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This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller's ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes. [ABSTRACT FROM AUTHOR]

Global, 24/7, and all-weather Synthetic Aperture Radars (SARs) are optimal platforms for Ground Moving Target Indication (GMTI) missions, and the tether constraint provides a stable mechanic connection for such configurations. To fulfill the requirements of such missions, the Space Tether System (STS) must be deployed to horizontal positions to form the necessary along-track interference baseline, which is unstable relative to traditional vertical positions and has not received adequate focus. To deal with this problem, this study focuses on the deployment control of the STS to the unstable horizontal positions. Firstly, the properties of the STS at the horizontal position are analyzed, and a synthetic criterion of measurement error is defined based on the observation principle of the GMTI mission. Secondly, two deployment control strategies are proposed, and corresponding desired trajectories are generated by considering two occasions respectively. In the end, considering the instability of horizontal positions, an adaptive closed-loop controller is designed utilizing the backstepping method to address gravitational moment and other disturbances. Simulations demonstrate that the system can successfully attain the desired horizontal positions under both deployment strategies, and the designed controller can quickly track trajectories under initial state errors and external disturbances. • Two along-track deployment strategies are proposed for unstable horizontal positions. • A synthetic criterion of measurement error is defined to evaluate deployment accuracy. • A back-stepping adaptive controller is designed to suppress unstable tether oscillations. [ABSTRACT FROM AUTHOR]

The low-load efficiency performance of alkaline water electrolyzers (AWEs) is poor so its operation range is limited. It is unfeasible for AWEs to follow the fluctuant renewable energy sources (RESs). Besides, the power fluctuation will lead to the electrolyzer temperature variation. The efficiency performance is negatively influenced. Focusing on these problems, an efficiency enhancement control strategy for AWEs operating at varying temperature is introduced in this paper. First, the efficiency model of AWEs is established. Then, a multi-mode adaptive control strategy is proposed to improve the hydrogen production efficiency of AWEs. Based on the U–I curves of AWEs, parameter identification method is used to obtain the key parameters of proposed efficiency model. Finally, the feasibility of the control strategy is experimentally validated on a 10 kW AWE. The experimental outcome shows that by applying the proposed method, the low-load hydrogen production efficiency of AWEs can be improved at different temperatures. At 15% rated load, the electrolyzer efficiency increases from 21.54% to 42.73% at 80 °C. The operation range of the electrolyzer can be extended from 30% to 100%–21%∼100% of rated load at 80 °C. © 2024 The Authors. Published by Elsevier Ltd. • The efficiency model of alkaline electrolyzers is established. • The efficiency of alkaline electrolyzers under varying temperatures is enhanced. • The proposed method is experimentally verified on a 10 kW alkaline electrolyzer. [ABSTRACT FROM AUTHOR]

• The dynamic model for VCRS based on the Switched Moving-Boundary method is built. • The impact of environmental disturbances on the refrigeration performance is studied. • A hybrid paradigm combining online identification and adaptive control is proposed. • The overshoot performance is optimized at 10–4 magnitude in uncertain environments. • Adaptive control's IAE by 2–3 orders of magnitude compared to traditional control. Vapor compression refrigeration systems (VCRS) occupy a crucial position in modern society and in the field of thermal sciences. However, the operation of VCRS is subjected to both external disturbances (dynamic-changing environment) and inherent system characteristics (coupling or nonlinear features), leading to issues like reduced refrigeration efficiency and significant fluctuations in cooling capacity. To address these challenges and enhance the adaptability of VCRS in dynamically changing environments, this study establishes a dynamic simulation model for VCRS based on the Switched Moving-Boundary method. The impact of external environmental disturbances on refrigeration performance is investigated, and continuous online identification methods are employed to elucidate its internal coupling characteristics and nonlinear features. The adaptive temperature control method is introduced, benefiting from the developed recursive least squares method with a forgetting factor for online identification, achieving precise model identification which facilities the real-time parameters tuning of adaptive controller. The results indicate that the hybrid paradigm of online identification and adaptive control algorithm not only effectively handles various disturbances but also reduces overshoot and IAE by 2–3 orders of magnitude compared to traditional PID controllers. Adaptive PID control maintains overshoot in the 10–4 order of magnitude and IAE in the 10–5 order of magnitude. [Display omitted] [ABSTRACT FROM AUTHOR]

This work presents the development of a multilevel electromagnetic actuation system that controls the shape of a flexible rotatory robotic structure. An array of electromagnets is used as the set of actuators that regulate the position of permanent magnets within the flexible device. The primary outcome of this study is the design and experimental validation of the multilevel rotating device. In addition, the theoretical description of the system motion under electromagnetic actuation is formulated using Euler–Lagrange and electromagnetic theories. Given the developed model, a theoretical study leads to designing an adaptive control that considers motion restrictions in the flexible device. The controller aims to modify the current applied to the electromagnets, which changes the interaction forces between the electromagnet and the permanent magnets in the robotic flexible structure. A set of numerical simulations confirms the proposed controller's effectiveness compared to the traditional state feedback approach that does not consider the state restrictions, which is implemented in devices that also operate under an electromagnetic approach. Furthermore, an experimental version of the flexible device allows for testing of the developed controller. The experimental results show the suitability of the proposed control to generate non-oscillatory controlled motion during the regulation of the flexible mechanic device shape. • The adaptive control of a rotating electromagnetic system is developed considering state restrictions. • State restrictions and electromagnetic interference are considered in the control design. • A barrier Lyapunov function is used to derive the adaptive gains for the control action. • The proposed controller is evaluated in an experimental platform. [ABSTRACT FROM AUTHOR]

The hybrid reluctance actuator (HRA) has achieved widespread application in scanning motion tasks. However, the nonlinear perturbations arising from position-dependent stiffness fluctuations, hysteresis, eddy, and flux leakage can significantly affect the control performance. To enhance the control performance of HRA-based systems in scanning motion, this paper introduces an adaptive feedforward method, known as the Chua operator-based Kalman feedforward compensator (COKFC), which aims to mitigate these nonlinear perturbations, with a PID controller serving as the central control element. In the COKFC approach, a Chua operator is employed to effectively capture the inverse hysteresis behavior. A Chua-based time-varying feedforward compensation model is then formulated to represent the inversion of the nonlinear perturbations inherent in the HRA. An improved Kalman filter is utilized for the real-time adaptation of the time-varying parameters within the feedforward compensation model. The design procedure for this control strategy is presented. Experimental evaluations are conducted on an HRA-based stage (HRA-BS), and comparisons are made between the proposed method and several advanced control methods. The experimental results demonstrate that the proposed COKFC method exhibits superior control performance for the scanning motion of the HRA-BS, highlighting its effectiveness in practical applications. [Display omitted] • A simplified Chua operator was created to capture inverse hysteresis. • A Chua operator-based feedforward compensation model was constructed for HRA. • A COKFC method was proposed for the adaptive feedforward control of HRA-BS. • Comparative experiments on HRA-BS are conducted to validate the method. [ABSTRACT FROM AUTHOR]

