Search Results (64)

This study addresses robust trajectory tracking for quadrotor unmanned aerial vehicles (QUAVs) under partial state measurements and bounded external disturbances. To this end, a control framework is introduced that integrates backstepping sliding mode control (BSMC) with a super-twisting observer (STO). In this scheme, only position and attitude are directly measured while the STO reconstructs the linear and angular velocities in real time. The estimated states are then fed into the control law, enabling accurate trajectory tracking and robust performance without full-state feedback or explicit disturbance compensation. The approach is validated through three simulation scenarios: nominal full-state feedback, observer-based control without disturbances, and observer-based control under bounded time-varying perturbations. Quantitative metrics confirm consistent tracking accuracy and closed-loop stability across all scenarios. These results demonstrate the effectiveness of the integrated BSMC–STO framework for QUAV operations in sensor-limited and disturbance-prone environments. Full article

This paper introduces a novel sensorless speed control strategy for squirrel-cage induction motors, which ensures robust operation in the presence of external disturbances by applying the state feedback technique. Based on the induction motor model, the speed controller is synthesized by defining a sliding variable that is driven to zero through the supertwisting control law, ensuring the stabilization of the tracking error. The time derivative of the error variable is estimated using a robust differentiator based on the sliding-mode twisting algorithm, thereby eliminating the need to estimate the load torque. A robust observer is employed to estimate the rotor speed and flux linkages simultaneously. The convergence of the estimated rotor flux linkages is enforced through a discontinuous first-order sliding-mode input, while the convergence of the rotor speed estimate is attained via a quasi-continuous super-twisting sliding-mode input. In the proposed model, the inductance parameters are determined from the magnetizing inductance and the leakage inductances of the stator and rotor. A procedure is also presented for adjusting the stator resistance and leakage inductances, taking into account the squirrel-cage rotor type and the skin effect in alternating current conduction. The performance of the sensorless speed control system under variations in load torque and reference speed is validated through experimental testing. The rotor speed estimation provided by the robust observer is accurate. The reference speed tracking control, evaluated using a 1600–1700 rpm pulse train phase-shifted by 4 s with respect to a 0–0.5 N·m pulse train, demonstrates high precision. Full article

The permanent magnetic synchronous motor (PMSM) is of significant use for the centrifugal hydrogen compressor (CHC) in the hydrogen fuel cell system. In order to satisfy the demand for improving the CHC’s performance, including higher accuracy, higher response speed, and wider speed range, this paper proposes a novel adaptive super-twisting sliding mode observer (ASTSMO)-based position sensorless control strategy for the highspeed PMSM. Firstly, the super-twisting algorithm (STA) is introduced to the sliding mode observer (SMO) to reduce chattering and improve the accuracy of position estimation. Secondly, to increase the convergence speed, the ASTSMO is extended with a linear correction term, where an extra proportionality coefficient is used to adjust the stator current error under dynamic operation. Finally, a novel adaptive law is designed to solve the PMSM’s problems of wide speed change, wide current variation, and inevitable parameters fluctuation, which are caused by the CHC’s complex working environment like frequent load changes and significant temperature variations. In the experimental verification, the position accuracy and dynamic performance of the PMSM are both improved. It is also proved that the proposed strategy can guarantee the stable operation and fast response of the CHC, so as to maintain the reliability and the hydrogen utilization of the hydrogen fuel cell system. Full article

A key trade-off persists in the control of permanent magnet synchronous motors (PMSMs): achieving fast finite-time convergence often exacerbates control chattering, while conventional chattering-suppression methods can compromise the system’s dynamic response. The existing literature often addresses these challenges in isolation. The core original contribution of this research lies in proposing a novel robust fuzzy adaptive control scheme that effectively resolves this trade-off through a synergistic design. The contributions are as follows: (1) A novel reaching law is formulated to significantly accelerate error convergence, achieving finite-time stability and improving upon conventional reaching law designs. (2) A super-twisting sliding mode observer is integrated into the control loop, providing accurate real-time estimation of load torque disturbances, which is used for feedforward compensation to drastically improve the system’s disturbance rejection capability. (3) A fuzzy adaptive mechanism is developed to dynamically tune key gains in the sliding mode law. This approach effectively suppresses chattering without sacrificing response speed, enhancing system robustness. (4) The stability and convergence of the proposed controller are rigorously analyzed. Simulations, comparing the proposed method with conventional adaptive sliding mode control (ASMC), demonstrate its marked superiority in control accuracy, transient behavior, and disturbance rejection. This work provides an integrated solution that balances rapidity and smoothness for high-performance motor control, offering significant theoretical and engineering value. Full article

