This paper investigates event-triggered prescribed-time stabilization for time-varying nonlinear systems. The motivation arises from three challenging issues: the singularity resulting from infinite control gains at the prescribed time instant, the management of infinity of implicit time variation, and trade-off between control effort and triggering intervals. Using a delicate trick that the finite value of a new time-varying function remains unchanged once all state variables of the system hit zero, we create an event-triggered strategy incorporating a sophisticated switching trigger rule equipped with a time-dependent threshold, based on the continuous feedback domination method with a series of integral functions containing nested sign functions. Better than existing results on prescribed-time stabilization, the scheme presented in this paper not only guarantees that states converge to zero precisely within the prescribed time and sustains non-truncated controller operation, but also uniquely tackles the prevention of the Zeno phenomenon. At last, the stabilization of tunnel diode circuit is conducted to confirm the validity and the effectiveness of our strategy.

This work is the first to solve the 3-D spin polarization control (3DSPC) problem of atomic ensembles, which controls the spin polarization to achieve arbitrary states with the cooperation of multiphysics fields. First, a novel adaptive dynamic programming (ADP) structure is proposed based on the developed multicritic multiaction neural network (MCMANN) structure with nonquadratic performance functions, as a way to solve the multiplayer nonzero-sum game (MP-NZSG) problem in 3DSPC under the constraints of asymmetric saturation inputs. Then, we utilize the MCMANNs to implement the multicritic multiaction ADP (MCMA-ADP) algorithm, whose convergence is proven by the compression mapping principle. Finally, the MCMA-ADP is deployed in the spin-exchange relaxation-free (SERF) system to provide a set of control laws in 3DSPC that fully exploits the multiphysics fields to achieve arbitrary spin polarization states. Numerical simulations support the theoretical results.

This work is the first to solve the data-driven modeling problem for quantum instrumentation and enables the model built is interpretable. First, a data-driven physical iteration (DPI) modeling approach is proposed to solve the modeling problem of a complex physical system with nonlinear characteristics based on the dynamic behavior of a quantum system described by the phenomenological rate equation. Second, the proposed DPI modeling approach incorporates the fast sampling technique, which is proved feasible by the Taylor mean value theorem, to solve the modeling problem of a nonautonomous system. Third, the convergence of the proposed approach is proved by the least squares criterion and the law of large numbers. Finally, the DPI modeling approach is deployed in the optically pumped magnetometer (OPM) and spin-exchange relaxation-free comagnetometer (SERFCM), the physical parameters of the system are estimated while the quantum instrumentation modeling is completed. Numerical simulations and practical experiments support the theoretical results.

This article is the first work to propose a series of control strategies for the longitudinal electron spin polarization of the spin-exchange relaxation-free comagnetometer system to ensure its ultrastable measurement. Two types of finite-time control strategies are presented for a nonlinear system with affine input. The first control strategy is finite-time fractional exponential feedback control (FEFC), which ensures that the trajectories of an autonomous system converge to an equilibrium state in a finite time that can be specified. The second control strategy is finite-time robust FEFC, which provides a finite-time stability of a nonautonomous system with unknown structures under disturbance and perturbations, and its upper bound of the settling time can be estimated. The theoretical results are supported by numerical simulations.

The issue of finite-time state estimation is studied for discrete-time Markovian bidirectional associative memory neural networks. The asymmetrical system mode-dependent (SMD) time-varying delays (TVDs) are considered, which means that the interval of TVDs is SMD. Because the sensors are inevitably influenced by the measurement environments and indirectly influenced by the system mode, a Markov chain, whose transition probability matrix is SMD, is used to describe the inconstant measurement. A nonfragile estimator is designed to improve the robustness of the estimator. The stochastically finite-time bounded stability is guaranteed under certain conditions. Finally, an example is used to clarify the effectiveness of the state estimation.

This study addresses estimator design for a class of nonuniformly sampled neural networks under the scenario of the sampling interval being inaccessible to the estimator. A new quantization model is described by a hidden Markov chain, where the emission probability depends on the network status and sampling interval. Two variables called hidden mode and observed mode are defined based on the assumption that the data receiver can recognize the quantization density instead of the sampling interval, and the associated observed mode-dependent estimator is designed. An augmented estimation error system is obtained, and the strict$(\mathcal {Q},\mathcal {S},\mathcal {R})-\gamma -$dissipativity for the nonuniformly sampled neural networks is investigated. Then the estimator gain is calculated by solving a set of linear matrix inequalities. Finally, the effectiveness of the proposed approach is demonstrated via an example.

The connectivity is an essential property of the connections between the nodes in networks. The efficient determination algorithm for the connectivity of complex directed networks is an important research direction in graph theory. Aiming at the determination problem of the strong connectivity of directed networks, we propose an improved algorithm over the Warshall algorithm, which extends the research object to complex directed networks and has only the half time complexity of that of the latter. In addition, this article also takes the lead in research on the determination algorithm for the unilateral connectivity of complex directed networks, and on this basis, we propose an algorithm to efficiently determine the unilateral connectivity. Finally, the above two algorithms are integrated into a unified and efficient algorithm with the time complexity of$\mathcal {O}({n}^{3}+4.5{n}^{2})$. This algorithm can determine not only the strong connectivity but also the unilateral connectivity of complex directed networks.