The task-space distributed adaptive neural network (NN) fixed-time tracking problem is studied for networked heterogeneous robotic systems (NHRSs). In order to address this complex problem, we propose a NN-based fixed-time hierarchical control approach that transforms the problem into two sub-problems: a distributed fixed-time estimation problem and a local fixed-time tracking problem, respectively. Specifically, distributed estimators are constructed so that each follower can acquire the dynamic leader's state in a fixed time. Then, the neural networks (NNs) are employed to approximate the compounded uncertainty consisting of the unknown dynamics of robotic systems and the boundary of the compounded disturbance. More importantly, to guarantee that the tracking errors can converge into a small neighborhood of equilibrium in a fixed time independent of the initial state, the adaptive neural fixed-time local tracking controller is proposed. Another merit of the proposed controller is that the approximation errors are addressed in a novel way, eliminating the need for prior precise knowledge of uncertainties and improving the robustness and convergence speed of unknown robotic systems. Finally, the experimental results demonstrate the effectiveness and advantages of the proposed control method. • Neural networks-based adaptive fixed-time control algorithm has been proposed to solve the task space tracking problem. • The adaptive learning algorithm is used to deal with the compounded disturbance. • Non-redundant and redundant robots are taken into consideration in task space. • Both the fixed-time convergence and uniform ultimate boundedness of tracking errors was guaranteed. [ABSTRACT FROM AUTHOR]

To reduce the cross-regulation and improve the dynamic response performance of a single-inductor double-output Boost converter, an adaptive backstepping sliding mode control (ABSMC) strategy is proposed in this paper. The nonlinear mathematical model of the converter is established, an output function satisfying the exact feedback linearization (EFL) is constructed based on the inverse system theory, and the linearization and decoupling of the model are implemented. Meanwhile, the problem of EFL heavily relying on an exact model is solved by combining backstepping control with sliding mode control. Furthermore, an adaptive reaching law is proposed to adjust the gain of the switching function, and the chattering phenomenon of sliding mode control is reduced. The stability of the system is proven according to the Lyapunov stability theorem. Finally, compared with the existing control methods, both the simulation results and experimental results verify the effectiveness and superiority of the proposed ABSMC strategy. • An adaptive reaching law is proposed, which improves the response speed and reduces the chattering phenomenon. • The advantages of EFL technology, backstepping sliding mode control method, and adaptive control approach are combined, which suppresses the cross-regulation between the output branches and enhances robustness. • The proposed ABSMC strategy is easy to implement and can be applied to other SIMO DC-DC converters. [ABSTRACT FROM AUTHOR]

This article investigates an adaptive dynamic programming-based online compensation hierarchical sliding-mode control problem for a class of partially unknown switched nonlinear systems with actuator failures and uncertain perturbations under an identifier-critic neural networks architecture. Firstly, by introducing a cost function related to hierarchical sliding-mode surfaces for the nominal system, the original control problem is equivalently converted into an optimal control problem. To obtain this optimal control policy, the Hamilton–Jacobi–Bellman equation is solved through an adaptive dynamic programming method. Compared with conventional adaptive dynamic programming methods, the identifier-critic network architecture not only overcomes the limitation on the unknown internal dynamic but also eliminates the approximation error arising from the actor network. The weights in the critic network are tuned via the gradient descent approach and the experience replay technology, such that the persistence of excitation condition can be relaxed. Then, a compensation term containing hierarchical sliding-mode surfaces is used to offset uncertain actuator failures without the fault detection and isolation unit. Based on the Lyapunov stability theory, all states of the closed-loop nonlinear system are stable in the sense of uniformly ultimately boundedness. Finally, numerical and practical examples are given to demonstrate the effectiveness of our presented online compensation control strategy. • An HSS-based online compensation control method is firstly presented for partially unknown switched nonlinear systems with actuator failures and uncertain perturbations. • The designed method considerably reduces the conservativeness arising from the unknown internal dynamics of switched nonlinear systems. • By utilizing the HSS technique, our proposed method has a faster response speed and stronger robustness, and is insensitive to unknown actuator failures. [ABSTRACT FROM AUTHOR]

This paper provides an adaptive impedance control strategy about docking robot, a locking mechanism scheme based on the Stewart platform developing for the problem of excessive collision contact force caused by external environmental interference during autonomous docking tasks of ground unmanned vehicles. First, the docking robot system was introduced, and an inverse kinematics model of the docking robot was constructed. Next, to solve the problem of excessive collision contact force during docking, we have designed an adaptive impedance control algorithm, which includes a steady-state error model of contact force, an adaptive compensation controller design, and system stability analysis, thus achieving active compliance control. Finally, some simulations and experiments were conducted on the docking robot. Compared with traditional impedance control, adaptive impedance control reduces docking collision contact force and achieves compliant control. In the future, the experimental results provide a new docking approach for autonomous docking of unmanned vehicles, and also serve as a reference for the development of intelligent vehicles. • A docking robot scheme based on the Stewart platform is developed for dynamic docking of UVG. • An adaptive impedance control strategy is proposed to improve adaptability to the environment. • The effectiveness of the control strategy has been verified through simulation and experimentation. [ABSTRACT FROM AUTHOR]

• Examined the impact of Level 2 automation systems on driving behavior using naturalistic driving data. • Utilized association rules mining on the collected data. • Factors such as eyes-off road time, off-road glance frequency and time were considered. • Different road conditions and intersection types significantly affected driver behavior, with varied patterns emerging based on L2 system activation status. Driver distraction and reduced situational awareness pose significant risks in vehicles with Level 2 (L2) automation systems, such as adaptive cruise control and lane-keeping assistance. This study analyzed naturalistic driving data using Association Rules Mining (ARM) to investigate the impact of L2 automation on driver behavior. The dataset included 771 driving events categorized by L2 system activation status (active or inactive), intersection types, and hand positions on the steering wheel. Key variables were analyzed, such as eyes-off-road (EOR) time, off-road glance frequency and duration, and the influence of different driving conditions. The findings revealed that driver distraction, indicated by longer EOR times and more frequent off-road glances, is significantly higher when L2 systems are active. Additionally, drivers exhibit the highest levels of inattention with no hands on the wheel during L2 activation. These insights highlighted the need for improved driver-system interfaces. They targeted driver education to enhance the safety and effectiveness of L2 automation, ultimately contributing to safer roadways and better-informed policy decisions. [ABSTRACT FROM AUTHOR]

