Search Results (1,370)

Simulation-based reinforcement-learning locomotion depends on the physical fidelity of the underlying model. This work presents a subsystem-level modelling, validation, and control framework for a 12-DoF biped robot, combining Denavit–Hartenberg kinematics, Euler–Lagrange dynamics, a Discrete Euler–Lagrange reference integrator, Hunt–Crossley contact, and Soft Actor-Critic training in PyBullet. Validation is scoped. For the fixed-hip leg, numerical damped-least-squares inverse kinematics achieved a round-trip error of 0.017 ± 0.022 mm, while a gravity path-integral test produced a residual of 0.006 J. On a one-DoF reference problem, DEL bounded energy error under a coarse-step stress test, whereas at the 1 ms training step, RK4 was more accurate; no RL-scale DEL advantage was established. Contact realism remained inconclusive because the available prescribed-penetration analysis was not a dynamically consistent whole-body impact test. The same nominal parameters were used in PyBullet for locomotion training, without establishing numerical equivalence between the two simulators. Across three asymmetric-reward runs, forward walking dominated final evaluations, but sustained velocity ranged from 0.62 to 1.24 m/s under unequal training budgets. An exploratory hybrid architecture reached 2.38 m/s in one run without controlled ablation. These results demonstrate subsystem-level diagnostics while identifying full-body validation, contact calibration, equal-budget replication, and architectural ablation as necessary future work. Full article

To address the coupling between the vertical and pitch motions of a vehicle body and the difficulty of suppressing instantaneous large impact under combined road- and operation-induced excitations, a five-degree-of-freedom (5-DOF) half-vehicle dynamic model of a two-axle wheeled engineering vehicle with a robotic arm is developed, and an ADF-PI∪D composite control algorithm is proposed. The proposed algorithm decouples the vertical and pitch motions of the vehicle body through acceleration-difference feedback (ADF) and integrates PI∪D control with fuzzy adaptive gains to effectively suppress the vehicle body’s vibration under combined excitations. Numerical simulation results demonstrate that the proposed ADF-PI∪D control achieves significant improvements over the passive suspension under three representative operating conditions, namely instantaneous large impact load, three-bump road excitation, and Class-C random road excitation. Specifically, the body vertical acceleration is reduced by 87.04%, 72.42%, and 50.08%, respectively, while the pitch angular acceleration is reduced by 96.05%, 96.01%, and 91.60%, respectively. These results indicate that the proposed algorithm can effectively decouple the vertical and pitch motions while significantly suppressing vehicle-body vibration across all considered operating conditions, providing a feasible approach for attitude-stability control of active suspension systems in wheeled engineering vehicles. Full article

As a core fluid device, the triplex diaphragm hose pump is prone to unstable operation owing to its complex internal flow field. In this study, a dynamic mesh technique is established as the core of the numerical model. The periodic flexural deformation of the hose wall is prescribed through a user-defined function (UDF) with a Gaussian distribution, which drives the mesh motion and realizes the one-way fluid–structure interaction (FSI) between the hose and the fluid; the opening and closing of the ball valve is simulated by the dynamic mesh combined with the gap model and the six-degree-of-freedom (6DOF) solver, realizing the two-way FSI between the valve and the fluid. The results reveal intense turbulence (with a turbulent kinetic energy of 0.1–0.5 ${\mathrm{m}}^2/{\mathrm{s}}^2$) at the valve gaps and vortices downstream of the valves. Owing to the positional effects, the three pump heads exhibit uneven flow distribution: within 1 s (two working cycles), the cumulative conveyed masses of pumps a, b, and c are 0.02258, 0.02299, and 0.02283 kg, respectively, differing by up to about 1.8%. The parametric analysis shows that a smaller inlet manifold diameter enlarges the flow-rate differences among the pump heads: as the diameter increases from 25 mm to 100 mm, the mean manifold velocity decreases from about 2.91 m/s to 0.66 m/s and the system pulsation rate ${\delta }_{q1}$ decreases from 0.408 to 0.280, while the improvement tends to become marginal beyond approximately 100 mm. Furthermore, as the hose deformation amplitude increases from 0.006 m to 0.020 m, the mean manifold velocity increases linearly from 0.299 m/s to 0.993 m/s, while the normalized pulsation rate ${\delta }_{q1}$ decreases from 0.309 to 0.139. These findings provide a quantitative basis for the design and performance control of the triplex diaphragm hose pump. Full article

