With the rapid development of modern industry, robotic manipulators are required to achieve increasingly high trajectory-tracking accuracy and robustness in practical applications. To enhance tracking performance under complex operating conditions, this paper proposes an Adaptive Model Predictive Control with Sliding-Mode Robust Compensation (AMPC–SMC) scheme that integrates an adaptive mechanism with sliding-mode control theory. First, a dynamic model of the manipulator is established, and parameter linearization is employed to transform the nonlinear dynamics into a linearly parameterized form with unknown parameters. Second, an adaptive law is derived based on Lyapunov stability theory to update the model parameters online, thereby mitigating the adverse effects of parametric perturbations and external disturbances on tracking accuracy. Building on this, a receding-horizon optimization strategy is introduced by formulating a quadratic cost function that penalizes both tracking errors and control effort, and the optimal control input is obtained by solving the resulting optimization problem. Meanwhile, a sliding-mode term is incorporated as a robust compensator to eliminate residual tracking errors. Finally, the desired trajectory is generated via point-to-point path planning in Cartesian space, and the proposed method is validated on a real six-degree-of-freedom robotic manipulator. Comparative experiments against conventional model predictive control (MPC) and traditional sliding-mode control (SMC) demonstrate that the proposed AMPC-SMC controller achieves remarkably superior tracking performance compared with the conventional MPC and SMC controllers. In terms of tracking accuracy, the mean absolute errors (MAE) of Joint 2, Joint 4 and Joint 5 under AMPC-SMC are reduced by $91.7\%$, $92.1\%$ and $86.1\%$ respectively relative to MPC, and decreased by $76.1\%$, $77.3\%$ and $85.1\%$ compared with the standalone SMC controller.

Free-curve V-groove arrays are difficult to machine using conventional diamond cutting due to tool interference and unstable axis motion in regions with significant curvature variation. To address this problem, an improved ultra-precision machining method integrating C -axis rotating diamond cutting with look-ahead acceleration control is proposed. The tool orientation is adjusted in real time to maintain an appropriate cutting posture, while adaptive feed rates are generated according to local curvature variations. Machining experiments were conducted on a nickel-plated substrate to fabricate free-curve V-groove arrays with a groove depth of 8 μ m. The maximum feed rate, acceleration, and jerk were set to 1600 mm min ⁻¹ , 1000 mm s ⁻² , and 10 000 mm s ⁻³ , respectively. The experimental results demonstrate that the proposed method effectively suppresses machining defects and improves surface quality compared with constant feed rate machining, particularly in high-curvature regions.