A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape low Reynolds number behavior. This work proposes an approach full translational dynamics of quadrotor UAV by feedforward ...

Abstract A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general systems under system uncertainties and subject to state input constraints. The LBNMPC strategy decouples the robustness performance requirements by employing an additional learned introducing it into MPC framework along with nominal model. helps ensure closed-loop system’s safety s...

By enabling constraint-aware online model adaptation, predictive control using Gaussian process (GP) regression has exhibited impressive performance in real-world applications and received considerable attention the learning-based community. Yet, solving resulting optimal problem real-time generally remains a major challenge, due to i) increased number of augmented states optimization problem, ...

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions fuel consumption of compression ignition engine. In work machine is applied two methods. first application, ML identify for implementation control optimization problems. second as replacement NMPC where learns optimal action by imitating or mimicking behavior controller. stu...