This paper addresses the snaking instability of distributed electric-drive articulated vehicles under different speed conditions. A 7-DOF nonlinear vehicle model is established, and separate ideal reference models are constructed for the front and rear bodies. The particle swarm optimization is employed for offline optimization of the LQR weighting matrices, and a fuzzy adaptive PSO-LQR lateral stability control strategy is proposed. In this strategy, a fuzzy scheduling mechanism using vehicle speed and yaw rate as inputs is designed to adjust the LQR gains. Furthermore, a series of comparative simulations are conducted in this paper, and the simulation results are obtained under a fixed-speed sweep. Compared with the three-segment fixed-parameter LQR, the proposed method maintains lower articulated angles. At 11 m/s, the peak articulated angle is reduced by approximately 20%. On roads with adhesion coefficients of 0.2 and 0.8, fixed-parameter control suffers severe performance degradation. The proposed strategy maintains suppression performance on both friction surfaces. Comparative analyses with PSO linear interpolation and speed-only fuzzy control confirm the necessity of incorporating yaw rate as a second input. Further comparison with interval type-2 fuzzy control shows that both methods deliver comparable steady-state performance. The proposed method reduces computation time by approximately 56.1%. Full article

This paper presents the design of a control strategy based on exact feedback linearization and dynamic stabilization for the mathematical model of a benchmark Quanser platform emulating the elevation, pitch, and travel motions of a three-degree-of-freedom (3-DOF) tandem helicopter. The orientation dynamics of the aerial vehicle are described by a set of highly coupled, inherently unstable nonlinear differential equations. Unlike conventional feedback linearization approaches, the core contribution of this work lies in a strategic subsystem decoupling technique combined with a symmetric input transformation and a formal proof of exact linearizability. Once the exact linear canonical representation is derived, optimal Linear Quadratic Regulator (LQR) control gains are synthesized directly within the transformed coordinates. Subsequently, a trajectory tracking scheme is developed for each degree of freedom, simulating aggressive trajectory changes that emulate obstacle-avoidance maneuvers during a mission. Numerical simulations in MATLAB/Simulink demonstrate that the proposed control scheme achieves precise tracking and rapid stabilization while strictly adhering to the physical angular bounds of the experimental platform and operational actuator constraints. Full article

Conventional five-degree-of-freedom (5-DOF) magnetic bearing supports often require a large axial span, while highly integrated magnetic circuits can introduce axial–radial flux coupling through shared return paths. This study proposes a compact permanent-magnet-biased 5-DOF magnetic bearing for flywheel energy storage systems, integrating two radial support sections and one axial support unit within a common stator. A series-opposed compensation winding is introduced to suppress axial-control-induced leakage into the radial branch. An electromagnetic design procedure considering leakage, ampere-turns, and magnetic-saturation constraints is evaluated using a three-dimensional magnetostatic finite-element model. At 6 A, the modeled axial and radial forces reach approximately 1.50 and 0.75 kN, respectively, while the maximum ferromagnetic flux density remains below 1.25 T. Under permanent-magnet-biased operation, the compensation winding reduces the peak radial-field deviation from approximately 161.5 to 1.7 mT, corresponding to approximately 99.0% suppression; the full-path RMS-deviation metric indicates approximately 93.1% suppression. Among the investigated configurations, the one with 50 turns provides the closest restoration to the bias-only radial field. Prototype tests demonstrate stable five-channel closed-loop static suspension, supporting physical realizability, but do not directly validate the predicted decoupling ratios. Full article

