To improve the dynamic performance of position tracking in permanent magnet synchronous linear motors, a model predictive position control method based on disturbance observer is proposed. Firstly, a novel neural network enhanced model reference adaptive observer is designed to estimate the lumped disturbance of the system. Taking the estimated disturbance as a new state variable, it is explicitly embedded in the framework of model prediction, which realizes the online estimation and compensation of disturbance, and effectively solves the deterioration of control performance caused by inaccurate system parameters and unknown disturbance in model prediction method. The increment of the control input is used as the input of the prediction equation, which makes the control input smoother and avoids drastic changes. The adaptive gain of the observer is designed by Lyapunov theory and the stability of the system is analyzed. A large number of experiments and analysis are carried out on the tubular permanent magnet linear synchronous motor platform, which proves the effectiveness of the proposed method.

Artificial muscles such as braided pneumatic actuators (BPAs) offer many advantages for robotic systems, including high durability and strength-to-weight ratios. However, their use in robotic systems is still extremely limited, in part due to their poor force, length, and pressure characterization. In this work, a test setup is created to compare force produced by Festo fluidic BPAs with leading models. Our analysis of the data has resulted in (1) the development of new equations to calculate force as functions of pressure and contraction for Festo BPAs with uninflated diameters of 10;20, and (2) a novel equation for the maximum force in 10;20 diameter Festo BPAs as a function of their resting length. This will lead to faster design processes and the development of new systems such as biomimetic robots that are able to more accurately reproduce the range of motion and isometric torque profiles that exist in the animals they are mimicking.

To suppress the severe output torque fluctuations caused by clutch engagement when a hybrid electric vehicle equipped with a dedicated hybrid transmission (DHT) switches from pure electric (E) drive mode to hybrid (H) drive mode, a coordinated control method for power source switching is proposed. First, an adaptive fuzzy proportional-integral (PI) controller regulates the engine speed based on the speed difference between the engine and the P2 motor. Second, an active disturbance rejection control (ADRC) controller is employed for trajectory tracking to eliminate the speed difference across the synchronizer’s friction surfaces. This compensates for clutch torque variations during engine startup and ensures rapid synchronizer engagement. Finally, the torque interruption caused by the decoupling of the engine and P2 motor from the driveline is compensated via feedforward control from the P3 motor. The proposed strategy was validated through MATLAB Simulink simulations and CANape calibration tests. The results indicate that applying the proposed method to E-H mode switching slightly extended the total duration by 0.02 s. However, compared with uncoordinated control, the maximum longitudinal jerk was reduced by 73.8%, and the clutch sliding work decreased by 38.6%. This significantly enhances switching smoothness and prolongs the clutch’s service life.

In this paper, an adaptive-weight model predictive control (AW-MPC) strategy is proposed to address the trajectory tracking problem of a lower-limb rehabilitation exoskeleton robot. First, based on human motion analysis, the dynamics of the lower-limb rehabilitation exoskeleton are established, and the nonlinear dynamic model is transformed into a linear model. Second, a MPC objective function is formulated to minimize the tracking error, yielding the optimal control input. Then, on the basis of conventional MPC, a weight-tuning scheme is developed: a weighting function is constructed according to the evolution of the tracking error to adaptively adjust the MPC weighting coefficients, and the closed-loop stability of the control system is proven via a Lyapunov-based analysis. Finally, the proposed method is validated on a lower-limb rehabilitation exoskeleton experimental platform, with a PID controller designed as a baseline for comparison. The experimental results demonstrate that, compared with the PID controller, the proposed AW-MPC achieves faster convergence of the tracking error, higher tracking accuracy, and enhanced robustness.

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios.

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers (v≤0.3 m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking (v≥1.0 m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers.

To enhance the trajectory tracking accuracy and lateral stability of distributed-drive electric vehicles, a hierarchical cooperative control strategy optimized by the Genetic–Firefly Algorithm (G-FA) is proposed. First, bottom-level controllers for trajectory tracking utilizing a Linear Quadratic Regulator (LQR) and stability relying on Sliding Mode Control (SMC) are jointly optimized offline using the G-FA to address the limitations of empirical parameter tuning and effectively mitigate chattering. Compared to traditional Nonlinear Model Predictive Control (NMPC), which relies on computationally demanding dynamic programming, the proposed G-FA acts as an efficient approximate optimization method that significantly reduces the online computational burden while maintaining high control accuracy. Second, an adaptive cooperative mechanism based on desired yaw rate correction is introduced. By constructing two reference benchmarks—“tracking-oriented” and “stability-oriented”—a cooperative weighting coefficient adapts the fusion of control objectives based on the vehicle’s stability state. Hardware-in-the-loop (HIL) simulation results demonstrate that, under high-adhesion double lane change maneuvers, the proposed strategy reduces peak lateral error and sideslip angle by 31.53% and 28.08%, respectively, compared to traditional LQR. In low-adhesion S-curve limit maneuvers, where traditional LQR fails, the proposed strategy outperforms the NMPC benchmark, further reducing these indices by 61.98% and 8.33%, respectively, significantly improving control performance under extreme conditions.

