Search Results
Shape-Controlled Flexible Microelectronics Facilitated by Integrated Sensors and Conductive Polymer Actuators
The next generation of biomedical tools requires reshapeable electronics to closely interface with biological tissues. This will offer unique mechanical properties and the ability to conform to irregular geometries while being robust and lightweight. Such devices can be achieved with soft materials and thin-film structures that are able to reshape on demand. However, reshaping at the submillimeter scale remains a challenging task. Herein, shape-controlled microscale devices are demonstrated that integrate electronic sensors and electroactive polymer actuators. The fast and biocompatible actuators are capable of actively reshaping the device into flat or curved geometries. The curvature and position of the devices are monitored with strain or magnetic sensors. The sensor signals are used in a closed feedback loop to control the actuators. The devices are wafer-scale microfabricated resulting in multiple functional units capable of grasping, holding, and releasing biological tissues, as demonstrated with a neuronal bundle.
Optimal design of the tweezer control for chimera states
Chimera states are complex spatio-temporal patterns, which consist of coexisting domains of spatially coherent and incoherent dynamics in systems of coupled oscillators. In small networks, chimera states usually exhibit short lifetimes and erratic drifting of the spatial position of the incoherent domain. A tweezer feedback control scheme can stabilize and fix the position of chimera states. We analyse the action of the tweezer control in small nonlocally coupled networks of Van der Pol and FitzHugh-Nagumo oscillators, and determine the ranges of optimal control parameters. We demonstrate that the tweezer control scheme allows for stabilization of chimera states with different shapes, and can be used as an instrument for controlling the coherent domains size, as well as the maximum average frequency difference of the oscillators.
Dynamical low-rank approximations of solutions to the Hamilton--Jacobi--Bellman equation
We present a novel method to approximate optimal feedback laws for nonlinar optimal control basedon low-rank tensor train (TT) decompositions. The approach is based on the Dirac-Frenkel variationalprinciple with the modification that the optimisation uses an empirical risk. Compared to currentstate-of-the-art TT methods, our approach exhibits a greatly reduced computational burden whileachieving comparable results. A rigorous description of the numerical scheme and demonstrations ofits performance are provided.
Surfing the edge: Finding nonlinear solutions using feedback control
Many transitional wall-bounded shear flows are characterised by the coexistence in statespace of laminar and turbulent regimes. Probing the edge boundary between the two attractors has led in the last decade to the numerical discovery of new (unstable) solutions to the incompressible Navier-Stokes equations. However, the iterative bisection method used to achieve this can become prohibitively costly for large systems. Here we suggest a simple feedback control strategy to stabilise edge states, hence accelerating their numerical identification by several orders of magnitude. The method is illustrated for several configurations of cylindrical pipe flow. Travelling waves solutions are identified as edge states, and can be isolated rapidly in only one short numerical run. A new branch of solutions is also identified. When the edge state is a periodic orbit or chaotic state, the feedback control does not converge precisely to solutions of the uncontrolled system, but nevertheless brings the dynamics very close to the original edge manifold in a single run. We discuss the opportunities offered by the speed and simplicity of this new method to probe the structure of both state space and parameter space.