Wrist rehabilitation requires high precision, haptic transparency, and accurate alignment with the human joint’s physiological center of rotation. Conventional robotic systems often suffer from high moving inertia or joint misalignment. This paper presents the design and kinematic validation of a novel 3-DOF spherical parallel exoskeleton featuring base-fixed actuators. By mounting all actuators to a fixed base, the proposed architecture significantly reduces moving mass, achieving a low-inertia response critical for safe patient–robot interaction. The “virtual center” concept eliminates physical central joints, enabling a compact design completed by the user’s anatomy. To perform the kinematic and singularity analyses of the manipulator, two distinct models were used: Rotated Frame Based (RFB) and Initial Frame Based (IFB). Performance metrics, namely the manipulability index and condition number, are evaluated to assess dexterity and isotropy. Comparative kinematic analysis of Rotated (RFB) and Initial Frame Based (IFB) models confirm ideal central isotropy ($\kappa =1.0$). While RFB yields 94.14% high-dexterity ($\kappa <5.0$) and 99.78% usable ($\kappa <10.0$) workspace coverage, IFB achieves 78.80% high-dexterity ($\kappa <5.0$) and 92.18% usable ($\kappa <10.0$) workspace coverage. Analytical manipulability metrics validate singularity-free motion throughout the anatomical range. Additionally, the use of exponential rotational matrices in this paper provides systematic derivations of equations in compact form. Full article

This simulation study evaluates a current-aware local planner for a circular planar remotely operated vehicle model in known static maps with time-varying currents. PFPA-v2 combines bounded attraction, threat-weighted repulsion, current-adjusted local commands, and prevalidated connected-cluster escape. A common continuous audit applies the same obstacle-body and inset-boundary clearance criterion to planned and executed trajectories, while a planar 3-DOF LOS–PI layer separates reference-path behavior from closed-loop tracking. In a frozen $2^4$ planning factorial over 60 map–current scenarios, PFPA-v2-primary yielded 54/60 safe references versus 44/60 with the four tested components disabled. The paired integration-level difference was significant, whereas no individual main or two-factor effect survived multiplicity correction. In an independently tuned common-clearance comparison, CA-Informed RRT* showed higher observed reliability and shorter conditional paths, whereas PFPA-v2-P09 produced greater obstacle clearance and much lower recorded desktop planning time; map-cluster intervals did not support a planner-family ranking. Current-aware execution was more reliable than no-current-feedforward execution in the unified PFPA-v2-primary chain. Moderate-current and affine-remapped observational-current studies were exploratory. The results support an interpretable simulation benchmark, not global completeness, six-degree-of-freedom feasibility, or field-scale ROV validation. Full article

Rehabilitation exoskeletons require supervisory control that can preserve globally safe operating modes while executing local therapeutic behaviors reactively. Pure Finite-State Machine (FSM) implementations provide explicit mode supervision but become increasingly coupled as behaviors and recovery transitions are added, whereas Behavior Trees (BTs) provide modular reactive execution but do not inherently encode a unique global operating mode. This work presents an application-specific hierarchical FSM–BT architecture for the SARA passive–active lower-limb rehabilitation platform. The novelty is not the generic combination of FSMs and BTs; rather, it lies in the rehabilitation-oriented separation of global therapeutic-mode supervision and event-priority safety arbitration from parameterizable BT execution and local recovery before escalation. The architecture was evaluated exclusively in a custom Python Software-in-the-Loop (SIL) environment using an eight-degree-of-freedom second-order joint model, a 100 Hz plant integration rate, a 10 Hz supervisory FSM, and 50 Hz BT execution. Thirty Monte Carlo trials were performed for each of four scenarios and for three functionally equivalent architectures (FSM-only, BT-only, and FSM–BT), for 360 comparative trials with a fixed random seed. The proposed FSM–BT architecture achieved a 98.3% overall trial-success rate, compared with 89.2% for FSM-only and 92.5% for BT-only. Its mean software-level unsafe-condition-to- Emergency Stop response was 71.9 ms, significantly lower than the 159.7 ms FSM-only response ($p<{10}^{-8}$, paired Wilcoxon test), while BT-only produced the same safety timing because it used the same 50 Hz local evaluation cadence. The active-assistance model yielded a mean eight-DOF angular RMSE of 2.42°. A structural extension study further required 54 edit operations to add six therapeutic routines to FSM–BT, versus 60 for BT-only, 96 for a hierarchical state-machine baseline, and 138 for FSM-only. Full article

