Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neurons models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control. Our methods are tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors. We illustrate our approach both in a canonical chaotic model and in a phenomenological neuron model, showing that many unknown parameters can be uncovered reliably and accurately from short and noisy observed time traces. Our method holds promise for estimation in larger-scale systems, given ongoing improvements in calcium reporters and genetically-encoded voltage indicators.

Reliable Centralized Control of PMSM

In this paper, we introduce the development methodology of a reliable centralized control applied to a synchronous permanent magnet machine. The proposed system is nonlinear, we linearize around a point of application. The resulting model will then be used to reproduce the dynamic behavior of the machine for a reliable control. The controller is based on the standard h infinite to increase performance, reduce measurement noise, and to tolerate the outage of certain sensors. To illustrate the results, we made a comparison between a standard state feedback control and reliable h infinite robust control. The simulation results shows, that the system in case of technical placements poles loses classic performance in the presence of an outage, that the reliable centralized robust control remain satisfactory performance even in the presence of outage.

3D crack identification using the Nelder-Mead Simplex algorithm combined with a random generation of crack positions

In this paper, we present a scheme for cracks identification in three-dimensional linear elastic mechanical components. The scheme uses a boundary element method for solving the forward problem and the Nelder-Mead simplex numerical optimization algorithm coupled with a low discrepancy sequence in order to identify an embedded crack. The crack detection process is achieved through minimizing an objective function defined as the difference between measured strains and computed ones, at some specific sensors on the domain boundaries. Through the optimization procedure, the crack surface is modelled by geometrical parameters, which serve as identity variables. Numerical simulations are conducted to determine the identity parameters of an embedded elliptical crack, with measures randomly perturbed and the residual norm regularized in order to provide an efficient and numerically stable solution to measurement noise. The accuracy of this method is investigated in the identification of cracks over two examples. Through the treated examples, we showed that the method exhibits good stability with respect to measurement noise and convergent results could be achieved without restrictions on the selected initial values of the crack parameters.

Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations

An accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide distance measurements. UWB sensors are based on radio frequency technology and offer low power consumption, wide bandwidth, and precise ranging in the presence of nominal environmental noise. However, in practical situations, UWB sensors experience varying measurement noise due to unexpected obstacles in the environment. The localization accuracy is highly dependent on the filtering of such noise, and the extended Kalman filter (EKF) is one of the widely used techniques. In varying noise situations, where the obstacles generate larger measurement noise than nominal levels, EKF cannot offer precise results. Therefore, this work proposes two approaches based on EKF: sequential adaptive EKF and piecewise adaptive EKF. Simulation studies are conducted in static, linear, and nonlinear scenarios, and it is observed that higher accuracy is achieved by applying the proposed approaches as compared to the traditional EKF method.

SENSITIVITY ANALYSIS ON THE EFFECT OF MEASUREMENT NOISE AND SAMPLING FREQUENCY ON THE CALCULATION OF THE TEMPORAL LIGHT ARTEFACTS

Temporal light modulation (TLM) and the resulting temporal light artefacts (TLA) can cause problems with health and wellbeing for users of lighting products. Therefore, TLM has to be measured accurately and repeatably. This study investigates important factors influencing the measurement uncertainty of TLM measurements. The study shows how measurement uncertainty on central TLM parameters can have a significant effect on the calculation of TLA. Specifically, we show a linear relationship between DC offset and the expected error. Further we show severe effects of random noise on PstLM on certain waveforms. And lastly, we show a curious effect related to SVM of pulse width modulated signals and the measurement sampling frequency.