Traction Control Allocation Employing Vehicle Motion Feedback Controller for Four-wheel-independent-drive Vehicle

A novel vehicle traction algorithm solving the traction force allocation problem based on vehicle center point motion feedback controller is proposed in this paper. The center point motion feedback control system proposed utilizes individual wheel torque actuation assuming all wheels are individually driven. The approach presented is an alternative to the various direct optimization-based traction force/torque allocation schemes. The proposed system has many benefits, such as significant reduction of the algorithm complexity by merging most traction system functionalities into one. Such a system enables significant simplification, unification, and standardization of powertrain control design. Moreover, many signals needed by conventional traction force allocation methods are not required to be measured or estimated with the proposed approach, which are among others vehicle mass, wheel loading (normal force), and vehicle center of gravity location. Vehicle center point trajectory setpoints and measurements are transformed to each wheel, where the tracking is ensured using the wheel torque actuation. The proposed control architecture performance and analysis are shown using the nonlinear twin-track vehicle model implemented in Matlab $\&$ Simulink environment. The performance is then validated using high fidelity FEE CTU in Prague EFORCE formula model implemented in IPG CarMaker environment with selected test scenarios. Finally, the results of the proposed control allocation are compared to the state-of-the-art approach

Dictionary-free Koopman model predictive control with nonlinear input transformation

This paper introduces a method for data-driven control based on the Koopman operator model predictive control. Unlike exiting approaches, the method does not require a dictionary and incorporates a nonlinear input transformation, thereby allowing for more accurate predictions with less ad hoc tuning. In addition to this, the method allows for input quantization and exploits symmetries, thereby reducing computational cost, both offline and online. Importantly, the method retains convexity of the optimization problem solved within the model predictive control online. Numerical examples demonstrate superior performance compared to existing methods as well as the capacity to learn discontinuous lifting functions

Protection of Ising spin-orbit coupling in bulk misfit superconductors

International audienceIsing superconductivity is present due to the combined effect of broken-inversion symmetry and spin-orbit coupling that locks the spins out of plane, features that are associated with two-dimensional materials. We show that bulk misfit superconductors, (LaSe) 1.14 (NbSe 2) and (LaSe) 1.14 (NbSe 2) 2 , comprising monolayers and bilayers of NbSe 2 , exhibit unexpectedly strong Ising protection with a Pauli-limit violation comparable to monolayer NbSe 2. We establish these misfit compounds as Ising superconductors using complementary experimental methods in combination with first-principles calculations. A concerted effect of charge-transfer, defects, reduction of interlayer hopping, and stacking enables Ising superconductivity in these compounds and therefore provides a possible pathway to design of bulk superconductors that are resilient to magnetic fields