• With a survey, we investigated factors that predict trust in ADAS for current users. • Subjective knowledge, but not objective knowledge of limitations, predicted trust. • Objective and subjective knowledge were not correlated. • Propensity to trust technology in general also predicted trust in ADAS. Trust has been shown to influence whether drivers use advanced driver assistance systems (ADAS) appropriately, and thus understanding the factors influencing trust in ADAS may help inform interventions to support appropriate use. We surveyed 369 drivers to investigate the factors that predict trust in ADAS for current users. Participants were required to have experience using ADAS, specifically systems that simultaneously control longitudinal and lateral movement of the vehicle (participants reported using adaptive cruise control and lane keeping assist systems at the same time in their vehicle at least 1–4 times per month). In addition to assessing trust, the survey included questions to assess objective knowledge about ADAS limitations, self-reported understanding of ADAS (i.e., how correct and complete drivers thought their understanding of ADAS was), number of methods they had previously used to learn about ADAS, frequency of ADAS use, familiarity with technology, propensity to trust technology, and demographics. Regression results showed that self-reported understanding, but not objective knowledge, predicted trust in ADAS, with higher self-reported understanding being associated with higher trust. Self-reported understanding was not correlated with objective knowledge; participants rated their self-reported understanding highly, but only identified an average of 42% of the system limitations included in the survey. Propensity to trust technology was also a significant predictor of trust in ADAS, with higher propensity to trust technology in general associated with higher trust in ADAS. These findings suggest that interventions aimed at supporting appropriate trust in ADAS could be designed to increase drivers' awareness of potential gaps in their understanding and adjust expectations of ADAS for those with a high propensity to trust technology. [ABSTRACT FROM AUTHOR]

Series elastic actuator (SEA) technology is promising for the development of compliant robotic joints. Despite advancements in the realization of precise tracking, challenges persist in controlling the vibration and transient performance. This study enhanced the resonance ratio control (RRC) algorithm by integrating it with the L1 adaptive control (L1AC) method to address overshoot, static error, and vibration in SEA position control. Initially, the resonance between the motor and link sides caused by the elastic transmission structure was analyzed, which can result in overshoots and vibrations that affect the transient performance of the SEA control. Subsequently, a control scheme based on L1AC was introduced to enhance the performance. The stability of the proposed algorithm was demonstrated through a comprehensive exploration of key control parameters. Furthermore, the algorithm was augmented with gravity compensation, effectively reducing the predicted and reference errors. Consequently, the transient performance was improved. The efficacy of this enhanced algorithm was validated through simulations and experimental platforms, and comparisons with the RRC and model reference adaptive control algorithms. In all the experiments, the overshoot did not exceed 1.1%, the maximum jitter amplitude on the link side was within 0.2° , and a larger time constant in the controller could effectively eliminate the overshoot and vibration with a small response time delay. Furthermore, the algorithm exhibited a protective response during link side collisions by moderating link velocity and limiting motor current, to safeguard the contact environment, humans, and the SEA itself, which take advantage of the L1AC's low-pass filter (LPF) properties in disturbance handling. [ABSTRACT FROM AUTHOR]

Addressing the requirements and challenges of traffic light control, a reinforcement learning based adaptive optimal control model for traffic lights in intelligent transportation systems is proposed. In the model design, we combined Markov decision process, Q-learning algorithm, and Deep Q-Learning Network (DQN) control theory to establish a comprehensive signal light Adaptive Optimal Control of Signal Lights in Intelligent Transportation Systems (AOCITL) control model. Through simulation experiments on the model and the application of actual road scene data, we have verified the superiority of the model in improving traffic system efficiency and reducing traffic pressure. The experimental results show that compared with traditional fixed cycle signal light control, the adaptive optimal control model based on reinforcement learning can significantly improve the traffic efficiency of roads, reduce the incidence of traffic accidents, and enhance the overall operational effectiveness of urban transportation systems. The proposed method is possible to further optimize the model algorithm, expand its application scope, and promote the development and practical application of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

The adaptive versions of charting tools are well-established statistical monitoring techniques for detecting unknown changes in the process over a range of shifts. A re-weighted adaptive CUSUM mean (RACUSUM) charting scheme has been proposed in this study for monitoring the range of shifts. The proposed RACUSUM statistic estimates the unknown changes in the ongoing process using an EWMA statistic by plugging in an unbiased shift estimator and choosing a suitable smoothing constant according to the estimated shift to update the slack value of the CUSUM statistic. By employing Monte Carlo simulation using the zero- and steady-state processes, the proposal's run-length (RL) profiles have been calculated. The proposed RACUSUM chart shows more sensitive behavior under the shift delay process than in the case of zero-state. A comparative analysis between the proposal and competitors has been presented using average RL, extra quadratic loss (EQL), and the relative mean index (RMI) as metrics for 1-sided and 2-sided charting schemes. The robustness of the proposed 2-sided RACUSUM chart against non-normality has also been reported. The proposal's applicability has been demonstrated using two cases, one from the artificial and the other from a dataset of piston ring manufacturing industrial process. [Display omitted] • A new re-weighted adaptive CUSUM (RACUSUM) mean chart is designed to monitor shifts in the interval. • The proposal RACUSUM statistic estimates the unknown shift in the process mean. • The run-length (RL) profiles of the 1-sided and 2-sided proposed RACUSUM charts are evaluated using Monte Carlo simulation. • The extra quadratic loss (EQL) and relative mean index (RMI) metrics have been selected for the shifts in the interval. • The designed structure is applied to real piston ring industrial process data. [ABSTRACT FROM AUTHOR]

The stability, reliability, and lifespan of proton exchange membrane fuel cells (PEMFCs) are closely related to power dynamic response. Therefore, it is necessary to study in depth the relationship between the power operation state and typical characteristics. In this paper, the fuel cell system (FCS) power dynamic response based on constant power change amplitude and various power change amplitude is analyzed. Then, three typical power characteristics of the FCS, namely, low single-cell voltage, drastic voltage uniformity change, voltage undershoot and voltage overshoot, are investigated. Furthermore, to solve the high-dimensional datasets problem, a quick evaluation method based on data dimensionality reduction (DDR) is proposed. The innovation of this method is based on principal component analysis to extract typical characteristic relationships of power operation levels. The results demonstrate that the extracted new one-dimensional (1-D) eigenvectors index exhibits a positive correlation with power levels. Meanwhile, the power operation state empirical model is established through data regression. The Pearson coefficients of the empirical model are all greater than 0.9981. Thus, the new index and proposed method offer novel insights for improving the power adaptive control of fuel cells and quickly assessing their health state. • The effects of different loading amplitudes on power output are investigated. • Typical characteristics of power operation state are analyzed. • A quick evaluation method for power operation state is proposed. [ABSTRACT FROM AUTHOR]

