic energy to produce the power.‎ • Applying different energy management strategies.‎ • Configuration and control of the two-wheel-drive electric vehicle.‎ • Improvement of energy efficiency and fuel consumption. [ABSTRACT FROM AUTHOR]

For most nanopositioning systems, maximizing positioning bandwidth to accurately track periodic and aperiodic reference signals is the primary performance goal. Closed-loop control schemes are employed to overcome the inherent performance limitations such as mechanical resonance, hysteresis and creep. Most reported control schemes are integer-order and combine both damping and tracking actions. In this work, fractional-order controllers from the positive position feedback family namely: the Fractional-Order Integral Resonant Control (FOIRC), the Fractional-Order Positive Position Feedback (FOPPF) controller, the Fractional-Order Positive Velocity and Position Feedback (FOPVPF) controller and the Fractional-Order Positive, Acceleration, Velocity and Position Feedback (FOPAVPF) controller are designed and analysed. Compared with their classical integer-order implementation, the fractional-order damping and tracking controllers furnish additional design (tuning) parameters, facilitating superior closed-loop bandwidth and tracking accuracy. Detailed simulated experiments are performed on recorded frequency-response data to validate the efficacy, stability and robustness of the proposed control schemes. The results show that the fractional-order versions deliver the best overall performance. • Fractional-order control improves nanopositioning systems, enhancing bandwidth and accuracy. • These controllers offer more parameters, boosting achievable bandwidth and accuracy. • Simulations confirm efficacy, stability, and robustness, even with unmodelled effects. • Achievable bandwidth increases by up to 17%, accuracy improves by 35%. • Fractional-order control allows for significant improvements in nanopositioning systems. [ABSTRACT FROM AUTHOR]

Path-tracking and lane-keeping tasks are critical to guarantee safety and navigation performance considerations for deploying autonomous cars. This paper presents a novel control framework for the path-tracking control of high-speed autonomous cars with structured uncertainties. This study introduces a nonlinear adaptive control system based on a fractional-order terminal sliding mode system while incorporating a novel Gaussian Nonsingleton type-3 fuzzy system (FOTSM-NT3FS). Therefore, the proposed controller is independent of the information about the ego vehicle's dynamic information, and instead, the dynamics are approximated through a developed NT3FLS. The developed control system exhibits robustness to measurement errors and faulty sensors, because the inputs to the NT3FS are uncertain. In order to guarantee the boundedness of the adaptation parameters, the σ − mod approach is employed. The Lyapunov stability theorem and Barbalat's lemma are used to ensure the uniform ultimate boundedness of the closed loop system and the convergence of tracking errors to the origin in finite time. High-fidelity co-simulations with CarSim and MATLAB are performed to verify the effectiveness of the proposed control scheme and are also compared to other reported methods in the literature. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path-tracking tasks and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances. • Robust path tracking for uncertain-input cars, ensuring reliability despite errors. • Adaptive control with fuzzy integration for high-speed autonomy, precise maneuvering. • Stable navigation using Gaussian Nonsingleton type-3 fuzzy system, smooth tracking. • Efficient path tracking under uncertainties, outperforming existing methods. [ABSTRACT FROM AUTHOR]

The uncertainty in mobile robot greatly affects control accuracy. This makes it difficult to apply to more rigorous high-precision engineering fields. Therefore, the fuzzy set theory is introduced to describe the uncertainty. Based on that, the fuzzy mobile robot system is established. The virtual speed controller using backstepping method is designed. Then, a robust control method is proposed to guarantee the uniform boundedness and uniform ultimate boundedness of the controlled system. Furthermore, the balance optimization problem of the performance and cost of the controlled system is explored. By minimizing the performance index containing fuzzy numbers, the optimal control parameter is obtained. Compared with the linear quadratic regulator algorithm, which is the representative optimal robust controller, the proposed control method and optimization strategy based on fuzzy set theory are verified to be effective. The control accuracy is further improved. • The kinematic and dynamic models of the mobile robot are established. • A virtual speed controller is designed by using the backstepping method. • A deterministic robust control is proposed to ensure the system performance. • A fuzzy performance index is introduced for optimizing parameter. [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]