This study proposes a hierarchical composite adaptive sliding-mode control strategy to address the strong nonlinear dynamics of a hexarotor Unmanned Aerial Vehicle (UAV) and the external disturbances encountered during flight. First, within the position-control loop, a Terminal Sliding Mode Control (TSMC) is designed to guarantee finite-time convergence of the system states, thereby significantly improving the UAV’s rapid response to complex trajectories. Concurrently, an online Adaptive rates mechanism is introduced to estimate and compensate unknown disturbances and modeling uncertainties in real time, further enhancing disturbance rejection. In the attitude-control loop, a Super-twisting Sliding Mode Control (STSMC) method is employed, where an Adaptive rate law dynamically adjusts the sliding gain to prevent overestimation and high-frequency chattering, while ensuring fast convergence and smooth response. To comprehensively validate the feasibility and superiority of the proposed scheme, a representative helical trajectory-tracking experiment was conducted and systematically compared, via simulation, against conventional control methods. Experimental results demonstrate that the proposed approach achieves stable control within 0.15 s, with maximum position and attitude tracking errors of 0.05 m and 0.15°, respectively. Moreover, it exhibits enhanced robustness and adaptability to external disturbances and parameter uncertainties, effectively improving the motion-control performance of hexacopter UAVs in complex missions. Full article

In response to the limitations of suboptimal control accuracy, compromised synchronization capability, and reduced stability inherent in PID control for conventional shaftless multi-permanent magnet synchronous motor drive systems, this article establishes a three-motor synchronous control system model for a shaftless printing system. On this basis, the speed loop adopts a sliding mode controller (NSMC) based on a new approach law, and the current loop adopts an improved super spiral structure. At the same time, the compensator of the deviation coupling control structure (NDCS) is optimized by weighted arithmetic mean. Finally, comparative simulation experiments were conducted on the system model using various algorithms. The results show that the deviation coupling control structure based on improved sliding mode control has better anti-interference ability, control accuracy, and synchronization in the synchronous control strategy of a multi-permanent magnet motor drive in a shaftless printing system, which is conducive to the safe and stable operation of shaftless printing systems under multiple working conditions. Full article

Piezoelectric actuators are commonly used in high precision, micro-displacement applications. However, nonlinear phenomena, like hysteresis, may reduce their performance. This article compares several control approaches—based on the combination of sliding mode control and artificial neural networks—for coping with these nonlinearities and improving actuator positioning accuracy and robustness. In particular, it discusses the application of diverse order sliding mode control techniques, such as conventional, twisting algorithms, super-twisting algorithms, and the prescribed convergence law, in combination with artificial neural networks. Moreover, it validates experimentally, with a commercial piezoelectric actuator, the application of these control structures using a dSPACE 1104 controller board. Finally, it evaluates the computational time consumption for the control strategies presented. This work intends to guide the designers of PEA commercial applications to select the best control algorithm and identify the hardware requirements. Full article

This study presents an advanced second-order sliding-mode guidance law with a terminal impact angle constraint, which ingeniously combines reinforcement learning algorithms with the nonsingular terminal sliding-mode control (NTSM) theory. This hybrid approach effectively mitigates the inherent chattering issue commonly associated with sliding-mode control while maintaining high levels of control system precision. We introduce a parameter to the super-twisting algorithm and subsequently improve an intelligent parameter-adaptive algorithm grounded in the Twin-Delayed Deep Deterministic Policy Gradient (TD3) framework. During the guidance phase, a pre-trained reinforcement learning model is employed to directly map the missile’s state variables to the optimal adaptive parameters, thereby significantly enhancing the guidance performance. Additionally, a generalized super-twisting extended state observer (GSTESO) is introduced for estimating and compensating the lumped uncertainty within the missile guidance system. This method obviates the necessity for prior information about the target’s maneuvers, enabling the proposed guidance law to intercept maneuvering targets with unknown acceleration. The finite-time stability of the closed-loop guidance system is confirmed using the Lyapunov stability criterion. Simulations demonstrate that our proposed guidance law not only meets a wide range of impact angle constraints but also attains higher interception accuracy and faster convergence rate and better overall performance compared to traditional NTSM and the super-twisting NTSM (ST-NTSM) guidance laws, The interception accuracy is less than 0.1 m, and the impact angle error is less than 0.01°. Full article

In this paper, a nonlinear sliding-mode super-twisting reaching law algorithm is designed to address the problem of coupling interference under deception attacks and actuator physical faults in USV formations during cooperative mining operations of a USVs-ROVs system. First, a USV model with attacks and disturbances is established, and a leader–follower formation system is designed. Then, based on the reaching law, the state error dynamic chatter can be effectively solved when it is far away from and reaches the sliding surface; a nonlinear sliding super-twisting reaching law is designed to improve the chatter characteristics of the sliding surface. Furthermore, to solve the problems of low fitting accuracy regarding control anomaly information and the difficulty of fending off signal-data interference attacks, a nonlinear saturation fault-tolerant filtering mechanism and a nonlinear fitting factor are designed. Finally, the stability of the algorithm is verified through Lyapunov theory. Under the same coupling deception probability, the nonlinear sliding-mode super-twisting reaching law algorithm designed in this paper enables the leader ship and each follower ship to reach stability within about 12s, and the formation system maintains its formation while also improving the control accuracy of each individual ship. Full article

