Search Results (107)

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains $(k_0^{*},k_1^{*})$ over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective $\tilde{J}$ constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 ($7.36\times {10}^{-5}$ rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 ($8.18\times {10}^{-3}$ rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints ($7.81\times {10}^{-5}$ rad, $8.39\times {10}^{-3}$ rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article

Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which constrains their ability to reproduce realistic breathing dynamics. To overcome these limitations, this study presents a dual-chamber lung simulator that can operate in both active and passive modes. The system integrates a sliding mode controller enhanced by a linear extended state observer, enabling the accurate replication of complex respiratory patterns. In active mode, the simulator allows for the precise tuning of respiratory muscle force profiles, lung compliance, and airway resistance to generate physiologically accurate flow and pressure waveforms. Notably, it can effectively simulate pathological conditions such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) by adjusting key parameters to mimic the characteristic respiratory mechanics of these disorders. Experimental results show that the absolute flow error remains within ±3 L/min, and the response time is under 200 ms, ensuring rapid and reliable performance. In passive mode, the simulator emulates ventilator-dependent conditions, providing continuous adjustability of lung compliance from 30 to 100 mL/${\mathrm{cmH}}_2$O and airway resistance from 2.01 to 14.67$\sqrt{{\mathrm{cmH}}_2\mathrm{O}}/(\mathrm{L}/\mathrm{s})$, with compliance deviations limited to ±5%. This design facilitates fine, continuous modulation of key respiratory parameters, making the system well-suited for evaluating ventilator performance, conducting human–machine interaction studies, and simulating pathological respiratory states. Full article

This paper extends one of the nonswitching-type reaching laws for discrete-time sliding mode control. The control task under consideration is the regulation of the grid current of the grid-following power inverter. A mathematical model of a plant is presented as an example of a microgrid. This system contains a T-type inverter, LCL filter, DC source, power grid connection and control system. The system tests were performed in a simulation environment. First, the methods known in the literature for implementing continuous sliding mode control are presented for the described problem, including stability analysis and implementation. Secondly, the well-known discrete sliding mode control algorithm based on the nonswitching reaching law type is discussed. The main part of this article consists of a proposed modification to the above algorithm. We consider the use of two separate regulation mechanisms: an adaptation of a specific control law parameter responsible for limiting the control signal and a mechanism for reducing the steady-state error. The aim of these procedures is to increase the quality of control, which, in turn, leads to an increase in the quality of energy transmitted in grids. The stability analysis is presented, as well as the simulation results. Finally, the results of all methods are compared and discussed, with conclusions drawn. Full article

Addressing escalating demands for high-precision compact robotic actuators, this study overcomes persistent challenges from nonlinear transmission dynamics and computational constraints through a co-designed framework integrating three innovations. A real-time second-order characteristic modeling approach enables 10 kHz online parameter identification, reducing computational load by 13.1% versus MPC. Building on this foundation, a hybrid integral sliding-mode controller eliminating modeling errors while maintaining ≤0.25 rad/s tracking error (SRMSE) under variable loads was created. These algorithmic advances are embedded within a miniaturized dual-ARM platform (47 × 47 × 12 mm³) achieving <30-ns overcurrent protection and 36% cost reduction versus DSP/FPGA solutions. Validated via Lyapunov stability proofs and experiments, this framework is particularly effective for high-performance robotic joint control in spatially- and thermally-constrained environments while dynamically compensating for unmodeled nonlinearities. Full article

In practical applications, most systems operate based on digital signals obtained through sampling. Applying fractional-order control to spacecraft attitude control is meaningful for achieving better performance, especially in the coordination of the multi-spacecraft attitude system. In this paper, a discrete-time fractional-order sliding mode attitude control problem is studied for multi-spacecraft systems based on the fully actuated system approach. Firstly, a discrete-time disturbance observer based on the fractional-order theory is constructed to estimate the disturbance. Secondly, a discrete-time fractional-order sliding mode controller is designed by combining the transformed fully actuated discrete-time system and the disturbance observer. Subsequently, every spacecraft can track the desired attitude under the designed controller. Finally, the simulation results show that the developed control method achieves faster convergence, smaller overshoot, and higher control accuracy. Full article