• The BLNNAC scheme is presented for finite-time stability. • The proposed node selection strategy in BLNN can improve the approximation ability. • A BLNN-based NDO is designed to eliminate the adverse impact of disturbances. In this work, a novel broad learning neural network based adaptive control (BLNNAC) scheme is designed for a class of spacecraft proximity systems with external perturbations, unmodeled dynamics and symmetrical time-varying constraints. Firstly, for avoiding the unacceptable complexity caused by conventional Barrier Lyapunov function, the constrained output signals are transformed into unconstrained ones by several nonlinear functions. Secondly, by virtue of fast response and insensitivity to external disturbance, the integral sliding-mode control (ISMC) scheme is incorporated into the presented control scheme. Then, to suppress the adverse impact of the dynamic uncertainties, a novel broad learning neural network (BLNN) is established to ameliorate the approximation performance. Based on that BLNN scheme, a nonlinear disturbance observer (NDO) is designed to approach the time-varying disturbances. Furthermore, a fractional power function, as a particular term of control input, is designed to guarantee the fast convergence rate. It is shown that all the signals are bounded, and the transient-states of the output signals satisfy the constraint conditions constantly. Finally, simulation results illustrate the effectiveness and advantages of the presented scheme. [ABSTRACT FROM AUTHOR]

This paper presents the design of a Lyapunov matrix-based adaptive resilient controller for unmanned marine vehicles (UMVs) under state-dependent sensor attacks, input-dependent thruster attacks, and time delays. Different from the thruster attack model that depends on state information, the thruster attack model studied in this paper is related to control input, that is, the input-dependent thruster attacks. This implies that the designed correction signal is also affected by the attacks. To mitigate the impact of the considered sensor attacks and thruster attacks on UMVs, an adaptive mechanism is employed to estimate the attack factors. Furthermore, a Lyapunov matrix-based complete-type Lyapunov–Krasovskii functional (LKF) is introduced, in which more comprehensive time delay information are considered. Based on this, linear matrix inequality (LMI) method and Jensen's inequality are used to obtain sufficient conditions for the existence of the controller. The proposed controller guarantees that the state errors of UMVs converge asymptotically to zero with the adaptive H ∞ performance index no larger than γ 0. Finally, the efficacy of the proposed approach is verified by simulation results. • A novel Lyapunov matrix-based resilient control strategy is proposed. • Compensate for the impact of the correction signal being attacked. • Constructs a Lyapunov matrix based complete-type LKF. • Ensure that the state errors of UMVs converge asymptotically to zero and has the prescribed H ∞ performance index γ 0. [ABSTRACT FROM AUTHOR]

In this paper, the problem of highly performance motion control of tank bidirectional stabilizer with dead zone nonlinearity and uncertain nonlinearity is addressed. First, the electromechanical coupling dynamics model of bidirectional stabilizer is developed finely. Second, the dead zone nonlinearity in bidirectional stabilizer is characterized as the combination of an uncertain time-varying gain and a bounded disturbance term. Meanwhile, an adaptive robust controller with dead zone compensation is proposed by organically combining adaptive technique and extended state observer (ESO) through backstepping method. The adaptive technique is employed to reduce the impact of unknown system parameter and dead zone parameter. Furthermore, the ESO is constructed to compensate the lumped uncertainties including unmodeled dynamics and dead zone residual, and integrated together via a feedforward cancellation technique. Moreover, the adaptive robust control law is derived to ensure final global stability. In stability analysis, the asymptotic tracking performance of the proposed controller can be guaranteed as the uncertainty nonlinearities in tank bidirectional stabilizer are constant. It is also guaranteed to achieve bounded tracking performance when time-varying uncertainties exist. Extensive co-simulation and experimental results verify the superiority of the proposed strategy. • A mechatronic dynamics model of the tank bidirectional stabilizer is developed, which is accurately modeled including couplings, nonlinearities and uncertainties. • The parametric adaptive function is constructed to update the unmeasurable dead zone parameters online, and the extended state observation technique is introduced to eliminate the dead zone nonlinearity and unmodeled dynamics. • The Lyapunov stability analysis demonstrates that the asymptotic tracking capability is guaranteed when the system uncertainty is constant, and the uniformly bounded tracking performance is guaranteed owing to the existence of time-varying uncertainties. • Comparative analysis of co-simulation and experiment confirms the effectiveness and feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions. • A new approach based on WADC is proposed for damping inter-area oscillations. • A new scheme based on individual WADC for each generator is proposed. • CART technique is used for estimating optimal gains of individual WADC controller. • A combination index is proposed to find the most effective individual WADCs. [ABSTRACT FROM AUTHOR]

In this paper, the problem of adaptive neural network prescribed performance tracking control for a class of non-strict feedback time-delay systems constrained by full-state is studied. Radial basis function (RBF) neural networks (NNs) are integrated into the backstepping medium to deal with the uncertain functions and the barrier Lyapunov function (BLF) technique ensures that the state of the system does not exceed its limits. Subsequently, integrated with the Lyapunov–Krasovskii functional, the proposed control scheme makes the tracking errors converge to the preset region while the state constraint is not violated. Finally, the effectiveness of the scheme is supported by two simulation experiments. • An adaptive neural control scheme is proposed for nonlinear time-delay systems. • For unknown model, the proposed controller can achieve the control objective. • Coordinate transformation is used to improve system tracking performance. [ABSTRACT FROM AUTHOR]

In this research, a new hybrid backstepping control strategy based on a neural network is proposed for tractor-trailer mobile manipulators in the presence of unknown wheel slippage and disturbances. To minimize the negative impacts of wheel slippage, the desired velocities of the tractor's wheels are computed with a proposed kinematic control model with an adaptive term. As the system's dynamical model contains unavoidable uncertainties, model-based backstepping control technique is unable to effectively manage these systems. Hence, the proposed controller blends a radial basis function neural network with the merits of a dynamical model-based backstepping approach. The neural networks are employed to approximate the non-linear unknown smooth function. To minimize the impact of external disturbances, and network reconstruction error an adaptive term is added to the control law. The Lyapunov theorem and Barbalat's lemma are employed to guarantee the stability of the control method. The tracking error is shown to be bounded and to rapidly converge to zero with the proposed method. To demonstrate the efficacy and validity of the control mechanism, comparison simulation results are presented. • A hybrid controller is proposed for tractor-trailer systems with wheel slippage. • By putting forward a kinematic control law, the desired velocities are calculated. • At the dynamical level, RBFNN is used to approximate the uncertainties of the system. • The stability of the system is analyzed with Lyapunov theory and Barbalat's Lemma. • The proposed control scheme is verified through simulation studies. [ABSTRACT FROM AUTHOR]

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]

In this paper, a quick convergence adaptive structure is proposed for trajectory tracking of an articulated type robotic manipulator using the Hardware in the Loop (HIL) simulation technique. A novel Nonlinear Quick Convergence Adaptive Sliding Backstepping Control (QCASBC) algorithm is implemented on a C2000 real-time controller board. The performance of the proposed control algorithm is inspected concerning the sliding mode PID (SM-PID) control technique to estimate its correlation with the proposed algorithm. The experimental part of the HIL simulation tests has been carried out on a simulated model of a three-link serial robot manipulator. A dynamic model of the robotic manipulator has been developed using Matlab Simulink software and its performance is analyzed using the HIL technique via a C2000 real-time controller for tracking the desired trajectory. Results show that the speed of convergence while tracking the desired trajectory of the manipulator is better in the proposed algorithm. It is also estimated that the positional error of the QCASBC algorithm is superior to the SM-PID control algorithm. The observed average angle error is improved by 14% and the average response time error is improved by 11% by using the proposed QCASBC algorithm compared to the SM-PID approach. [ABSTRACT FROM AUTHOR]

