Abstract
This study presents a Pareto-optimized Model Predictive Control (MPC) framework for dynamically feasible three-dimensional trajectory generation in robotic manipulators operating under physical constraints. Unlike conventional interpolation-based methods that emphasize geometric smoothness while neglecting system dynamics, the proposed approach integrates a second-order discrete-time model with explicit constraints on position, velocity, and acceleration, ensuring physically consistent motion profiles. A multi-objective optimization strategy is introduced, combining grid search with Pareto front analysis to systematically tune key MPC parameters, including prediction horizon and discretization step. This enables a principled trade-off between tracking accuracy and control effort, addressing a critical limitation in existing MPC implementations that rely on heuristic parameter selection. Experimental results demonstrate that the proposed method achieves competitive tracking performance while significantly improving trajectory smoothness and reducing acceleration peaks compared to spline-based and linear interpolation approaches. The framework maintains real-time feasibility with computation times below 20 ms per control cycle, making it suitable for practical deployment in robotic systems. Furthermore, the integration of learning-based trajectory generation highlights the adaptability of the approach in complex and dynamic environments. Overall, the proposed methodology offers a scalable, interpretable, and computationally efficient solution that bridges the gap between geometric trajectory planning and physically realizable robotic motion, contributing to the advancement of control-aware trajectory generation in modern robotic applications.