This paper tackles the challenge of achieving robust and precise control for a novel quadrotor featuring continuously variable arm lengths (15 cm to 19 cm), enabling enhanced adaptability in complex environments. Unlike conventional fixed-geometry or discretely morphing unmanned aerial vehicles, this design’s continuous structural changes introduce significant complexities in modeling its time-varying moment of inertia. To address this, we propose a control strategy that decouples dynamic motion from the evolving geometry, allowing for the development of a robust control model. A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. Extensive MATLAB 2016 simulations validate the proposed approach, demonstrating superior tracking accuracy in both fixed and variable arm-length configurations, achieving root mean square error values of 0.05 m (fixed arms), 0.06 m (variable arms, path 1), and 0.03 m (variable arms, path 2). Notably, the PSO-tuned SMC controller reduces tracking error by 30% (0.07 m vs. 0.10 m for PID) and achieves a 40% faster settling time during structural transitions. This improvement is attributed to the PSO-optimized SMC parameters that effectively adapt to the continuously changing inertia, concurrently minimizing chattering by 10%. This research advances the field of morphing UAVs by integrating continuous geometric adaptability with precise and robust control, offering significant potential for energy-efficient flight and navigation in confined spaces, as well as applications in autonomous navigation and industrial inspection. Full article

The permanent magnet synchronous motor (PMSM) is a critical device that converts kinetic energy into mechanical energy. However, it faces issues such as nonlinearity, time-varying uncertainties, and external disturbances, which may degrade the system control performance. To address these challenges, this paper proposes a prescribed performance model-free adaptive fast integral terminal sliding mode control (PP-MFA-FITSMC) method. This approach replaces conventional techniques such as parameter identification, function approximation, and model reduction, offering advantages such as quantitative constraints on the PMSM tracking error, reduced chattering, strong disturbance rejection, and ease of engineering implementation. The method establishes a compact dynamic linearized data model for the PMSM system. Then, it uses a discrete small-gain extended state observer to estimate the composite disturbances in the PMSM online, effectively compensating for their adverse effects. Meanwhile, an improved prescribed performance function and error transformation function are designed, and a fast integral terminal sliding surface is constructed along with a discrete approach law that adaptively adjusts the switching gain. This ensures finite-time convergence of the control system, forming a model-free, low-complexity, high-performance control approach. Finally, response surface methodology is applied to conduct a sensitivity analysis of the controller’s critical parameters. Finally, controller parameter sensitivity experiments and comparative experiments were conducted. In the parameter sensitivity experiments, the response surface methodology was employed to design the tests, revealing the impact of individual parameters and parameter interactions on system performance. In the comparative experiments, under various operating conditions, the proposed strategy consistently constrained the tracking error within ±0.0028 rad, demonstrating superior robustness compared to other control methods. Full article

Owing to their high performance and high-efficiency controllability, surface-mounted permanent magnet synchronous motors (SPMSMs) have been widely considered for various robotic systems. The conventional three-vector-based model predictive torque control (MPTC) is frequently applied to SPMSMs, while the adjustment of weight factors is difficult. Compared with the five-segment sequence output method, the three-segment sequence output method can effectively reduce the switching frequency. However, the three-segment sequence output method leads to large torque and stator flux ripple. For these issues, a three-vector-based smart MPTC method based on the optimal vector sequence optimized by a genetic algorithm is proposed. Firstly, the reference voltage vector output from the discrete-time sliding mode (DTSM) current controller is utilized to simplify the process of selecting the vectors, and it can enhance the robustness of the SPMSM system. Secondly, an improved cost function is employed to select the optimal vector sequence, aiming to minimize torque and flux ripple. Furthermore, the multi-objective genetic algorithm is leveraged to seek the Pareto solution for weight factors. As a final step, the efficacy of the designed MPTC approach is confirmed through simulations and experiments. Full article