Humanoid robots are highly susceptible to structural damage during irrecoverable falls due to high landing velocity, short impact duration, and high peak impact force. Inspired by human protective strategies, namely instinctive postural adjustment and soft-tissue energy absorption, this paper proposes a passive–active cooperative fall-protection method that combines pre-impact motion regulation with post-impact structural energy absorption. On the passive protection side, high-risk contact regions are identified through multi-directional fall simulations, and a multi-region, multilayer protective suit is optimized considering impact energy absorption, peak-force reduction, anti-bottoming safety, added mass, and thickness constraints. On the active protection side, a variable height inverted pendulum (VHIP) model is used to optimize the center of pressure and center of mass trajectories, reducing the terminal impact energy before ground contact. The residual impact energy is then matched with the absorption capacity of the passive protective layers, forming a unified framework that integrates pre-impact motion unloading and post-impact energy absorption. Numerical validation is performed on a MATLAB–CoppeliaSim co-simulation platform, and physical experiments are conducted on the FCR humanoid robot (approx. 50 kg, 1.65 m, 22 DOF). Compared with the unprotected case, the proposed method reduces the peak equivalent impact force from 4819.1 N to 1038.2 N, i.e., a reduction of 78.5%, demonstrating its effectiveness in attenuating impact loads and enhancing protection capability. Full article

This paper presents the kinematic design and experimental validation of a two-degree-of-freedom ankle mechanism based on stepper motor actuation and ball-screw transmission. The proposed system employs a hybrid architecture, combining actively controlled motion in the sagittal plane with passively compliant motion in the frontal plane. Experimental evaluation under no-load laboratory conditions demonstrated a strong linear relationship between motor steps and joint angle within an operating range of approximately ±22°, with coefficients of determination exceeding 0.97. The results confirm the predictability and repeatability of the kinematic transformation while revealing minor hysteresis effects associated with mechanical transmission. The proposed mechanism is intended as a validation platform for studying motion transformation in multi-DoF (degree-of-freedom) ankle systems, providing a basis for future work on load analysis, torque modeling, and closed-loop control. Full article

To address the problems of poor steering smoothness and handling stability for four-wheel independent steering (4WIS) vehicles, this paper presents an adaptive nonlinear model predictive control (ANMPC) strategy based on variable steering ratio (VSR). First, a two-degree-of-freedom (2-DOF) vehicle and reference model are established to provide a theoretical basis for controller design. Second, the Hermite interpolation polynomial is used to achieve a smooth transition design of the four-stage VSR. By optimizing the steering ratio curve, the steering smoothness and flexibility across the entire speed range are significantly improved. Furthermore, innovatively, the hyperbolic tangent function (tanh) is employed in combination with the μ-V adaptive weight optimization method to construct an adaptive weight adjustment mechanism for the ANMPC controller. To assess the efficacy of the control strategy, co-simulation experiments are performed using CarSim/Simulink. Simulation results demonstrate that versus conventional MPC, the developed algorithm achieves a 37.75% reduction in yaw rate dynamic response RMSE and a 4.61% decrease in lateral velocity RMSE during low-adhesion double lane-change maneuvers, significantly boosting handling stability and steering smoothness in 4WIS vehicles. The VSR-ANMPC control strategy enhances steering performance substantially while demonstrating exceptional handling stability control across diverse speed and adhesion conditions. Full article

