This study proposes a fractional-order adaptive super-twisting sliding mode control (FO-ASTSMC) strategy to mitigate the difficulties arising from nonlinearity, uncertain parameters, and substantial external interferences during path-following operations of a hydraulic excavator working device. The developed approach merges a high-order sliding mode differentiator aimed at state observation, a fresh fractional-order sliding manifold that embeds a memory component for bolstering transient performance and equilibrium accuracy, together with an adaptable super-twisting coefficient. This adaptive gain eliminates the requirement for prior awareness of disturbance limits, all the while mitigating chattering effects and bolstering system robustness. Utilizing Lyapunov theory, the finite-time stability of the overall closed-loop framework has been thoroughly demonstrated. For controller verification, joint simulations employing AMESim and Simulink platforms were performed, pitting its efficacy against both terminal sliding mode control (TSMC) and adaptive fuzzy sliding mode control (AFSMC). In nominal scenarios, the FO-ASTSMC method yielded the lowest root mean square error (RMSE) along with maximum error (MAXE) across boom, arm, and bucket articulations, registering mean decreases of $60\%$ in RMSE and $58.2\%$ in MAXE when benchmarked against AFSMC, alongside $41.8\%$ in RMSE and $43.6\%$ in MAXE versus TSMC. Facing sudden variations in loading, it exhibited enhanced robustness, achieving reductions of $64.2\%$ in RMSE and $54.5\%$ in MAXE beyond AFSMC, as well as $39\%$ in RMSE and $36.5\%$ in MAXE in comparison to TSMC. Outcomes from the simulations affirm that the suggested controller exhibits elevated precision, formidable robustness, and good applicability to actuators, thereby highlighting its considerable promise for implementation in actual engineering scenarios.

To address the issue of low trajectory tracking accuracy in six-degree-of-freedom robotic arms, this study proposes a trajectory tracking control strategy that integrates a Radial Basis Function Neural Network (RBFNN) with non-singular fast terminal sliding mode (NFTSM) control. (1) The Lagrangian method is utilized to develop the dynamic model of the robotic arm. At the same time, a non-singular fast terminal sliding surface is designed to accelerate trajectory convergence and resolve the singularity problem commonly associated with traditional sliding mode control by integrating nonlinear and fast terminal terms. (2) The RBF neural network is employed to globally approximate and compensate for uncertainties in the model and variations in the parameters of the robotic arm. (3) To confirm the overall stability of the control system with the proposed NFTSM control strategy, the Lyapunov stability theory is applied to formulate a Lyapunov function. (4) The six-degree-of-freedom robotic manipulator is simulated in the MATLAB/Simulink environment to assess the effectiveness of the proposed control method. In addition, experimental validation is carried out on a real robotic manipulator to verify the effectiveness of the proposed method. The simulation and experimental results show that, compared with NFTSM and RBFNN-SMC, the proposed control strategy significantly enhances the trajectory tracking accuracy of the six-degree-of-freedom robotic manipulator, thereby offering an effective and practical solution for its trajectory tracking control.