Search Results (23)

Time-varying lumped disturbance and measurement noise are primary obstacles that restrict the control performance of permanent magnet synchronous motor (PMSM) drives. To tackle these obstacles, an adaptive nonsingular terminal sliding mode (ANTSM) algorithm is combined with augmented recursive sliding mode observer (ARSMO) for PMSM speed regulation system in this paper. Generally, conventional nonsingular terminal sliding mode (NTSM) controller adopts a fixed and conservative control gain to suppress the time-varying disturbance, which will lead to unsatisfactory steady-state performance. Without requiring any information of the time-varying disturbance in advance, a novel barrier function adaptive algorithm is utilized to adjust the gain of NTSM controller online according to the amplitude of disturbance. In addition, the ARSMO is emoloyed to estimate the total disturbance and motor speed simultaneously, thereby alleviating the negative impact of measurement noise and excessive control gain. Comprehensive experimental results verify that the proposed enhanced ANTSM strategy can optimize the dynamic performance of PMSM system without sacrificing its steady-state performance. Full article

Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article

Failures in gas path components pose significant risks to aeroengine performance and safety. Traditional fault diagnosis methods often require extensive data and struggle with real-time applications. This study addresses these critical limitations in traditional studies through physics-informed modeling and adaptive estimation. A nonlinear component-level model of the JT9D engine is developed through aero-thermodynamic governing equations, enhanced by a dual-loop iterative cycle combining Newton–Raphson steady-state resolution with integration-based dynamic convergence. An augmented state-space model that linearizes nonlinear dynamic models while incorporating gas path health characteristics as control inputs is novelly proposed, supported by similarity-criterion normalization to mitigate matrix ill-conditioning. A hybrid identification algorithm is proposed, synergizing partial derivative analysis with least squares fitting, which uniquely combines non-iterative perturbation advantages with high-precision least squares. This paper proposes a novel enhanced Kalman filter through integral compensation and three-dimensional interpolation, enabling real-time parameter updates across flight envelopes. The experimental results demonstrate a 0.714–2.953% RMSE in fault diagnosis performance, a 3.619% accuracy enhancement over traditional sliding mode observer algorithms, and 2.11 s reduction in settling time, eliminating noise accumulation. The model maintains dynamic trend consistency and steady-state accuracy with errors of 0.482–0.039%. This work shows marked improvements in temporal resolution, diagnostic accuracy, and flight envelope adaptability compared to conventional approaches. Full article

This study proposes a novel fuzzy disturbance observer (FDO)-augmented adaptive nonsingular terminal sliding mode control (NTSMC) framework for multi-joint robotic manipulators, addressing critical challenges in trajectory tracking precision and disturbance rejection. Unlike conventional disturbance observers requiring prior knowledge of disturbance bounds, the proposed FDO leverages fuzzy logic principles to dynamically estimate composite disturbances—including unmodeled dynamics, parameter perturbations, and external torque variations—without restrictive assumptions about disturbance derivatives. The control architecture achieves rapid finite-time convergence by integrating the FDO with a singularity-free terminal sliding manifold and an adaptive exponential reaching law while significantly suppressing chattering effects. Rigorous Lyapunov stability analysis confirms the uniform ultimate boundedness of tracking errors and disturbance estimation residuals. Comparative simulations on a 2-DOF robotic arm demonstrate a 97.28% reduction in root mean square tracking errors compared to PD-based alternatives and a 73.73% improvement over a nonlinear disturbance observer-enhanced NTSMC. Experimental validation on a physical three-joint manipulator platform reveals that the proposed method reduces torque oscillations by 58% under step-type disturbances while maintaining sub-millimeter tracking accuracy. The framework eliminates reliance on exact system models, offering a generalized solution for industrial manipulators operating under complex dynamic uncertainties. Full article

The independent metering control system is renowned for its ability to independently regulate the flow and pressure of various actuators, achieving high efficiency and energy savings in hydraulic systems. The high-speed digital valve is known for its fast response to control signals and precise fluid control. However, challenges such as jitter in the position control of hydraulic cylinders, unknown dead zone nonlinearity, and time variance in electro-hydraulic proportional systems necessitate further investigation. To address these issues, this study initially establishes an independent metering control system with digital valves. Based on the state space equation and Lyapunov stability judgment conditions, a high-order sliding mode controller is designed. In addition, a radial basis function (RBF) neural network is constructed to approximate uncertainties arising from the modeling process, the accuracy error indicator uses Mean Absolute Error (MAE), and a finite time generalized extended state observer (GESO) is introduced to conduct online disturbance observation for the external disturbances present within the control system. Consequently, a variable structure high-order sliding mode control strategy, augmented by RBF neural networks and finite time generalized extended state observer (RBF-GESO-SMC), is proposed. Finally, simulations and experimental verification are performed, followed by a comprehensive analysis of the experimental results. Compared with the sliding mode control (SMC), the RBF-GESO-SMC diminishes the displacement-tracking control accuracy error by 63.7%. Compared with traditional Proportional-Integral-Derivative (PID) control, it reduces the displacement-tracking control accuracy error by 78.1%. The results indicate that, through the comparison with SMC and PID control, RBF-GESO-SMC exerts significant influence on the improvement of position accuracy, anti-interference ability, transient response performance, and stability. Full article