This paper proposes a hybrid integral sliding mode and finite-time control (ISMC–FTC) strategy for a two-degree-of-freedom (2DOF) robotic manipulator, targeting finite-time stability and high-precision elliptical trajectory tracking. To ensure practical deployment, the controller explicitly enforces actuator saturation limits, joint kinematic bounds, workspace constraints, and prescribed stabilization time requirements. Particle swarm optimization (PSO) is employed to tune the FTC parameters, minimizing convergence time while guaranteeing constraint satisfaction. By integrating ISMC’s inherent robustness against matched disturbances with a PSO-optimized FTC, the scheme eliminates the reaching phase and ensures rapid, finite-time convergence. The simulation results demonstrate that the proposed approach significantly reduces stabilization time for both joints, maintains exceptionally low tracking errors, and enforces all physical constraints under bounded disturbances and model uncertainties through explicit saturation limits and sliding manifold invariance. These results validate the framework’s effectiveness and highlight its potential for safety-critical, high-precision robotic applications requiring guaranteed finite-time performance. Robustness is further validated under simultaneous disturbances and uncertainties, where the controller maintains stable performance and consistently satisfies the required robust stability condition. Full article

Concentrating on a large-scale motion simulator, this article proposes the cable-driven parallel mechanism (CDPM), whose virtues are simplified geometry, being lightweight, and having a large workspace, high workload, and fast dynamic performance. To meet engineering requirements for motion simulation, we devise several CDPM configurations and establish a systematic selection procedure. The target configuration is determined based on several constraints, including the simulator’s installation space, the end-effector’s shape and load, and the required degrees of freedom (DOFs) and motion range. We calculate the wrench-closure workspace (WCW) and wrench-feasible workspace (WFW), incorporating the range of posture variations into their volume calculations. These workspaces serve as key criteria for configuration selection. Based on the WFW, we optimize the distal attachment points of the cables connected to the end-effector and adjust the minimum and maximum cable tension constraints. This optimization provides the basis for selecting the cable and motor models and ultimately establishes the optimal configuration. The proposed method encompasses the entire process, from defining large-scale motion requirements to determining the final parameters of the simulator. Full article

Passive magnetic localization for wireless capsule endoscopy degrades under involuntary gastrointestinal motion: displacement toward one sensor layer simultaneously saturates near-side magnetometers while driving far-side sensors below the noise floor, a failure mode we term asymmetric layer visibility. Existing single-layer and multi-layer systems do not model this explicitly, and full-array Jacobian conditioning degrades progressively with displacement even where a layer-conditioned decomposition remains comparatively stable. We present a two-layer magnetometer array with three algorithmic contributions: per-sensor SNR gating excluding saturated or low-SNR sensors before optimization; layer-conditioned Levenberg–Marquardt estimation on a five-dimensional manifold via spherical parameterization; and SNR-weighted fusion with geodesic interpolation on $S^2$, combined with layer-wise ellipsoid calibration and neural network initialization. Eight LIS3MDL tri-axial magnetometers in a $15\times 15\times 30$ cm³ two-layer array were validated across 150 Monte Carlo trials using a 4-DOF robotic arm. Mean positional error was $2.32\pm 0.35$ mm and orientational error $2.15\pm 0.88°$ at 12.4 Hz, with 93.85% convergence success under displacement-induced asymmetric visibility events of 1.00–4.00 cm—over 70% reduction over single-layer and layer-agnostic baselines. Hardware generalization was confirmed across three magnetometer types. The SNR weight ratio also yields a passive discriminant between capsule displacement and global array motion, cross-validated AUC $0.924\pm 0.045$, requiring no additional hardware. Reported accuracy is specific to the LIS3MDL magnetometer used in this study. Full article