An improved super-twisting sliding mode and linear active disturbance rejection control strategy is proposed to improve the dynamic response performance and immunity performance in switched reluctance motor speed control systems. Firstly, the linear extended state observer in linear active disturbance rejection control is improved by using the super-twisting sliding mode (STSM) control algorithm in order to improve the performance of the observer and thus enhance the controller’s immunity to disturbances. Secondly, the STSM control algorithm is used to replace the original linear state error feedback control law to improve the dynamic response performance of the controller, and the sigmoid function is used to replace the sign function in the STSM algorithm to further weaken the inherent chattering of the sliding mode and improve the stability of the system. Finally, the proposed control strategy is verified using the MATLAB/Simulink simulation platform. The simulation results show that the proposed control strategy has a better dynamic response and disturbance immunity performance. Full article

This study focuses on designing a controller for trajectory tracking of quadcopters using advanced sliding-mode techniques. Specifically, an integral terminal sliding-mode control based on an adaptive barrier function with a super-twisting reaching law is employed to achieve precise trajectory tracking. The performance of the controller is enhanced by applying Particle Swarm Optimization to fine-tune the gain values. The nonlinear dynamics of the quadcopter are modeled using the Euler–Lagrange approach, followed by a Lyapunov stability analysis to verify the stability of the controller. The adaptive barrier function is used to prevent control signal saturation, while the third-order sliding-mode controller effectively reduces the chattering. Additionally, a saturation function is introduced to further mitigate the chattering effect. The effectiveness of the proposed approach is demonstrated through numerical simulations, and its performance is further validated through controller-in-the-loop implementation. The results show that the proposed method significantly improves trajectory-tracking accuracy. Full article

Due to long-term wear and attitude disturbance caused by shafting friction and other factors, the model parameters of the UAV-mounted optoelectronic platform are transformed, and the control accuracy and robustness of the platform are reduced. The traditional approach involves utilizing disturbance observers to observe disturbance values and subsequently reduce their impact on the system. However, when there is significant uncertainty in the model parameters, the application of this method is constrained. Therefore, a super-twisted sliding mode control based on predictive adaptive law (SSMC + PAL) (SSMPAL) is proposed. Firstly, to adapt to the impact of changes in platform structural parameters on the system and mitigate speed fluctuations, a predictive adaptive law is devised. Subsequently, a super-twisted sliding mode controller(SSMC) was developed, whose high-order performance effectively mitigates the chattering phenomenon associated with traditional sliding mode control strategies and minimizes the impact of observation errors stemming from significant model parameter uncertainties on system control accuracy. The convergence and robustness of the designed control strategy are proven using Lyapunov’s theorem. Finally, the effectiveness of the algorithm is verified using an actual UAV-mounted optoelectronic platform. The step response test results indicate that, compared to the disturbance observer control strategy, this method reduces the overshoot by 7.8% and significantly shortens the response time and transition process, demonstrating its superior dynamic response capability. Subsequent anti-disturbance and robustness tests further highlight the superiority of SSMPAL over disturbance observers in terms of anti-disturbance ability and stability, highlighting its significant engineering application value. Full article

After eliminating the speed sensor in the linear induction motor (LIM) high-performance closed-loop control system, the speed feedback information is missing in the speed closed loop. The accuracy of speed observation results is affected by changes in magnetizing inductance and primary resistance. This effect can cause significant oscillations in the results of the speed sensorless control system, preventing them from converging. An enhanced model reference adaptive system (MRAS) multi-parameter parallel identification methodology based on the interconnected second-order super-twisting algorithm (SOSTA) is proposed. To enhance the system’s dynamic performance, we designed an improvement to the MRAS observer based on the SOSTA, with a focus on the LIM state-space equation that considers dynamic edge-end effects. The impact of parameter alterations on the LIM system is examined. To improve speed observation accuracy and system stability, a two-parameter MRAS identification model was created. The Popov hyperstability principle was used to formulate control laws for these two parameters, ultimately enabling the identification of these two parameters. The identified values were fed back to the speed observation and control system, which reduces the coupling of these two parameters and speed. Simulation and hardware-in-the-loop experiments demonstrate that the observation system estimates speed accurately when these two parameters undergo abrupt changes within the rated speed range, enhancing the precision and robustness of the speed sensorless control system. Full article

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision. Full article

Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches. Full article