Surface movement exploration techniques are an important approach to further the understanding of small celestial bodies. Hopping movement is employed to take advantage of the weak gravitational field of small celestial bodies, facilitating long-distance mobility of the rover. The rover exhibits a strong attitude-orbit coupling characteristic during the hopping movement. Different contact attitudes contribute to the challenge of determining the rebound trajectory, rendering it difficult for the rover to maintain continuous movement towards the target position along the nominal trajectory. In this paper, a dual-stage control method for continuous surface hopping movement is developed, wherein the control is applied separately during the flight stage and the collision stage. During the flight stage, applying attitude control enables the rover to establish contact with the surface in a specific attitude, ensuring accuracy in movement direction and creating favorable conditions for the control process in the collision stage. The flywheel is controlled in the collision stage to correct the state change and energy loss due to the collision, enabling the rover to attain the desired hopping state. Considering the energy consumption of the rover, the minimum hopping velocity sequence and angular velocity sequence are designed based on the hopping angle and heading angle. The intricacies and uncertainties inherent in the surface environment of small celestial bodies pose challenges for the rover during hopping and flying. In response to the issue, a finite-time adaptive sliding-mode control law is devised. The control law facilitates the rapid adjustment of the rover to the target attitude in flight, demonstrating strong robustness. In addition, by integrating the changes in contact point velocity with the impulse contact model, the accuracy of the rover's rebound state updates is enhanced. Finally, a set of simulations is performed, and simulation results indicate that the dual-stage control method can achieve a final landing position error of less than 0.2 m for a continuously hopping. The finite-time adaptive sliding-mode control law can stabilize the rover's attitude within 10 s. Conducting 500 Monte Carlo simulations of the continuously hopping rover in three small celestial body surface simulation environments shows that the position endpoint error is within 0.3 m. • An innovative control method for continuous hopping on the surface of small celestial bodies with low gravity is proposed. • The attitude-orbit coupling challenge is addressed through a staged control. • A sequence of hopping states with minimum velocities is devised. • A finite-time adaptive sliding-mode control law is designed to effectively address unknown disturbances. • A corrected sliding-mode control law is developed for precise hopping state updates. [ABSTRACT FROM AUTHOR]

• The adaptive nonlinear damping is integrated into lateral suspension to improve hunting stability of high-speed train. • Three typical hunting bifurcations with different wheel/rail equivalent conicity functions are analyzed. • An adaptive constraint-following control method is employed to implement nonlinear damping via active suspension system. • The proposed method can significantly improve both carbody and bogie hunting stability. This study employs an active secondary suspension with adaptive nonlinear damping to enhance the hunting stability of high-speed trains. Adjusting passive suspension parameters to optimize ride comfort and hunting stability simultaneously in varied extreme operational conditions poses a significant challenge for high-speed trains. This research integrates high-order displacement-dependent nonlinear damping into the secondary lateral suspension, drawing on insights from field test data. We analyze three typical hunting motion bifurcations: carbody hunting and both subcritical and supercritical bifurcations of bogie hunting. Theoretical analysis demonstrates that (a) the adaptive nonlinear damping narrows the unstable speed range and reduces the amplitude of the limit cycle in carbody hunting, (b) while it does not increase the critical speed for supercritical bogie hunting bifurcation, it substantially reduces the amplitude of the limit cycle, and (c) it increases the nonlinear critical speed for subcritical bogie hunting bifurcation, with the potential to alter the bifurcation from subcritical to supercritical, which holds considerable practical significance. Implementing such nonlinear damping directly through passive structure is challenging. Therefore, in view of this underactuated control problem, the constraint-following control is applied in the active suspension system to inherit the benefits of adaptive nonlinear damping. The simulation results show that the proposed active suspension system can effectively suppress the abnormal vibration caused by hunting motions. [ABSTRACT FROM AUTHOR]

The method, using a servicing spacecraft (SS) mounted with very flexible operation devices (FOD), has shown promising application in detumbling rotating satellites due to its unique advantage of compliant contact processes. The SS is highly flexible, and successful implementation of detumbling rotating satellites is dependent on the advanced controller with high performance. However, the controller design suffers from two problems: (i) since the uncertain dynamics arising from the FOD lead to difficulty in acquiring an accurate dynamic model of the SS, the conventional model-based controllers are unable to achieve high performance; (ii) the large deformation of the FOD caused by contact processes generates impactive disturbance that severely deteriorates the transient and steady-state performances. To address these problems, this paper proposes a prescribed performance based adaptive model-free control for the first time. In the controller design process, a finite-time prescribed performance function is designed to incorporate into the model-free controller, which ensures the user-specified error constraints concerning the convergence rate, overshoot, and tracking accuracy. Meanwhile, an adaptive estimation algorithm is developed to significantly enhance the robustness against the disturbance. Moreover, an anti-windup compensator is constructed to effectively handle the adverse effect of thruster saturation. Simulation results validate the effectiveness of the proposed controller, and further show that the SS can successfully detumble the rotating satellite while achieving prescribed performance. [ABSTRACT FROM AUTHOR]

• Designed affordable 3DOF attitude simulator. • In–house built 3-axis Helmholtz coil cage. • Identified CubeSat demonstrator parameters. • Demonstrated CubeSat self-actuation & detumbling. Spacecraft attitude determination and control systems (ADCS) stand as a pivotal factor for ensuring the success of missions. As satellite sizes continue to decrease, the need for more intelligent and efficient control algorithms becomes increasingly pronounced. In response to this imperative, we propose the development of a scaled-down testbed specifically designed for evaluating satellites (ACDS). This testbed will demonstrate effective control of a CubeSat through the use of Helmholtz coils, magnetic torquer solenoids, reaction wheels and inexpensive student-made components and available components off the shelf (COTS) in the San Diego State University SPACE Lab. Referred to as the Spacecraft Attitude Testbed (SAT), this custom-designed 3-DoF experimental attitude platform replicates the conditions experienced by a weightless satellite in space. The objective of this research paper is to improve the understanding of how to manufacture a testbed and components like those within this paper, expose some of the issues encountered and improve the outcomes for future academic testbeds. Within this study, the testbed employs torque application to magnetic torquers and reaction wheels to regulate attitude. In summary, this research endeavors to establish a comprehensive and cost-effective testbed platform dedicated to the assessment of spacecraft attitude control and determination systems. It not only seeks to enhance our understanding of advanced control algorithms but also aims to contribute valuable insights into fault detection and adaptive control strategies, ultimately bolstering the efficiency and robustness of satellite operations. [ABSTRACT FROM AUTHOR]

