The properties of quantum materials are commonly tuned using experimental variables such as pressure, magnetic field and doping. Here we explore a different approach: irreversible, plastic deformation of single crystals. We show for the archetypal unconventional superconductor SrTiO3 that compressive plastic deformation induces lowdimensional superconductivity significantly above the superconducting transition temperature (Tc) of undeformed samples. We furthermore present evidence for unusual normal-state transport behaviour that suggests superconducting correlations at temperatures two orders of magnitude above the bulk Tc. The superconductivity enhancement is correlated with the appearance of structural features related to selforganized dislocation structures, as revealed by diffuse neutron and X-ray scattering.These results suggest that deformed SrTiO3 is a potential high-temperature superconductor, and push the limits of superconductivity in this low-density electronic system. More broadly, we demonstrate the promise of plastic deformation and dislocation engineering as tools to manipulate electronic properties of quantum materials.

This paper is concerned with the control law synthesis for robot manipulators, which guarantees that the effect of the sensor faults is kept under a permissible level, and ensures the stability of the closed-loop system. Based on Lyapunov’s stability analysis, the conditions that enable the application of the simple bisection method in the optimization procedure were derived. The control law, with certain properties that make the construction of the Lyapunov function much easier—and, thus, the determination of stability conditions—was considered. Furthermore, the optimization problem was formulated as a class of problem in which minimization and maximization of the same performance criterion were simultaneously carried out. The algorithm proposed to solve the related zero-sum differential game was based on Newton’s method with recursive matrix relations, in which the first- and second-order derivatives of the objective function are calculated using hyper-dual numbers. The results of this paper were evaluated in simulation on a robot manipulator with three degrees of freedom.

We report the design and construction of a two-axis goniometer capable of any sample orientation with respect to the external magnetic field. The advantage of this design is that it allows free rotations around a single axis independent of the other which minimizes rotational error without reduction of angle range. Goniometer is capable of operating with high precision at both low and high temperatures and in high magnetic fields. It was mounted on the custom made nuclear magnetic resonance probe for use in Oxford Instruments wide-bore variable field superconducting magnet.

This paper presents an approach for the solution of a zero-sum differential game associated with a nonlinear state-feedback H∞ control problem. Instead of using the approximation methods for solving the corresponding Hamilton–Jacobi–Isaacs (HJI) partial differential equation, we propose an algorithm that calculates the explicit inputs to the dynamic system by directly performing minimization with simultaneous maximization of the same objective function. In order to achieve numerical robustness and stability, the proposed algorithm uses: quasi-Newton method, conjugate gradient method, line search method with Wolfe conditions, Adams approximation method for time discretization and complex-step calculation of derivatives. The algorithm is evaluated in computer simulations on examples of first- and second-order nonlinear systems with analytical solutions of H∞ control problem.

In recent years in mobile robotics, the focus has been on methods, in which the fusion of measurement data from various systems leads to models of the environment that are of a probabilistic type. The cognitive model of the environment is less accurate than the exact mathematical one, but it is unavoidable in the robot collaborative interaction with a human. The subject of the research proposed in this paper is the development of a model for learning and planning robot operations. The task of operations and mapping the unknown environment, similar to how humans do the same tasks in the same conditions has been explored. The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot. The mobile robot’s motion with developed artificial neural networks and genetic algorithms is defined. The transfer method of obtained knowledge from simulated to a real system (Sim-To-Real; STR) is proposed. This method includes a training step, a simultaneous reasoning step, and an application step of trained and learned knowledge to control a real robot’s motion. Use of the basic cognitive elements language, a robot’s environment, and its correlation to that environment is described. Based on that description, a higher level of information about the mobile robot’s environment is obtained. The information is directly generated by the fusion of measurement data obtained from various systems.