As operational scenarios become more complex and task demands intensify, the requirements for the intelligence and automation of manipulators in industry are increasing. This work investigates the challenge of posture tracking control for hydraulic flexible manipulators by proposing a discrete-time integral terminal sliding mode predictive control (DITSMPC) method. First, the proposed method develops a second-order dynamic model of the manipulator using the Lagrangian dynamic strategy. Second, a discrete-time sliding mode control (SMC) law based on an adaptive switching term is designed to achieve high-precision tracking control of the system. Finally, to weaken the influence of SMC buffeting on the manipulator system, the predictive time domain function is integrated into the proposed SMC law, and the delay estimation of the unknown term in the manipulator system is carried out. The DITSMPC scheme is derived and its convergence is proven. Simulation experiments comparing the DITSMPC scheme with the classical discrete-time SMC method demonstrate that the proposed scheme results in smooth torque changes in each joint of the manipulator, with the integral of torque variations being $5.22\times {10}^3$. The trajectory tracking errors for each joint remain within ±0.0025 rad, all of which are smaller than those of the classical scheme. Full article

This study presents an adaptive sliding mode predictive control (ASMPC) algorithm intended to improve the control precision and robustness of path tracking for wheeled agricultural vehicles. Firstly, the kinematics state equations of the vehicle were established based on path tracking errors. Secondly, in order to design the path tracking controller by combining the precision advantage of model predictive control (MPC) algorithm with the robustness advantage of sliding mode control (SMC) algorithm, the sliding mode functions were designed and used as the output equations to establish the kinematics state space model of the vehicle. Thirdly, on the basis of linearization and discretization for the kinematics state space model, the control law of path tracking was obtained using the MPC algorithm. Finally, according to the fuzzy rules designed by the working speed of the vehicle and the curvature of the reference path, the prediction horizon and control horizon of the MPC algorithm were adaptively adjusted to further improve the control precision and robustness of the path tracking system. The results of CarSim and MATLAB/Simulink co-simulation show that the proposed ASMPC algorithm is superior to the traditional SMC algorithm and conventional MPC algorithm in terms of control precision, dynamic performance, and robustness. The results of our field test show that the root mean square (RMS) values of the lateral errors for straight path tracking and curve path tracking do not exceed 2.1 and 8.7 cm, respectively, in the speed range of 1.0 to 3.5 m/s, suitable for field working. The control precision and robustness of the proposed ASMPC algorithm can meet the working requirements of wheeled agricultural vehicles. Full article

In order to improve the imaging quality of the vehicle photoelectric servo system, image motion compensation under the vehicle platform is studied. Based on the principle of image motion compensation, combined with coordinate system transformation and velocity vector decomposition, the angular velocity compensation formula of a fast mirror in dynamic scanning imaging of a vehicle photoelectric servo system is obtained. A discrete sliding mode control algorithm based on the Kalman filter is proposed. The proposed algorithm and the discrete sliding mode control algorithm are simulated and compared to verify the system control performance. The simulation results show that the designed algorithm improves control accuracy by 76.3%, reduces overshoot by 75%, and improves response time by 31.25% compared with the discrete sliding mode control algorithm. The experimental platform is built to verify the experimental results. The experimental results show that the speed stability accuracy of the fast mirror is better than 19 μrad, which is 74.37% higher than that of the traditional control scheme. This study provides a reference for the follow-up study of image motion compensation in a vehicle photoelectric servo system. Full article

The layered soil crushing rotary tillage machine with L-shaped reclamation rotary blades and rotary-reclamation rotary blades combination was designed to deal with the problems of a low soil fragmentation rate, low straw mulching rate, and poor surface leveling after plowing in the traditional rotary tiller tillage mode in the coastal saline land of the Yellow River Delta. A dual active layered soil fragmentation tillage mode was proposed, and the key structural parameters, blade axis arrangement, and spatial layout of L-shaped reclamation rotary blades and rotary-reclamation rotary blades were determined based on the sliding cutting principle analysis. A discrete element model of soil straw tillage component aggregates suitable for coastal saline alkali land was constructed using EDEM, and the influence of L-shaped reclamation rotary blades and rotary-reclamation rotary blades on the soil tillage layer displacement performance and straw burial performance of saline alkali land was comprehensively analyzed from a microscopic perspective. Taking the rotation speed of the L-shaped reclamation rotary blades, the rotation speed of the rotary-reclamation rotary blades, and the forward speed as experimental factors, and using soil fragmentation rate and straw burial rate as evaluation indicators for experimental optimization analysis, the optimal parameters were obtained: the rotation speed of the L-shaped reclamation rotary blades was 295.04 r/min, the rotation speed of the rotary-reclamation rotary blades was 359.06 r/min, and the forward speed was 3.12 km/h. At this time, the theoretical soil fragmentation rate of saline alkali land was 94.67%, and the straw burial rate was 93.56%. Field experiments have shown that the average soil fragmentation rate of the L-shaped reclamation rotary blades and rotary-reclamation rotary blades combined layered soil crushing rotary tiller after cultivation is 94.37%, the straw burial rate is 95.68%, the surface flatness is 25.82 mm, and the stability of the tillage depth is 95.64%. The machine has shown increased performance in comparison to traditional single axis rotary tillers, meeting the needs of crop bed preparation in saline alkali land. Full article