During autonomous rendezvous and docking (AR&D) with a tumbling target, multiple unfavorable engineering constraints exist, such as constraints on the control, velocity, or collision avoidance. Meanwhile, many sources of uncertainty exist, including incomplete and inaccurate measurements, control deviation, dynamic uncertainty, and inertial parameter uncertainty of the tumbling target, which significantly complicate the design of the guidance and control algorithm. Considering these challenges, this paper proposes a robust identification, planning, and control unified framework for AR&D with a tumbling target, which incorporates an extended Kalman filter (EKF) with a robust tube-based model predictive control for tracking (TMPT) controller. First, the EKF is designed to estimate the system state and inertial parameters of the target and then predict the future behavior of the tumbling target. Additionally, the prediction information of the target is transmitted to the designed TMPT controller as a reference signal. This TMPT controller unites trajectory planning and control in a single layer that can ensure the stability and recursive feasibility of the algorithm with a short prediction horizon. Moreover, it can guarantee that the closed-loop system is robustly and asymptotically convergent to the reachable set whose center is the optimal reachable periodic trajectory concerning the docking port of the tumbling target. The unified framework represents a robustness-to-optimality control strategy that can accomplish the AR&D mission efficiently while considering multiple engineering constraints and uncertainties. Finally, the efficiency of the proposed control framework is verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

The problem of retargeting rigid body attitude with attitude constraints has been extensively investigated, whereas the challenge persists in maneuvering rigid-body attitude with multiple concurrent attitude constraints and finite sequential actuator faults. Finite sequential actuator faults, which can reduce controllability or destabilize the system, necessitate the implementation of fault-tolerant control strategies. Therefore, this paper aims to address the issue of redirecting spacecraft attitude subject to multiple attitude constraints and finite sequential actuator faults comprehensively. Firstly, we represent spacecraft attitude using unit quaternions and employ an artificial potential function to effectively handle multi-attitude constraints. The negative gradient direction guides the rigid-body spacecraft towards seamless convergence with the desired attitude. Our proposed unwinding strategy effectively avoids unwinding phenomena during large-angle spacecraft reorientation as well. Additionally, we design an adaptive compensation strategy for finite sequential actuator faults that enables real-time calculation of the fault compensation matrix under such actuator faults. Importantly, we propose an improved adaptive fault-tolerant back-stepping controller integrated with a potential function that effectively addresses both attitude constraints and finite sequential actuator faults simultaneously. Subsequently, stability analysis is conducted on the proposed controller to ensure its stability in practical applications rigorously. Finally, numerical simulations are performed meticulously to demonstrate the effectiveness and robustness of our proposed controller. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties. • We propose an adaptive probabilistic recurrent TSK fuzzy neural PID controller. • The Lyapunov stability criterion is used to update the proposed controller parameters. • The proposed controller is applied practically for controlling a servo motor. [ABSTRACT FROM AUTHOR]

In this paper, for macro–micro composite motion platform with piezoelectric hysteresis, an finite-time adaptive robust control method based on extended hysteresis observer is proposed. The dynamic model of macro–micro system is constructed at first. An extended hysteresis observer was designed to estimate the actual displacement and speed of motion system. Then, an adaptive robust control law is designed to eliminate the uncertain hysteresis model parameters. After this, exponential convergence result of the proposed control method is given and proved. By setting the expected bandwidth of macro–micro system, the gain adjustment process of the control method can be reduced in computation. The effectiveness of the proposed control method is demonstrated by comparison with other control methods in simulation, and the proposed control method has more stable tracking effect and smaller tracking error. • Finite-time adaptive robust controller for Macro–micro composite platform. • Design and implementation of extended state observer for piezoelectric hysteresis. • Design of gain adjustment process to simplify calculation. [ABSTRACT FROM AUTHOR]

Reconfigurable variable stiffness actuator (RVSA) has attracted increasing attention in robotics due to its safety, compliance, and robustness. However, the control of the RVSA is challenging due to nonlinear factors such as high-order nonlinear dynamic, model uncertainties, time-varying model parameters, and disturbances. In this paper, firstly, a lightweight RVSA structure with both passive and active nonlinear variable stiffness characteristic is developed. Secondly, a dynamic surface backstepping control method based on a radial basis neural network and disturbance observer (DSBC-RBFNN-DOB) is proposed to achieve position control of the lightweight RVSA with matched and unmatched uncertainties. To address solve the "complexity explosion" and noise problems in traditional backstepping control, the dynamic surface backstepping control (DSBC) method is used to design the controller. Then, a method based on radial basis neural network (RBFNN) and disturbance observer (DOB) are used to compensate for the matched and unmatched uncertainties in the link and motor. In this method, the matched uncertainties are compensated using RBFNN, and the DOB is integrated to compensate RBFNN approximation errors and unmatched uncertainties. Through Lyapunov stability analysis, the semi-global boundedness of the controller is proven. Finally, the proposed method is simulated and actually implemented, verifying the effectiveness of the method. Simulation and experimental results show that the root mean square error (RMSE) of the proposed method is only 0.97277° and 0.6418°, respectively. Compared with PID, DSBC, and DSBC-RBFNN, the error reduction percentages in simulation (experiment) are 85.6 % (88.9 %), 49.4 % (88.4 %) and 36.1 % (80.0 %) respectively. • A lightweight reconfigurable variable stiffness actuator with smaller mass and more compact structure is proposed.. • A dynamic surface backstepping control method based on neural network and disturbance observer is proposed. • A method of uncertainty and disturbance estimation combining neural network and disturbance observer is proposed. • Experiments and simulations were conducted under the condition of actively changing stiffness. [ABSTRACT FROM AUTHOR]

• We propose a novel adaptive sliding mode based spacecraft attitude controller. • The boundary of the model uncertainty and external disturbance is not required. • Actuator failure and saturation problem is considered by using an RBF network. • A control gains reduction technique is applied to weaken chattering phenomenon. Taking the external disturbance, model uncertainty, actuator failure, and actuator saturation into consideration, this paper investigates the nonlinear fault-tolerant attitude control problem for the spacecraft. First, to deal with the disturbance and uncertainty with unknown boundaries, an adaptive sliding mode control method is proposed to stabilize the state variables of the spacecraft attitude control system. Then the actuator failure and saturation are further considered, and the second spacecraft fault-tolerant attitude controller is derived based on adaptive sliding mode control and radial basis function (RBF) neural networks. The adaptive control gains reduction technique is applied in the design process, which can weaken the chattering phenomenon of the controller to some extent, and the stability of the attitude control system is analyzed through Lyapunov theory. In the proposed controller, model information and external disturbance are not required and only the system states are needed. Furthermore, the boundaries of the model uncertainty, external disturbance, actuator fault and saturation are assumed to be existing but unknown by the proposed controllers. These features make the proposed attitude controllers need few modeling information of the spacecraft, or maintain strong robustness even if the model or external environment occurs significant changes. Finally, numerical simulations demonstrate the great performance of the proposed fault-tolerant attitude control method. [ABSTRACT FROM AUTHOR]

