Sep 2026· Proceedings in Applied Mathematics and Mechanics· Vol 26· 0 citations· 7 references
Abstract
This work presents a nonlinear model predictive control (NMPC) framework for a lambda‐shaped parallel robot that incorporates data‐driven surrogates to facilitate control design. An inverse surrogate model is used to provide an efficient initialization of the control inputs for NMPC. The presented controller enables anticipation of the system behavior, enforces constraints, and optimizes the control performance over a finite prediction horizon. The inverse surrogate model is trained using an appropriately generated data set. The resulting initialization, therefore, is already close to the optimal solution, which significantly accelerates the optimization process in the NMPC framework. Simulation results demonstrate that the proposed method achieves high accuracy and robust tracking performance in the presence of parametric uncertainty. Furthermore, comparative studies with a sliding mode controller (SMC) indicate that the proposed method exhibits performance characteristics that are comparable to those of SMC, where each approach shows distinct advantages depending on the specific operating conditions.
Accurate trajectory tracking is a fundamental control task in robotics, typically achieved using model-based methods. Computed Torque Control (CTC) is computationally efficient but struggles to handle system constraints, while Model Predictive Control (MPC) can manage constraints but often results in nonlinear formulat...
Tin Tran, Michael Burke, Dana Kulić· IEEE Robotics and Automation...· 0 citations
Considering the presence of modeling errors, load variations, and time‐varying parameters in collaborative robot joint modules, we propose a novel robust approximate constraint‐following control algorithm based on the Udwadia–Kalaba (U–K) framework. The core innovation of this work lies in the analytical decompositio...
Xiao-Li Liu, Yu Chen, Sheng-Chao Zhen et al.· International Journal of Rob...· 0 citations
A robust control method is suggested for attitude control of the two-wheeled self-balancing robot by combining feedback linearization with sliding mode control techniques, which reduces the rise time by approximately 40% and 50% with respect to PID.
Alaa Jumaah Al-Maiahy, Y. Khidhir, A. J. Attiya et al.· IAES International Journal o...· 0 citations
This thesis investigates advanced modeling and control strategies for robotic manipulators, focusing on the DLR-HIT II robotic hand and the KUKA LBR iiwa. It presents three core contributions that integrate simulation, model-based control, and data-driven methods to improve torque and position control under uncertainti...
This paper presents a robust hierarchical motion control framework for quadruped robots, emphasizing feasible motion planning and disturbance rejection rather than explicit disturbance identification. The proposed model predictive control-sliding mode control (MPC-SMC) combines model predictive control (MPC) with slidi...
Zi-Yi Xu, Ya-Nan Liu, Shi-Yu Li et al.· International Conference on...· 0 citations
