Contact-based industrial inspection requires aerial platforms to maintain stable interaction while rejecting disturbances. Underactuated aerial manipulators present control challenges due to the dynamic coupling between vehicle attitude and force generation. This paper proposes a robust control framework for an underactuated hexarotor equipped with a 1-DoF manipulator to perform sustained contact inspection. The architecture integrates integral-augmented Sliding Mode Control (SMC) for trajectory tracking with an admittance control law for force regulation. The contact force is mapped to a feedforward attitude term, while the 1-DoF arm actively compensates for the tilt to maintain surface alignment. Software-in-the-loop simulations demonstrate that the SMC-based approach achieves superior tracking and coupling rejection compared to traditional PID. Furthermore, the interaction strategy achieved precise force regulation with an RMSE of 0.12N and was able to stably exert up to 20N force, confirming the system's efficacy for stable, reliable contact-based inspection.
We present a robust contact-aware control framework for aerial writing on an underactuated platform. The framework combines nonlinear model predictive control (NMPC) for accurate end-effector position and normal-force tracking at small reference penetration depths, with consistent performance across controller tunings, with whole-body incremental nonlinear dynamic inversion (INDI) for robustness to frictional and aerodynamic disturbances during contact. The proposed controllers are validated on a quadrotor-based aerial manipulator with a rigid, single-link, one-degree-of-freedom (DoF) arm in simulation and real-world experiments. The aerial writing experiments span vertical and inclined surfaces, multiple reference forces, different friction conditions, and wind disturbances. The results demonstrate that robust simultaneous five-DoF end-effector pose and contact-force tracking is achievable on a standard underactuated quadrotor with a simple, rigid, single-link arm, without requiring a fully actuated platform, a complex arm, or dedicated force/torque sensing.
Precise control of multi-degree-of-freedom (DoF) robotic manipulators is essential in industrial settings, where high tracking accuracy, fast convergence, and robustness to uncertainty are critical. However, many existing methods continue to suffer from high chattering, slow response, and limited adaptability to disturbances or abrupt input variations. This paper presents a control framework for a 3-DoF industrial manipulator that integrates nonsingular terminal sliding mode control (NTSMC), backstepping design, and a clustering-enhanced radial basis function neural network (RBFNN). The NTSMC component provides finite-time convergence and strong robustness to matched uncertainties, leading to superior tracking performance with a root mean squared error (RMSE) of 0.000202 under ideal conditions and 0.002634 under disturbances, both significantly better than those achieved by Fuzzy-sliding mode control (SMC) and SMC-neural network (NN) baselines. Backstepping enables smooth coordination of the control input across dynamic subsystems, which reduces settling time to 0.0375 seconds in the ideal case and 0.2808 seconds under disturbance. The clustering-enhanced RBFNN allows structured and efficient initialization of network parameters, identifying effective centers and widths in fewer than 500 iterations and improving generalization to both periodic and abrupt input signals. The combined design eliminates the common trade-off between chattering suppression and trajectory smoothness and demonstrates practical potential for real-time, high-precision control in uncertain industrial environments.
To enhance the tracking accuracy and compliance of unmanned excavators, a force–position heterogeneous dual-loop nested cooperative control strategy is proposed. The outer loop employs admittance control to establish a dynamic mapping between contact forces and position correction commands, converting abrupt force variations into compliant position compensation. The inner loop utilizes an RBF neural network for online gain scheduling of PID parameters, thereby suppressing residual tracking errors introduced by the outer loop. Furthermore, asymptotic stability of the heterogeneous dual-loop nested closed-loop system is demonstrated via the Lyapunov method. Simulation results indicate that, in terms of position control, the RMSE and MAE for all three joints remain within 1°, with steady-state tracking errors confined to ±2°; compared with conventional PID and RBF-PID controllers, the tracking accuracy is improved. Regarding force control, compliant regulation of excavation contact forces is achieved, thereby enhancing both the precision and robustness of trajectory tracking for unmanned excavators.