This article studies the passive tracking problem of a wearable exoskeleton for lower limb rehabilitation therapy in the face of unmodeled dynamics, interactive friction, disturbance, prescribed performance constraints, and actuator faults. Adaptive neural networks and a smooth performance function are incorporated to establish a novel fault-tolerant tracking scheme, which can not only compensate for the nonlinear uncertainties and disturbance, but also handle the actuator fault with guaranteed tracking performance. A state feedback controller is presented by using the full state information and an output feedback controller is developed when the angular velocity is unavailable. The differential explosion issue of the backstepping technique is resolved by constructing a first-order filter and the unmeasurable velocity is estimated by a nonlinear observer. Semiglobal uniform boundedness stabilities of the exoskeleton system are proved via the Lyapunov direct method. The tracking performances of the designed control approaches are tested by comparative simulations. [ABSTRACT FROM AUTHOR]

In this study, the problem of non-overshooting tracking control (NOTC) for a class of strict-feedback systems with prescribed finite-time (FT) is studied. In order to obtain a simple closed-loop system (CLS), we first propose a criterion lemma for prescribed FT stable control. A prescribed FT controller is designed by using backstepping and the proposed criterion lemma. According to the CLS, many conditions of NOTC are analyzed. Compared with the related results, the NOTC conditions obtained in this paper are relatively extensive, which reduces conservatism. The algorithm makes the system overshoot zero, so this result increases the accuracy. The final simulation results show that this algorithm is effective. • In the related research results of no overshoot control, the closed-loop system obtained by the existing prescribed finite-time control algorithm is very complicated. This leads to the failure to calculate the expression of tracking error through the closed-loop system, and then the control condition with non-overshooting cannot be obtained. Then, a new criterion lemma of prescribed finite-time control is proposed to design the control in this paper. Obviously, our closed-loop system is relatively simple. • In the related research results of non-overshooting control, the non-overshooting tracking control conditions obtained in this paper are relatively extensive, which reduces the conservatism of the existing algorithms. • Compared with mean-nonovershooting tracking control, the algorithm in this paper can make the overshoot of tracking error zero. Therefore, this result increases accuracy. [ABSTRACT FROM AUTHOR]

This paper focuses on the output-feedback control for path-following of underactuated autonomous underwater vehicles subject to multiple uncertainties and unmeasured velocities. First, a novel extended state observer is proposed to estimate the mismatched lumped disturbance and recover the unmeasured velocities. Based on this premise, to overcome the limitation of relying solely on the accurate kinematic model, a disturbance observer-based stabilizing controller is developed. The difference in bandwidths between the observer and the vehicle dynamics allows for a mathematical setup amenable to standard singular perturbation theory. In the fast mode, a kinematic observer is designed to reject system uncertainty caused by unknown attack angular velocity and prohibitive path-tangential angular velocity, using a novel physical perspective. In the slow mode, an interconnected-system control law is proposed by integrating the backstepping technique with the time scale decomposition method. Furthermore, the stability of the overall closed-loop system is established. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method for path-following of underactuated autonomous underwater vehicles in the vertical plane. • A novel ESO is developed to estimate the mismatched lumped disturbances and recover the unmeasured velocities by combining high-gain and singular perturbation techniques, leading to reduced complexity in stability analysis. • A DO is designed to estimate model uncertainties by utilizing the difference in bandwidths between the observer and state dynamics. The resulting singular perturbation analysis provides a new perspective on designing a DO in a straightforward manner. • An interconnected-system control law is proposed based on a time scale decomposition method to address the problem of the "explosion of terms" inherent in conventional backstepping. • The stability analysis enables the derivation of mathematical bounds on the control gains, offering guidance for selecting control gains to prevent controller ill-conditioning and/or closed-loop instability. [ABSTRACT FROM AUTHOR]

Uncertainty can lead to jitter or overshoot in mechanical systems, necessitating the design of multiple constraints to stabilize them. This paper proposes a control structure based on the generalized Udwadia–Kalaba equation to address these constraints simultaneously. An uncertain dynamical model is developed, incorporating both equality and inequality constraints. By integrating diffeomorphism theory, a robust control strategy is designed to ensure compliance with these constraints. Utilizing the Lyapunov approach, the uniform boundedness and uniform ultimate boundedness of the dynamical system are demonstrated. Finally, the feasibility of the proposed control method is validated through its application to a belt conveyor system. • Equality constraints and inequality constraints are simultaneously considered in dynamical system based on the generalized Udwadia–Kalaba approach. • A robust control for handling inequality constraints that does not affect equality constraints is designed. • The robust control can render the uncertain system uniformly bounded and uniformly ultimately bounded. • The method is proposed of applying inequality constraints to belt conveyors to avoid belt breakage and folding faults in engineering. [ABSTRACT FROM AUTHOR]

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved. [Display omitted] • A passive power filter design to share the reactive power requirement in addition to mitigating the predominant harmonics of the 5th and 7th order. • A robust nonlinear adaptive filter implementation based on exponential functional link network (EFLN-NAF) algorithm to the shunt hybrid active power filter (SHAPF) control for reference current generation and fundamental component separation from harmonics. • Solution for current-associated power quality issues such as reducing the reactive power burden, achieving unity power factor operation, and mitigating the harmonics, thereby reducing the source current THD value. • Simulation of the proposed technique for the control of shunt hybrid active power filter and evaluation of the outcomes for resolving the power quality issues in both steady-state and dynamic conditions. • Development of a hardware prototype at a laboratory scale using OPAL-RT (OP4510) real-time simulator as a controller for the proposed technique for the control of SHAPF and evaluation of the outcomes. [ABSTRACT FROM AUTHOR]

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]

