Model Predictive Control with Numerical Solution based on Contraction Mapping Method for Stabilization of Vehicle Nonlinear Dynamics

This paper investigates the nonlinear model predictive control problem for stabilization of unstable vehicle dynamics. Model predictive control (MPC) method is a kind of optimal feedback control method in which the control performance over a finite future is optimized. The contraction mapping algorithm is used for solving the nonlinear model predictive control problem within a short sampling period. A nonlinear tire model is employed to describe the realistic behavior of vehicle motions. The objective of this paper is to propose a nonlinear model predictive control method with a fast numerical solution algorithm called contraction mapping method for designing an automatic vehicle control system. The effectiveness of the proposed method is verified by numerical simulation

Imagens de 1968: A Batalha da Maria Antônia

Fotos feitas por Hiroto Yoshioka da Batalha da Maria Antôni

Active Initialization Experiment of Superconducting Qubit Using Quantum-circuit Refrigerator

The initialization of superconducting qubits is one of the essential techniques for the realization of quantum computation. In previous research, initialization above 99\% fidelity has been achieved at 280 ns. Here, we demonstrate the rapid initialization of a superconducting qubit with a quantum-circuit refrigerator (QCR). Photon-assisted tunneling of quasiparticles in the QCR can temporally increase the relaxation time of photons inside the resonator and helps release energy from the qubit to the environment. Experiments using this protocol have shown that 99\% of initialization time is reduced to 180 ns. This initialization time depends strongly on the relaxation rate of the resonator, and faster initialization is possible by reducing the resistance of the QCR, which limits the ON/OFF ratio, and by strengthening the coupling between the QCR and the resonator

Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes

This paper presents a robust position/attitude tracking control method for a fully-actuated hexarotor unmanned aerial vehicle (UAV) based on Gaussian processes. Multirotor UAVs suffer from modelling errors due to their structure complexity and aerodynamical disturbances whose perfect mathematical formulation is intractable. To handle this issue, this paper incorporates a data-based learning technique with model-based control. The hexarotor UAV dynamical model, considering modelling errors and aerodynamic disturbances as unknown dynamics, is first derived. Gaussian process regression is next introduced as a learning method for the unknown dynamics, which provides probabilistic distributions of the predicted values. The predicted means are regarded as deterministic information and cancelled out by feedforward control inputs. The predicted variances are considered as the bounds of the model uncertainties with high probability, and a robust control method to ensure ultimate boundedness of the tracking control error is proposed for the uncertain system. The effectiveness of the proposed method is demonstrated via experiments with a self-developed hexarotor UAV testbed