A novel robust controller is designed for Electric Helicopter Tail Deceleration (EHTD) system which is subject to uncertainties and nonlinearity. Then, the optimal parameters of the control are solved based on Stackelberg game theory. The fuzzy set theory is used to describe the uncertainty bound, which is distinguished from probability theory or fuzzy logic theory. Based on two-player (leader and follower) Stackelberg game, two performance indexes are proposed for novel robust controller which contains two optimal parameters. The system performance and control cost are included in performance indexes. Then, based on Stackelberg strategy, the optimal decisions are made by leader and follower, which can minimize the performance indexes. Therefore, by numerical simulation, the EHTD system is applied to prove that the design of robust control and optimal parameters based Stackelberg strategy are effective. • A novel robust controller is designed for Electric Helicopter Tail Deceleration system which is subject to uncertainties. • The novel robust controller contains two adjustable control parameters. • Based on Stackelberg game theory, the optimal decision can be chosen by players (leader and follower). [ABSTRACT FROM AUTHOR]

An air–ground heterogeneous unmanned swarm system coordination is considered. The system consists of N unmanned aerial vehicles (UAVs) and one unmanned ground vehicle (UGV). This forms a complicated mission, which consists of the following four different tasks. First, the aerial vehicles are in a compact formation, while avoiding collision with each other. Second, the aerial vehicles should stay close to the ground, while avoiding collision with the ground. Third, the aerial vehicles should stay close to the ground vehicle. Fourth, the ground vehicle should follow a desired trajectory. These tasks reflect two seemingly contradictory nature: close to (due to tracking) and away from (due to avoidance). The effective control design should address all four tasks even in the presence of uncertainty. By two creative transformations, this multitude of tasks are consolidated in a χ -measure. An adaptive robust control, which includes a robust control scheme and an online adaptation law, is then proposed to render guarantee boundedness performance of this χ -measure. As a result, the control design is able to accomplish the combined tracking-avoidance mission for the uncertain swarm system. Despite the presence of conflicting aspects between these tasks, the designed controller exhibits outstanding performance. • A multitasking control framework is proposed for air-ground coordinated systems. • Two contradictory tasks of tracking and avoidance are solved by one control method. • An adaptive robust control is designed for the systems with uncertainty. • Multiple performances of the systems are realized by one controller. [ABSTRACT FROM AUTHOR]

Successive approximation techniques are effective approaches to solve the Hamilton–Jacobi–Bellman (HJB)/Hamilton–Jacobi–Isaacs (HJI) equations in nonlinear H 2 and H ∞ optimal control problems (OCPs), but residual errors in the solving process may destroy its convergence property, and related numerical methods also pose computational burden and difficulties. In this paper, the HJB/HJI partial differential equations (PDEs) for infinite-horizon nonlinear H 2 and H ∞ OCPs are handled in a unified formulation, and a sparse successive approximation method is proposed. Taking advantage of successive approximation techniques, the nonlinear HJB/HJI PDEs are transformed into sequences of easily solvable linear PDEs, to which the solutions can be computed point-wise by handling simple initial value problems. Extra constraints are also incorporated in the solving process to guarantee the convergence under residual errors. The sparse grid based collocation points and basis functions are then employed to enable efficient numerical implementation. The performance of the proposed method is also numerically demonstrated in simulations. • The proposed method handles nonlinear H 2 and H ∞ problems with a unified formulation. • Residual errors are explicitly considered in the designed numerical scheme. • The scale of the resultant quadratic programming problem only grows polynomially. [ABSTRACT FROM AUTHOR]

This paper proposes a method for high-performance motion control of the dual-valve hydraulic system subject to parameter and model uncertainties, unknown proportional valve dead-zone, and servo valve fault. By constructing a detailed dual-valve fault system model (DFSM), a disturbance observer-based adaptive robust fault-tolerant controller is proposed via the backstepping method. This controller integrates a model-based fault detection algorithm for real-time fault monitoring and subsequent controller reconfiguration. Additionally, the DFSM-based adaptive robust control (ARC) technique is applied to handle the unknown dead-zone problem and other nonlinearities, ensuring precise control. Once the servo valve fault occurs, a nonlinear observer estimates the fault and collaborates with the ARC to establish a reconfigured controller, thereby maintaining motion control. The effectiveness of the proposed method has been experimentally verified. • The novel fault-tolerant control algorithm for dual-valve hydraulic system is investigated. • The novel dual-valve faults system model is proposed. • The model-based fault detection algorithm and nonlinear disturbance observer are established. • The advanced adaptive robust fault-tolerant controller is designed. [ABSTRACT FROM AUTHOR]

