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In this work, stabilization of an axonal growth in a neuron associated with the dynamics of tubulin concentration is proposed by designing a boundary control. The dynamics are given by a parabolic Partial Differential Equation (PDE) of the tubulin concentration, with a spatial domain of the axon's length governed by an Ordinary Differential Equation (ODE) coupled with the tubulin concentration in the growth cone. We propose a novel backstepping method for the coupled PDE-ODE dynamics with a moving boundary, and design a control law for the tubulin concentration flux in the soma. Through employing the Lyapunov analysis to a nonlinear target system, we prove a local exponential stability of the closed-loop system under the proposed control law in the spatial $H_1$-norm., Comment: Accepted for CDC 2021

We present two novelties in our analysis of fully dynamical QCD+QED ensembles with C* boundary conditions. The first one is the explicit computation of the sign of the Pfaffian. We present an algorithm that provides a significant speedup compared to traditional methods. The second one is a reweighting of the mass in the context of the RHMC. We have tested the techniques on both pure QCD and QCD+QED ensembles with pions at $m_{\pi^\pm}\approx400$ MeV, a lattice spacing of $a\approx0.05$ fm, a fine-structure constant of $\alpha_{\mathrm{R}}=0$ and $0.04$., Comment: RC* Collaboration, Lattice 2021

This paper studies the feedback stabilization problem of the motion of a tank that contains an incompressible, Newtonian, viscous liquid. The control input is the force applied on the tank and the overall system consists of two nonlinear Partial Differential Equations and two Ordinary Differential Equations. Moreover, a spill-free condition is required to hold. By applying the Control Lyapunov Functional methodology, a set of initial conditions (state space) is determined for which spill-free motion of the liquid is possible by applying an appropriate control input. Semi-global stabilization of the liquid and the tank by means of a simple feedback law is achieved, in the sense that for every closed subset of the state space, it is possible to find appropriate controller gains, so that every solution of the closed-loop system initiated from the given closed subset satisfies specific stability estimates. The closed-loop system exhibits an exponential convergence rate to the desired equilibrium point. The proposed stabilizing feedback law does not require measurement of the liquid level and velocity profiles inside the tank and simply requires measurements of: (i) the tank position error and tank velocity, (ii) the total momentum of the liquid, and (iii) the liquid levels at the tank walls. The obtained results allow an algorithmic solution of the problem of the spill-free movement and slosh-free settlement of a liquid in a vessel of limited height (such as water in a glass) by a robot to a pre-specified position, no matter how full the vessel is., Comment: 24 pages,1 figure, submitted to IEEE TAC for possible publication

We develop an input delay-compensating design for stabilization of an Aw-Rascle-Zhang (ARZ) traffic model in congested regime which is governed by a $2\times 2$ first-order hyperbolic nonlinear PDE. The traffic flow consists of both adaptive cruise control-equipped (ACC-equipped) and manually-driven vehicles. The control input is the time gap of ACC-equipped and connected vehicles, which is subject to delays resulting from communication lag. For the linearized system, a novel three-branch bakcstepping transformation with explicit kernel functions is introduced to compensate the input delay. The transformation is proved {\ae}to be bounded, continuous and invertible, with explicit inverse transformation derived. Based on the transformation, we obtain the explicit predictor-feedback controller. We prove exponential stability of the closed-loop system with the delay compensator in $L_2$ norm. The performance improvement of the closed-loop system under the proposed controller is illustrated in simulation.

Variational quantum algorithms have found success in the NISQ era owing to their hybrid quantum-classical approach which mitigate the problems of noise in quantum computers. In our study we introduce the dynamic ansatz in the Variational Quantum Linear Solver for a system of linear algebraic equations. In this improved algorithm, the number of layers in the hardware efficient ansatz circuit is evolved, starting from a small and gradually increasing until convergence of the solution is reached. We demonstrate the algorithm advantage in comparison to the standard, static ansatz by utilizing fewer quantum resources and with a smaller quantum depth on average, in presence and absence of quantum noise, and in cases when the number of qubits or condition number of the system matrix are increased. The numbers of iterations and layers can be altered by a switching parameter. The performance of the algorithm in using quantum resources is quantified by a newly defined metric.

This paper studies the global feedback stabilization problem of a system with two pistons and the area between them containing a viscous compressible fluid (gas) modeled by the Navier-Stokes equations. The control input is the force applied on the left piston (boundary input) and the overall system consists of two nonlinear Partial Differential Equations and four nonlinear Ordinary Differential Equations. Global feedback stabilizers are designed for the overall system by means of the Control Lyapunov Functional methodology. The closed-loop system exhibits global asymptotic stability with an exponential convergence rate. The proposed stabilizing boundary feedback laws do not require measurement of the density and velocity profiles inside the area between the pistons and simply require measurements of the gas density and velocity at the position of the actuated piston., Comment: 30 pages, submitted to SIAM Journal on Control and Optimization for possible publication

The soft error rate (SER) of integrated circuits (ICs) operating in space environment may vary by several orders of magnitude due to the variable intensity of radiation exposure. To ensure the radiation hardness without compromising the system performance, it is necessary to implement the dynamic hardening mechanisms which can be activated under the critical radiation exposure. Such operating scenario requires the real-time detection of energetic particles responsible for the soft errors. Although numerous particle detection solutions have been reported, very few works address the on-chip particle detectors suited for the self-adaptive fault tolerant microprocessor systems for space missions. This work reviews the state-of-the-art particle detectors, with emphasis on two solutions for the self-adaptive systems: particle detector based on embedded SRAM and particle detector based on pulse stretching inverters.