When a robot performs a grinding operation, the steady-state force/position tracking accuracy of its end-effector is critical to achieving high grinding quality. To solve this problem, a fuzzy adaptive impedance method is incorporated into the robot’s compliant control framework. Firstly, the robot dynamics model is established based on the Newton–Euler method. To describe the robot dynamics more comprehensively, a linear friction compensation model is also introduced. Secondly, a dynamic feedforward trajectory-tracking controller is proposed based on the dynamic model, and its stability is verified using a Lyapunov function. The impedance parameters are adjusted in real time according to the feedback contact force and its rate of change, thereby enabling dynamic equilibrium between the end contact force and end position. This allows the robot end-effector to exhibit compliance during external environmental interactions. Finally, a control platform of a force/position compliance controller was constructed, and two grinding conditions of plane and arc were designed to validate the effectiveness of force/position compliance control based on impedance control. Compared with the fixed impedance approach, the proposed method reduces overshoot by 11.6% (plane) and 12.45% (arc), improves surface roughness from Ra 0.042 μm to Ra 0.021 μm, and achieves faster force tracking with fewer oscillations.
Fan Yang, Ming Hu, Jin-Fei Bian et al.· Machines· 0 citations
Multi-link aerial robots can actively deform their articulated structures during flight, giving them strong potential for aerial manipulation. However, they still face substantial challenges in contact-rich aerial manipulation tasks such as surface sliding, which requires both disturbance robustness and compliance to uncertain surface geometry. Force-control strategies such as impedance and admittance control are commonly employed to address these requirements. Although impedance control can provide disturbance-resistant interaction and admittance control can offer compliant adaptation, their opposite force--motion causalities prevent their simultaneous implementation when applied through the same actuation source, such as the rotor thrusts used by conventional aerial robots. To overcome this limitation, we propose a hybrid impedance--admittance control strategy for a multi-link aerial robot. The articulated morphology enables a functional separation of force and motion regulation across joint and rotor actuation sources. In this framework, admittance behavior is generated through joint angle regulation to enhance adaptive interaction, while impedance behavior is achieved by modulating rotor thrust to regulate the sliding motion. This structural coordination allows the robot to leverage the complementary strengths of both control paradigms. As a result, the multi-link aerial robot achieves resilient and adaptive surface sliding. Experimental results demonstrate robust and compliant sliding performance on unknown surfaces.
Zicheng Luo, Maolin Lei, Jinjie Li et al.· 0 citations
This study presents an adaptive sliding mode control approach for trajectory tracking of a quadrotor unmanned aerial vehicle functioning under external interference and kinematic unpredictability. Linear and rotational motion equations formulated in inactive local coordinate system frames are developed with the aid of a nonlinear six-degrees-of-freedom quadrotor dynamic model. Through mitigating excessive switching activity, reliability is improved. Here, the proposed controller integrates sliding mode control with bounded adaptive switching gain factors and boundary-layer smoothing. The operational design is applied within a sequential outer-loop/inner-loop structure for linear and orientation control. The conventional sliding mode control, alongside the proportional derivative control, which employs MATLAB/Simulink R2024a simulations while being interference-affected with an unknown performance set-up, is deployed in this work to relatively appraise the proposed ASM controller. The assessment involves three-dimensional trajectory, control input characteristics, tracking error analysis, adaptive gain growth, and chattering analysis with quantitative performance metrics. The computational output revealed that the proposed ASMC attained superior tracking performance with limited oscillation and level control action. The controller achieves a total RMSE of approximately 0.38 m and a lower aggregate tracking error when using the conventional SMC and PD controllers under equivalent conditions. Furthermore, the adaptive gain mechanism successfully lowers chattering while maintaining robustness against interferences, a large amount of ambiguity, and inertial imbalance with signal noise. The results validate that the proposed ASMC delivers a functional balance between robustness, control smoothness, and tracking accuracy alongside execution homogeneity for autonomous quadrotor UAV trajectory tracking in unsettled and unstable environments.
Muktar Fatihu Hamza· Automation· 0 citations
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