This study investigates the dynamic behavior of the suction-side lubrication gap between bushing and gear in external gear pumps (EGPs), with emphasis on how surface texturing and bushing micromotion influence the effective stiffness and damping of the oil film. A three-dimensional CFD model of a lubrication gap between bushing and gear is developed to resolve the coupled sliding–squeezing hydrodynamics arising under realistic suction-side operating conditions. Steady-state simulations are used to determine the nonlinear static force–gap height relationship and extract the hydrodynamic stiffness, while transient simulations with harmonic perturbations are post-processed to evaluate the damping coefficient through acceleration-based filtering. The results show that both stiffness and damping increase sharply as the gap height decreases due to the strong confinement of the lubricant in the small-clearance region. Increasing the textured area slightly enlarges the effective gap height and reduces the hydrodynamic load capacity, leading to lower stiffness and damping values; this behavior highlights that the choice of an appropriate texturing configuration is a critical design parameter. Overall, the study provides a comprehensive dynamic characterization of textured bushing–gear lubrication films in EGP and offers quantitative data for developing lumped parameter models of EGP with textured bushings.

Flywheel energy storage systems exhibit superior performance in electric vehicle regenerative braking, railway traction power supply, and grid frequency regulation due to their high instantaneous power and fast dynamic response. However, systems supported by conventional mechanical bearings face severe radial structural coupling; unbalanced excitation and gyroscopic effects drastically amplify vibrations during critical speed traversal, undermining operational reliability and engineering scalability. To tackle this challenge, this paper proposes a full-speed vibration suppression scheme for active–passive supported flywheel energy storage systems integrated with a damping ring, combined with an inverse system decoupling controller to eliminate structural coupling, unbalance-induced vibration, and gyroscopic effects. A dynamic model of the integrated system is established using Lagrange’s equations, and four-degree of freedom decoupling expressions are derived to achieve complete radial decoupling. A speed-stage-based control strategy is further developed for full-speed adaptation. Comprehensive simulations validate the scheme’s decoupling performance, vibration suppression efficacy, and robustness. Results demonstrate that the proposed controller achieves full radial decoupling, reducing the average steady-state tracking error by 99.86%. The segmented control enables stable operation across 100–20,000 rpm and cuts critical speed resonance peaks by 81.23%. Compared with pure mechanical and magnetic bearing systems, the integrated active–passive support reduces resonance peaks by 94.72% and 42.25%, respectively. Under current perturbation and parameter variation, the scheme reduces the average steady-state error by 75.89% relative to the coupled system, confirming its strong engineering applicability.

As manufacturing progresses, the demand for precision inspection of complex parts has intensified. To guarantee functionality and sensory performance, high-efficiency 3D shape measurement is required. In this paper, a collaborative robot-based approach for efficient and high-precision 3D shape inspection of complex parts is proposed. The system employs a collaborative robot to drive the scanner along optimized trajectories. First, the configuration of the inspection system is presented, and the ideal measurement mode for the sensor is analyzed. Subsequently, adaptive viewpoints are generated through parametric discretization based on surface geometric features. For inter-region scanning path planning, the problem is modeled as the Shortest Path Problem (SPP) within the framework of the Traveling Salesman Problem (TSP) and solved by constructing a Successive Approximation Algorithm (SAA). Furthermore, a Modified Denavit-Hartenberg (MDH) method is applied to establish the precise kinematic model of the collaborative robot. Inverse kinematics solutions are derived to convert planned viewpoints into target joint configurations, thereby achieving precise end-effector pose control. Simulation and experimental results on an engine cover and a cylinder head demonstrate that the proposed approach enables comprehensive 3D shape inspection of complex parts in a single setup and achieves higher efficiency and accuracy compared to existing methods. This work offers a viable solution for integrating robotic actuation and active sensing in the automated inspection of complex geometries.