This work investigates the use of pulse stretching inverters for monitoring the variation of flux and Linear Energy Transfer (LET) of energetic particles. The basic particle detector consists of two cascaded pulse stretching (skew-sized) inverters designed in CMOS technology, and the required sensing area is obtained by connecting multiple two-inverter pulse stretching cells in parallel, and employing the required number of parallel arrays. The particle strikes are detected in terms of the Single Event Transients (SETs), and the detector provides the information on the SET count rate and SET pulse width variation, from which the particle flux and LET can be determined. The main advantage of the proposed solution is the possibility to sense the LET variations using purely digital processing logic. The SPICE simulations done on IHP 130 nm bulk CMOS technology have shown that the SET pulse width at the output of detector changes by 550 ps over the LET range from 1 to 100 MeVcm2mg-1. The proposed solution is intended to operate as an on-chip particle detector within the self-adaptive multiprocessing systems.

We introduce a novel class of Nash equilibrium seeking dynamics for non-cooperative games with a finite number of players, where the convergence to the Nash equilibrium is bounded by a KL function with a settling time that can be upper bounded by a positive constant that is independent of the initial conditions of the players, and which can be prescribed a priori by the system designer. The dynamics are model-free, in the sense that the mathematical forms of the cost functions of the players are unknown. Instead, in order to update its own action, each player needs to have access only to real-time evaluations of its own cost, as well as to auxiliary states of neighboring players characterized by a communication graph. Stability and convergence properties are established for both potential games and strongly monotone games. Numerical examples are presented to illustrate our theoretical results., Comment: Presented at the IEEE Conference on Decision and Control, on Dec. 14-18, 2020

In this paper, we present a novel Newton-based extremum seeking controller for the solution of multivariable model-free optimization problems in static maps. Unlike existing asymptotic and fixed-time results in the literature, we present a scheme that achieves (practical) fixed time convergence to a neighborhood of the optimal point, with a convergence time that is independent of the initial conditions and the Hessian of the cost function, and therefore can be arbitrarily assigned a priori by the designer via an appropriate choice of parameters in the algorithm. The extremum seeking dynamics exploit a class of fixed time convergence properties recently established in the literature for a family of Newton flows, as well as averaging results for perturbed dynamical systems that are not necessarily Lipschitz continuous. The proposed extremum seeking algorithm is model-free and does not require any explicit knowledge of the gradient and Hessian of the cost function. Instead, real-time optimization with fixed-time convergence is achieved by using real time measurements of the cost, which is perturbed by a suitable class of periodic excitation signals generated by a dynamic oscillator. Numerical examples illustrate the performance of the algorithm., Comment: Presented at the IFAC World Congress on July 11-17, 2020. arXiv admin note: text overlap with arXiv:1912.06999

This paper provides novel Input-to-State Stability (ISS)-style maximum principle estimates for classical solutions of highly nonlinear 1-D parabolic Partial Differential Equations (PDEs). The derivation of the ISS-style maximum principle estimates is performed by using an ISS Lyapunov Functional for the sup norm. The estimates provide fading memory ISS estimates in the sup norm of the state with respect to distributed and boundary inputs. The obtained results can handle parabolic PDEs with nonlinear and non-local in-domain terms/boundary conditions. Three illustrative examples show the efficiency of the proposed methodology for the derivation of ISS estimates in the sup norm of the state., Comment: 20 pages, submitted to ESAIM COCV for possible publication

In order to understand the bounds of utilization of the Grover's search algorithm for the large unstructured data in presence of the quantum computer noise, we undertake a series of simulations by inflicting various types of noise, modelled by the IBM QISKit. We apply three forms of Grover's algorithms: (1) the standard one, with 4-10 qubits, (2) recently published modified Grover's algorithm, set to reduce the circuit depth, and (3) the algorithms in (1) and (2) with multi-control Toffoli's modified by addition of an ancilla qubit. Based on these simulations, we find the upper bound of noise for these cases, establish its dependence on the quantum depth of the circuit and provide comparison among them. By extrapolation of the fitted thresholds, we predict what would be the typical gate error bounds when apply the Grover's algorithms for the search of a data in a data set as large as thirty two thousands.

This paper deals with the problem of boundary stabilization of 1D reaction-diffusion PDEs with a time- and space- varying reaction coefficient. The boundary control design relies on the backstepping approach. The gains of the boundary control are scheduled under two suitable event-triggered mechanisms. More precisely, gains are computed/updated on events according to two state-dependent event-triggering conditions: static-based and dynamic-based conditions, under which, the Zeno behavior is avoided and well-posedness as well as exponential stability of the closed-loop system are guaranteed. Numerical simulations are presented to illustrate the results., Comment: 20 pages, 5 figures, submitted to SICON