Mechanical systems subject to constraints play a essential role in the field of control engineering, profoundly influencing the design and performance of control strategies. Consequently, there is a compelling need to explore diverse control methods to effectively tackle the complex task of stabilizing nonlinear systems while ensuring the constraints are not violated. In this context, this paper proposes a design procedure for position-constrained controllers in robot manipulators. The solution relies on the construction of a diffeomorphism (a differentiable coordinate transformation) that maps all the trajectories of the constrained dynamics into an unconstrained one. The controller design is carried out in the unconstrained dynamics without dealing directly with the constraints. The proposed family of controllers employ an explicit control law which circumvents the need for additional time-consuming computation for feasibility and/or optimization. Moreover, the proposed controller is parametrized by a class of diffeomorphisms which can be selected by the designer. Exponential stability in constrained and unconstrained position states is achieved, in the certain case. For the uncertain case, the controller is augmented through sliding modes guaranteeing finite-time convergence towards the manifold and keeping the exponential convergence within the manifold dynamics. The approach is validated through experiments in an actual 2 DOF lightweight robot manipulator. • A diffeomorphism-based control law for MIMO constrained nonlinear systems. • The explicit controller ensures (local) exponential convergence of the all states. • A robustification of the controller to deal with uncertainties via sliding modes. [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]

This paper proposes a fixed-time tracking control for robot manipulators in the presence of parametric uncertainties and disturbances. An auxiliary function is first proposed for constructing a fixed-time sliding manifold. Benefited from this fixed-time sliding manifold, a singularity-free robust control is proposed to evade the effects of algebraic loop problem of the commonly-used sliding mode controls (SMC). The key advantages of the proposed approach are: (i) exact fixed-time stability featuring the convergence time does not relate to the initial conditions and is acquired in advance; (ii) the singularity and algebraic loop problems are eliminated completely; (iii) a simple and intuitive control structure is used for easy implementation of trajectory tracking control for uncertain robot manipulators with faster transient and higher steady-state precision. Simulations and experimental comparisons validate the improved tracking performance of the proposed approach. • Propose a non-singular terminal sliding mode control with fixed-time convergence. • Prove exact fixed-time tracking stability. • Features include exact fixed-time stability with simple structure. • Demonstrate the improved performance. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots (WMRs) have a wide range of applications in logistics transportation and industrial productions, among which the motion control has always been one of the hot spots in the current WMR researches. However, most of previous designed controllers assumed that the WMR motion had no slippage. Ignoring the slippage factors usually results in a decrease in control performance and even leads to unstable motion. To address such a challenge, a kinematic model with differential flatness is established through dynamic feedback-linearization, which comprehensively considers the multidirectional slippage of mobile robot, including longitudinal and steering slippage. Subsequently, benefited from the one-to-one mapping of states and inputs to flat outputs in differential flat system, an adaptive robust control (ARC) method is proposed to stabilize the system. Different from previous robust control studies, even if the knowledge of the upper bound of system uncertainties is unknown in advance, the proposed adaptive robust controller can still achieve satisfying performance by adaptive estimation of the upper bound of system uncertainties. The effectiveness and feasibility of the proposed method are confirmed by comparative experiments on WMR with slippage disturbance. [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]

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 study focuses on developing a continuous differential neural network (DNN) approximating min–max robust control by applying dynamic neural programming. The suggested controller is applied to a class of nonlinear perturbed systems providing satisfactory dynamics for a given cost function depending on both the trajectories of the perturbed system and the designed constrained control actions. The min–max formulation for dynamic programming offers reliable control for restricted modeling uncertainties and perturbations. The suggested design considers control norm restrictions in the optimization problem. DNN's approximation of the worst (with respect to the admissible class of perturbations and uncertainties) value function of the Hamilton–Jacobi–Bellman (HJB) equation enables the estimation of the closed-loop formulation of the controller. The robust version of the HJB partial differential equation is studied to create the learning law class for the time-varying weights in the DNN. The controller employs a time-varying Lyapunov-like differential equation and the solution of the corresponding learning laws. A recurrent algorithm based on the Kiefer–Wolfowitz technique can be used by modifying the weights' initial conditions to fulfill the specified cost function's end requirements. A numerical example tests the robust control proposed in this study, validating the robust optimal solution based on the DNN approximation for Bellman's value function. • A robust optimal control is developed using approximate neural dynamic programming. • Robust optimal control is solved for nonlinear systems with restricted control actions. • A differential neural network is used to approximate Belman's function for Hamilton–Jacobi–Bellman equation. • The learning laws are derived based on the Hamilton–Jacobi–Bellman equation. [ABSTRACT FROM AUTHOR]