This article proposes a fault diagnosis method based on an adaptive sliding mode observer (SMO) for current sensors (CSs) in the charging modules of DC charging piles. Firstly, we establish a model of the phase-shift full-bridge (PSFB) converter with CS faults. Secondly, the fault of the CS is reconstructed through system augmentation and non-singular coordinate transformation. Then, an adaptive SMO is designed to estimate the reconstructed state, and the residual between the actual value of the reconstructed state and the observed value is used as the fault detection variable. Finally, by using norms to design adaptive thresholds and comparing them with fault detection variables, the diagnosis of incipient faults, significant faults, and failure faults in CSs can be achieved. The experimental results verify the effectiveness of the proposed method in this paper; the robustness of the method has been verified under the conditions of DC voltage fluctuations and load fluctuations. Full article

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based sliding mode control (FESO-SMC) methodology aimed at enhancing the aircraft’s resilience against such disturbances. Initially, this study adopts a state expansion strategy to integrate both wind and structural disturbances into a comprehensive disturbance model applicable to the agricultural unmanned helicopter. Following this, a sliding mode control law is formulated with consideration for unknown total disturbances, employing specific sliding mode functions alongside exponential reaching laws. An extended state observer is simultaneously implemented within the sliding mode control framework to estimate and mitigate these disturbances, thereby augmenting the disturbance rejection capabilities of the flight control system. Additionally, the integration of fuzzy logic facilitates adaptive parameter adjustment for the extended state observer, leading to more accurate disturbance estimation. Consequently, a trajectory tracking control system tailored specifically for the agricultural unmanned helicopter has been developed, and its performance was evaluated through simulation experiments. The results indicate that, under certain disturbances, the attitude control error of the FESO-SMC controller is reduced to one-fifth that of traditional sliding mode controllers, while position control accuracy is enhanced more than twofold, thus demonstrating that the proposed FESO-SMC controller not only exhibits superior anti-disturbance capability and robustness but also achieves higher tracking accuracy compared to conventional sliding mode controller. Full article

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter’s ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system’s state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems. Full article

This research presents a novel current loop control strategy for a three-phase PWM rectifier system aimed at mitigating challenges related to substandard power quality, excessive current harmonics, and insufficient robustness. The suggested approach combines an extended state observer (ESO) with dual-power sliding mode control that is further enhanced by fractional-order micro-integral operators. This amalgamation enhances the adaptability of the controller to system dynamics and augments the flexibility of the current loop control mechanism. The results of this integration include diminished system oscillations, heightened immunity to external disturbances, and improved robustness and dynamics of the overall system. Through MATLAB/Simulink simulations, the effectiveness of the proposed control methodology is validated, demonstrating superior performance in terms of robustness, dynamic response, power quality enhancement, and mitigation of current harmonics when compared to conventional PI control and standard fractional-order dual-power sliding mode control techniques. Full article

Fault data injection attacks may lead to a decrease in system performance and even a malfunction in system operation for an automatic feedback control system, which has motive to develop an effective method for rapidly detecting such attacks so that appropriate measures can be taken correspondingly. In this study, a secure descriptor estimation technique is proposed for continuous-time Lipschitz nonlinear cyber physical systems affected by actuator attacks, sensor attacks, and unknown process uncertainties. Specifically, by forming a new state vector composed of original system states and sensor faults, an equivalent descriptor dynamic system is built. A proportional and derivate sliding-mode observer is presented so that the system states, sensor attack, and actuator attack can be reconstructed successfully. The observer gains are obtained by using linear matrix inequality to secure robustly stable estimation error dynamics. Moreover, a robust descriptor fast adaptive observer estimator is presented as a complement. Finally, the efficacy levels of the proposed design approaches are validated using a vertical take-off and landing aircraft system. Comparison studies are also carried out to assess the tracking performances of the proposed algorithms. Full article

