Contact-responsive high-gain robot motion controllers using a constrained disturbance observer

Disturbance rejection tracking control of robotic manipulators based on extended state observer

The trajectory tracking control problem of multi-degree-of-freedom robotic manipulators ((DOF-RM) has attracted widespread attention due to inherent challenges, including nonlinearity, parameter uncertainty, and unknown external disturbances. This paper proposes a robust trajectory tracking control scheme utilizing linear extended state observer (LESO). First, a dynamic model of the robotic manipulator is established, and all unmodeled dynamics and exogenous perturbations are merged into a lumped disturbance term. Second, a parameterized high-gain LESO is designed for realtime estimation of the system state and lumped disturbances, the designed observer only requires the adjustment of a single bandwidth parameter. Finally, the LESO is combined with a robust tracking controller to design an active disturbance-resistant tracking controller. Rigorous stability proofs using Lyapunov stability theory demonstrate that the tracking error and the observer estimation error are uniformly and eventually bounded. Finally, numerical simulations of a two-DOF robotic arm verify the tracking performance of the proposed method.

Intelligent robust control design for a 3-DoF industrial manipulator

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.

Neural Adaptive Admittance Control With Guaranteed Performance for Physical Human–Robot Interaction

Physical human-robot interaction (pHRI) offers considerable potential for improving task efficiency and alleviating operator workload. Nevertheless, the intrinsic variability of human motion intention (HMI) and robot model uncertainties pose substantial challenges to achieving accurate coordinated control. To address these issues, this paper proposes a guaranteed-performance neural adaptive admittance control framework. First, the damping coefficient is dynamically tuned using real-time interaction force feedback, while a neural network (NN) is employed to estimate HMI-induced uncertainties in the coupled human-robot system. These two components are then integrated into the admittance model to construct a high-level interaction strategy that generates compliant reference trajectories for smooth and stable collaboration. Subsequently, low-level motion control with error transformation is developed to enforce prescribed output constraints, thereby ensuring unified regulation of transient and steady-state performance. Moreover, another NN is introduced to approximate the lumped robot dynamics for improved tracking accuracy. Finally, the effectiveness and superiority of the proposed method are validated through trajectory tracking, circle drawing, and obstacle avoidance tasks. Note to Practitioners—This paper focuses on developing an active interaction control approach that enables high-performance tracking for robots subject to model uncertainties while providing high-quality assistance to operators with unknown motion intention. The proposed framework is well-suited to industrial applications such as human-robot cooperative assembly and co-transportation. By incorporating output-constraint-based neural adaptive admittance control, safe, reliable, and compliant physical interaction can be achieved. Consequently, the controller supports further extension to medical rehabilitation and exoskeleton systems, demonstrating broad promise across a wide range of interaction-intensive scenarios.

Spheres in motion: Adaptive dynamic programming for robotic intelligence

Controlling spherical robots is challenging due to their nonlinear dynamics, underactuated characteristics, and non-holonomic constraints. These challenges become more pronounced in the presence of parameter variations and external disturbances. To address these issues, this paper proposes an Adaptive Dynamic Programming (ADP)-based control framework for spherical robot dynamics. The stability properties of the proposed method are analyzed using Lyapunov theory. The kinematic control layer is designed based on the feedback linearization approach, while the dynamic controller employs ADP to compensate for uncertainties and disturbances. The effectiveness of the proposed method is evaluated through two simulation case studies involving trajectory-tracking tasks under parametric uncertainties and external disturbances. The simulation results show that the proposed controller is capable of achieving accurate trajectory tracking while maintaining stable system performance. To provide a comparative assessment, a Sliding Mode Control (SMC) scheme is also implemented under the same conditions. The obtained results indicate that the ADP-based controller can reduce tracking errors and power consumption compared with the considered SMC approach. In one case study, the tracking error integral achieved by the ADP controller is approximately 36% lower than that of SMC, while the corresponding power consumption is reduced by about 23%. These results demonstrate the potential of the proposed ADP framework for improving trajectory-tracking performance in spherical robots under uncertain operating conditions.

Robust end-effector trajectory control of rigid robotic manipulators subjected to base vibrations

Robust Sliding Mode and Admittance Control of Underactuated Aerial Manipulators for Contact-Based Inspection

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.