This paper aims to review the technology, developments, and applications of spin exchange relaxation free (SERF) atomic magnetometers. The generalized comparisons between the SERF magnetometer and the other types of weak magnetic sensors are elaborated. Further, the fundamental composition and operational principle of the SERF atomic magnetometer are elucidated. The development history and recent technological developments with respect to several aspects, including the alkali metal vapor source, atom heating method, pump and probe lasers, and magnetic shielding system, are presented. The classification and respective advantages and disadvantages of these technologies are described in detail. Finally, the existing and potential applications, as well as the prospective future development trends are presented.

In this paper, the optimal control scheme for ice-storage air conditioning (IAC) system is solved via a data-based adaptive dynamic programming (ADP) method. It is the first time that adaptive dynamic programming is employed to design a self-learning scheme which obtains the optimal control policy of ice-storage air conditioning system. First, based on the data of the temperature, irradiance and cooling load in an actual project, a prediction model of cooling load is built by a three-layer neural network with the performance verification. Second, the operation of the ice-storage air conditioning system is analyzed. Third, a data-based adaptive dynamic programming method is designed to realize a self-learning optimal control for the ice-storage air conditioning system. Then, numerical results show that using the data-based optimal control method can reduce the operation costs. Finally, the comparison results show that the developed adaptive dynamic programming method improves the system efficiency, minimizing the overall cost. Thus, the superiority of the developed algorithm is verified.

This paper presents an indirect data-driven method for the trajectory tracking control problem of a class of nonlinear discrete-time systems, which have unknown dynamics. This method first establishes an approximate model of the controlled object using historical I/O data and neural network; then, designs and adjusts the feedback gain matrix online using measured output data and previous estimates. This is an adaptive control process of prediction, estimation, and adjustment, which needs to solve some nonlinear optimization problems online, can overcome the adverse effects of the modeling errors caused by neural networks, and is the key to making the system output asymptotically track the reference trajectory. The convergence analysis and simulation results demonstrate the effectiveness and feasibility of the presented method. In addition, based on Lagrange's mean value theorem, we also give an online linearization technique which is applicable to nonlinear discrete-time systems, whose dynamic models have continuous partial derivatives with respect to the input and the output.

This paper studies the state estimator design for periodic neural networks, where stochastic weight matrices B(k) and packet dropouts are considered. The stochastic variables, which may influence each other, are introduced to describe uncertainties of weight matrices. In order to model the time-varying conditions of the communication channel, a Markov chain is employed to study the jumping cases of the stochastic properties of the packet dropouts (i.e., Bernoulli process with jumping means and variances being used to handle the packet dropouts). A state estimator is constructed such that the augmented system is stochastically stable and satisfies the H _∞ performance. The estimator parameters are derived by means of the linear matrix inequalities method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.

This paper investigates the laser polarization error in the optical rotation detection system (ORDS) of an atomic comagnetometer (ACM), which will seriously degrade the long-term performance of the ORDS. We first establish an optical transmission model of the ORDS by using Jones matrix concerning the optical imperfection of polarizers. Then, we analyze the polarization error based on this model and propose a novel error suppression method. Finally, we experimentally test the long-term performance of the ORDS and the ACM before and after the polarization error suppression to verify the effectiveness of the proposed method. The experimental results show that the long-term performance of the ORDS and the ACM can be improved by approximately 3.4 times with the proposed polarization error suppression method.

A new design method is proposed for a triaxial magnetic field gradient compensation system based on ferromagnetic boundary for atomic sensors. The target field method and the whale optimization algorithm are combined to design magnetic field coils thus transforming the complex analytical solution process into an intelligent optimization problem, while the boundary mirror method is introduced to decouple the coil from the magnetic shield. Magnetic field gradient coils are designed according to the coil design method and combined with the divergence and rotation theorems to form the triaxial gradients compensation system to suppress the magnetic field inhomogeneity in nine directions. The simulation and experimental results are compared with the results before and after compensation by using the box line diagram representing the statistics of the magnetic field deviation, in which the median value is reduced by 65.87% and 85.16%, the minimum by 99.66% and 77.76%, the 25 th quantile by 97.89% and 79.94%, and the 75th quantile by 91.52% and 85.17%, respectively.