This paper presents a cross-coupling control strategy that enhances sliding mode control by incorporating active disturbance rejection control. This approach effectively addresses the issue of inadequate synchronous control accuracy in a dual-motor servo system subjected to high load disturbances. Firstly, a mathematical model of a single motor is established, and a discrete sliding mode controller (DSMC) is designed to enhance the motor’s response speed and dynamic performance. Secondly, the approach rate is optimized to improve the control smoothness of the single-motor controller, and the system’s stability is demonstrated using the Lyapunov theorem. In addition, to enhance the precision and stability of synchronous control when the load is unevenly distributed on both sides of the motor, a discrete nonlinear tracking differentiator (DNLTD) and a discrete nonlinear extended state observer (DNLESO) based on active disturbance rejection control (ADRC) theory are proposed, which are, in turn, combined with nonsingular fast terminal sliding mode control (NFTSMC), utilizing an optimized approach rate to form the ADRC-NFTSMC control strategy, and the cross-coupled control structure is used to achieve synchronous closed-loop control. Finally, the experimental results demonstrate that, compared to the NFTSMC strategy, the proposed control strategy improves response speed by 18.9% and synchronous control accuracy by 46.7%, which significantly enhances the quality of dual-motor servo control. Full article

This paper presents a quaternion-based robust sliding-mode controller for quadrotors operating under significant wind disturbances. The proposed control method improves the reliability and efficiency of quadrotor control by eliminating the singularity problem inherent in the Euler angle method. The quadrotor dynamics and wind environment are modeled, and dynamic analysis is performed via numerical simulation. A realistic wind model is used, similar to a combination of deterministic and statistical models. The Lyapunov stability theory is utilized to prove the convergence and stability of the proposed control system. The simulation results demonstrate that the quaternion-based controller enables the quadrotor to follow the desired path and remain stable, even under external wind disturbances. Specifically, both position and attitude converge to the desired values within 10 s, demonstrating stable performance despite the challenging wind disturbances in both scenarios. Scenario 1 features turbulence with an average wind speed of 12 m/s and changing wind directions, while Scenario 2 models an environment with wind speeds that change abruptly and discretely over time, coupled with temporal variations in wind direction. Additionally, a comparative analysis with the conventional PD controller highlights the superior performance of the proposed RSMC controller in terms of trajectory tracking, stability, and energy efficiency. The rotor speeds remain within a reasonable and hardware-feasible range, ensuring practical applicability. Full article

In this paper, a discrete-time design of a fractional internal model-based repetitive controller with a sliding mode approach is presented for uncertain linear systems subject to repetitive trajectory and periodic disturbance. The proposed algorithm, named a fractional delay-based repetitive sliding mode controller (FD-RSMC), aims to enhance tracking accuracy, transient response, and robustness against parametric variations beyond what is offered by conventional repetitive controllers. First, a fractional delay-based repetitive controller (FD-RC) that allows the periodic delay steps to be noninteger is presented to improve the trajectory tracking accuracy and good disturbance compensation of multiple periodic signals. Second, a sliding mode control (SMC) with a discrete-time reaching law is systematically incorporated into FD-RC to improve transient response, especially during the learning period of FD-RC, and also to provide system robustness against model uncertainties. Finally, the stability proof of the closed-loop system with the proposed controller is assessed based on a delayed-sliding mode-reaching condition. Finally, comparative simulation studies are presented to demonstrate the superior performance of the proposed controller. Full article