This article addresses an attitude stabilization control problem for the asymmetric underactuated tethered spacecraft subject to external disturbances, and a reinforcement learning(RL)-based finite time control scheme is proposed to enhance the control performance and energy efficiency of the closed-loop system. Firstly, the error dynamics of the underactuated tethered system in the presence of external disturbances is built based on the Lagrange's modeling technique. Then, a RL-based control algorithm is implemented by a radial basis function (RBF) neural network (NN), in which the actor–critic networks are developed to obtain the optimal performance index function and the optimal controller. According to the Lyapunov theorem, semi-global finite-time stability of all the closed-loop signals is achieved through rigorous mathematical analysis, and tracking errors can be ensured to an arbitrarily small neighborhood of the origin in a finite time. Finally, comparative simulation results with hierarchical sliding mode controller are presented to demonstrate the viability of the proposed strategy. [Display omitted] • The proposed algorithm does not rely on the harsh symmetry condition of mass matrix. • Actor-critic NNs are used to obtain the performance index function and controller. • A novel RL-based scheme is designed to ensure semi-global finite-time convergence. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the concept of machinability by considering the dynamic, tribological, and thermo-mechanical interactions encountered at the tool-chip-machined surface interfaces. The paper provides a demonstration of the capabilities and gaps of the physics-based models for the characterization of the machining performance and the prediction of machinability of difficult-to-cut materials, including additively manufactured (AM) materials, nanocrystalline (NC) materials, fibre reinforced polymers (FRP), metal matrix composites reinforced with ceramic hard particles (MMC), and ceramic matrix composites (CMC). The utilization of efficient computation methods for accurate prediction of force, torque, power consumption, cutting temperature, deflection errors, vibration amplitudes, chatter stability, and thermomechanical interactions in the tool-workpiece system is discussed. The development of thermally-activated dissolution-diffusion wear models to describe the chemical reactions at the tool-chip-workpiece contact interfaces is also presented. These predictions are critical for identifying multi-objectives optimal machining conditions. The integration of predictive machining models within the framework of digital twins in cyber-physical spaces, for in-process monitoring and adaptive control, is demonstrated. Future research for developing new models that can characterize the machinability of AM and NC materials, by considering the effects of varying material microstructure and anisotropy, is presented for conventional and micro-machining operations. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to address the issue of vibration control for a class of Euler–Bernoulli beam systems that are subject to external disturbances and input saturation. The proposed controller differs from other backstepping methods in that it employs a radial basis function (RBF) neural network to accurately estimate boundary disturbances and incorporates the hyperbolic tangent function to ensure input constraints. The nonlinear partial differential equation (PDE) model is initially derived based on Hamilton's principle to capture the dominant dynamic characteristics of the flexible beam. In the framework of the Lyapunov direct approach, an adaptive RBF neural network-based law is subsequently designed to estimate the state-related boundary disturbances. The backstepping approach is then developed to propose sufficient conditions for ensuring the stability and convergence of closed-loop systems subject to input saturation. Finally, the effectiveness and superiority of the proposed methodology are further demonstrated by comparing the simulation results of constrained backstepping controllers. [Display omitted] • The paper addresses the issue of vibration control for Euler–Bernoulli beam models. • This system is susceptible to unknown external disturbances and input saturation. • A constrained boundary controller is developed based on backstepping technology. • An RBF neural network observer is proposed to estimate the boundary disturbances. • The closed-loop stability is established by considering the above constraints. [ABSTRACT FROM AUTHOR]

Three-level T-type converters are necessary interfaces for distributed energy resources to interact with the public grid. Naturally, designing a control strategy, featuring superior dynamics and strong robustness, is a promising solution to guarantee the efficient operation of converters. This article presents an improved finite-time control (IFTC) strategy for three-level T-type converters to enhance the dynamic performance and anti-disturbance capacity. The IFTC strategy integrates a dual-loop structure to regulate the dc-link voltage and grid currents. Specifically, the voltage regulation loop employs a finite-time adaptive controller that can counteract load disturbances without relying on current sensors. In the current tracking loop, finite-time controllers combined with a command filter are constructed to obtain fast and accurate current tracking. In this loop, the command filter is utilized to avoid calculating the derivative of current references. Theoretical analysis and experimental results demonstrate the IFTC strategy's effectiveness. • It aims to achieve fast finite-time tracking control of three-level T-type converters. • The proposed improved finite-time control (IFTC) strategy integrates a dual-loop structure. • The IFTC enhances the robustness against load disturbances and parameter uncertainties. • Both theoretical analysis and experimental results confirm its effectiveness. [ABSTRACT FROM AUTHOR]

• A preassigned finite-time attitude synchronization controller is proposed for multiple rigid spacecraft systems by using two designed barrier Lyapunov functions. • The convergence time of the designed controller can be preassigned according to different space task requirements and it is independent of the initial condition of the system states and control parameters. • The steady-state error of the closed-loop system can be limited to a certain range by the appropriate choice of the controller parameters. This paper investigates the preassigned finite-time attitude synchronization control for the spacecraft formation with a dynamic virtual leader under an undirected connected graph. Each follower is described by a rigid spacecraft model subjects to parameter uncertainty, actuator saturation, and the disturbance. First, two fixed-time sliding mode estimators are introduced to estimate the reference attitude variable and the reference angular velocity, respectively. Then, by selecting the attitude errors as the system states, an attitude tracking error system for the spacecraft formation is established. An extended state observer is applied for computing the total disturbance in the attitude tracking error system. Based on the estimations of the extended state observer and two sliding mode estimators, a novel distributed controller is proposed to force the states of the established attitude tracking error system converge to a small region in preassigned finite time by using two modified time-varying barrier Lyapunov functions. The practical preassigned finite-time stability of the resulting closed-loop system is proven by Lyapunov stability theory. Finally, some simulations are provided to illustrate the effectiveness and advantages of the developed preassigned finite-time attitude synchronization control scheme. [ABSTRACT FROM AUTHOR]

A significant attention has concentrated on systems with "hidden attractors" in recent years. The aim of this work is to study an oscillator displaying "hidden attractors". The oscillator has all nonlinear terms but no equilibrium. The oscillator exhibits attractive dynamics such as chaos, bubble bifurcation, symmetrical attractors, and boosting attractors. Oscillator's feasibility is verified by a microcontroller. Utilizing the oscillator for biomedical image encryption illustrates its application. In addition, the adaptive control is designed for the oscillator. [ABSTRACT FROM AUTHOR]

The conventional ternary composite control method utilized in linear motor control offers the advantages of high positioning accuracy and high dynamic response. However, its performance is constrained due to time delays in high-speed and high-precision servo systems. To tackle this problem, a segmented ternary composite control method considering time delay is developed in this work. Firstly, a nominal model is established with the consideration of sampling delay, current control loop delay, etc. A robust feedback controller is designed using the nominal model to achieve a high closed-loop bandwidth. Besides, a novel iterative feedforward tuning algorithm based on feedback control signal decomposition (IFFT-FCSD) is proposed to attain high dynamic performance. The algorithm decomposes the feedback control signal into several basis signals using the least squares algorithm. More precise tracking accuracy is obtained after compensating for the time delay between the basis signals and the feedback control signal. Furthermore, to increase the bandwidth of the disturbance observer (DOB), the observed control signal is compensated with the equivalent time delay of the nominal model. Finally, the segmented control strategy is implemented. The motion-profile-based feedforward controller is only engaged during the motion tracking period, and the DOB is employed exclusively during the settling period to attain precise positioning while minimizing the negative impact on dynamic performance. Comparative experiments conducted on a linear motor validate the effectiveness of the proposed method. The maximum tracking error is 1. 28 μ m and the positioning error converges to within ± 450 nm in just 9.3 ms during the settling period for a point-to-point motion with a maximum speed of 0.55 m/s, a maximum acceleration of 150 m/s 2 , and a stroke of 4 mm. • The effect of time delays on high-speed and high-precision servo systems is studied. • A segmented ternary composite control method considering time delay is proposed. • A novel IFFT-FCSD algorithm is developed to tune the feedforward parameters. • High dynamic performance and precise positioning of the linear motor are achieved. [ABSTRACT FROM AUTHOR]