It is of great importance to simultaneously stabilize output power and suppress platform motion and fatigue loads in floating offshore wind turbine control systems. In this paper, a novel composite blade pitch control scheme considering actuator fault is proposed based on an augmented linear quadratic regulator (LQR), a fuzzy proportional integral (PI) and an adaptive second-order sliding-mode observer. Collective pitch control was achieved via the fuzzy PI, while individual pitch control was based on the augmented LQR. In the case of actuator fault, an adaptive second-order sliding-mode observer was constructed to effectively eliminate the need for the upper bound of unknown fault derivatives and suppress the chattering effect. This paper conducted co-simulations based on FAST (Fatigue, Aerodynamics, Structures, and Turbulence) and MATLAB/Simulink to verify the effectiveness and superiority of the proposed scheme under different environmental conditions. It is shown that platform roll was reduced by approximately 54% compared to that under PI control. For the tower fore–aft moment, load reductions of 45% or more were achievable. The proposed scheme can greatly reduce the pitch and roll of the floating platform and loads in the windward direction of the wind turbine. Full article

This paper presents a novel extended state observer (ESO) approach for a class of plants with nonlinear dynamics. The proposed observer estimates both the state variables and the total disturbance, which includes both exogenous and endogenous disturbance. The study’s changes can be summarized by developing a sliding mode higher-order extended state observer with a higher-order augmented state and a nonlinear function for the estimation error correction terms (SMHOESO). By including multiple enhanced states, the proposed observer can monitor total disturbances asymptotically, with the second derivative of the total disturbance serving as an upper constraint on the estimation error. This feature improves the observer’s ability to estimate higher-order disturbances and uncertainty. To extend the concept of the linear extended state observer (LESO), a nonlinear function can modify the estimation error in such a way that the proposed observer can provide faster and more accurate estimations of the state and total disturbance. The proposed nonlinearity also reduces the chattering issue with LESOs. This research thoroughly examines and analyzes the proposed SMHOESO’s convergence using the Lyapunov technique. According to this analysis, the SMHOESO is asymptotically stable, and the estimation error can be significantly reduced under real-world conditions. In addition to the SMHOESO, a modified Active Disturbance Rejection Control (ADRC) scheme is built, which includes a nonlinear state error feedback (NLSEF) controller and a nonlinear tracking differentiator (TD). Several nonlinear models, including the Differential Drive Mobile Robot (DDMR), are numerically simulated, and the proposed SMHOESO is compared to several alternative types, demonstrating a significant reduction in controller energy, increased control signal smoothness, and accurate tracking of the reference signal. Full article

This article proposes an adaptive flight framework that integrates a discrete-time incremental nonlinear dynamic inversion controller and a neural network (NN)-based observer for maneuvering flight. The framework is built on the feedback-inversion scheme in which the adaptive neural network augments a discrete-time disturbance observer in the loop. The effects of the modeling uncertainties and the exogenous perturbations are both taken into consideration and are alleviated by the observer. By utilizing the Lyapunov synthesis method, the updating rule of the NN’s weights is introduced, which guarantees the system’s stability with enhanced tracking performance. The efficiency of the proposed scheme is presented through numerical verification of a 6-DOF fixed-wing fighter performing several aggressive flight maneuvers. Extensive simulation results illustrate that this versatile controller is more practical for aerobatic flights compared with the discontinuous sliding mode (DSM) and the nonlinear dynamic inversion (NDI) methods. Given well-generated maneuver commands, the aircraft can accurately track the aggressive reference in the presence of modeling perturbations such as changes in aerodynamic coefficient, inertial parameters, and wind gusts. Full article

This paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An $H_{\infty }$ performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods. Full article

This study proposes a tele-aiming control strategy for the ground reconnaissance robot to track the maneuvering target rapidly in the presence of dynamic uncertainties, sensory measurement noises, and time-varying external disturbances. First, the tele-aiming control trajectory generated by human–computer interaction (HCI) device is filtered with a tracking differentiator and a recursive average filter. Second, the inertial impact force disturbance generated by maneuvering tele-aiming control jointly with the other uncertainties (e.g., internal friction, modeling error, etc.) is considered as a lumped disturbance, and then a novel multiple-model augmented-state extended Kalman observer (MEKO) is designed, capable of filtering out the joint measurement noises and estimating the lumped disturbance simultaneously. Lastly, a nonsingular terminal sliding mode controller is applied to eliminate the lumped disturbance and control the joints to track the corresponding desired joint trajectory. To verify the tele-aiming control performance, the random trajectory tracking experiments are designed to simulate the tele-aiming tracking control of maneuvering targets. As indicated from the experimental results, the proposed control strategy is capable of significantly suppressing the effect of inertial impact force disturbance and joint measurement noises, and achieving fast and stable tele-aiming control. Full article