This article investigates the problem of state estimation for discrete-time systems with a Markov driven transmission strategy. A buffer with limited capacity is used to store the latest measurements, and they are transmitted simultaneously once the system accesses to the shared channel. A buffer-dependent smart estimator is then proposed to process the received measurements. A convex sufficient condition concerning the exponential mean-square stability and the$l_{2}-l_{\infty }$performance is established for the estimation error system to design the estimator gains. Finally, two examples are presented to illustrate the effectiveness of the derived result under different conditions.

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 issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measurements, and the estimation error system is derived by using the Kronecker product. By using the Lyapunov method, sufficient conditions are established, which guarantee that the estimation error system is stochastically finite-time bounded and stochastically finite-time stable, respectively. Then, the estimator gains are obtained via solving a set of coupled linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed new design method.

This paper studies how to control the output of a class of nonlinear discrete-time systems, to completely track any given bounded reference trajectories in finite time. For this problem, we develop two kinds of constructive control methods for the total output case and the partial output case, respectively. For each case, the first kind of methods can design the time instant after which the complete trajectory tracking is accomplished, but cannot guarantee the monotonic decrease of the norm of the tracking error before that time instant; the other kind of methods not only can determine when the output trajectory coincides with the reference trajectory, but also can make the norm of the tracking error decrease monotonically before that time instant. For the partial output case, the proposed control methods can guarantee that the rest part of the system output is bounded for all the time. These control methods are feasible no matter whether the dynamic models of these systems are smooth or nonsmooth. Then, the simulation and experiment results prove the feasibility of the proposed methods.

This paper is concerned with the finite-time tracking for linear and nonlinear systems, with respect to the reference signal (to be tracked by the system state) obeying some uniformity. Before the control design of finite-time tracking, an interesting and basic problem is whether or not such control exists for the system concerned. This paper aims to this basic problem. First, the sufficient and necessary conditions on finite-time tracking are proposed for linear systems. Such conditions are then extended to those for affine nonlinear systems. It should be pointed out that the sufficient and necessary conditions proposed in the paper essentially reveal the relationship between the structure of the linear/nonlinear systems and the existence of the finite-time tracking control.

A class of nonlinear multiagent systems with time delays and external noises is investigated, and a distributed adaptive robust control protocol is developed. It is the first time for a class of multiagent systems to take both time delays and external noises into consideration. By virtue of Lyapunov-Krasovskii functional and Young's inequality, the effects of time delay can be eliminated. Then, to exclude external noises, a robustifying term is introduced to eliminate the negative effects of these noises. Moreover, neural networks are utilized to learn the unknown nonlinear terms to adapt to the complex external environment. Finally, a numerical simulation is conducted to validate the effectiveness of our distributed control protocol.

In this paper, we develop an adaptive output trajectory tracking control (AOTTC) method for a class of affine nonlinear discrete-time systems. The controller designed by this AOTTC method can make real-system output track the given expected trajectory asymptotically. Our method has some advantages: 1) it requires relatively few assumptions about the system model; 2) it can simplify the control problem by dynamically linearizing the system model and designing and adjusting the feedback gain matrix to adaptively stabilize the system online; and 3) it can estimate the system parameters by using an optimization scheme without using vast amounts of sampled data. This designing and adjusting procedure of the feedback gain matrix has a clear physical meaning and is easily applied. We also give out the convergence condition of the AOTTC method. Then, a voltage-controllable permanent magnet linear motor system is employed for computer simulation, whose results demonstrate the feasibility of our control method.

Teaching-learning-based optimization (TLBO) is an intelligent optimization algorithm with relatively fewer parameters that should be determined in updating equations. For solving complex optimization problems, the local optima often appear in the evolution. To decrease the possibility of this phenomenon, a novel TLBO variant (EI-TLBO) with experience information (EI) and differential mutation is presented. In the method, neighborhood information (the best individual NTeacher and the mean individual NMean) of each learner's neighbors is introduced to improve the exploration capability. The EI before the current iteration of each learner is introduced to make him or her accurately judge the learning behavior in future. In addition, instead of duplicate elimination to maintain the diversity of population at the end of each generation in the original TLBO, differential mutation is introduced to maintain the diversity of learners during the iterative learning process. The main contribution of this paper is to improve the convergence speed and accuracy by introducing neighborhood topology structure, EI, and differential mutation. The efficiency of the proposed algorithm is evaluated on 46 benchmark functions, among which 27 functions are selected from CEC2013. Its performance is compared with those of six other reported EAs. The results indicate that EI-TLBO algorithm can achieve superior performance.

In this paper, a data-based state feedback control method is developed for a class of nonlinear systems. It is a real-time control method, which requires little prior knowledge about the system dynamics, and does not need to know or to build the mathematical model of the system. We apply a fast sampling technique to sample the state signal, which contains useful information of the system. The zero-order hold (ZOH) and the control switch are also used to obtain system information. The feedback gain matrix is calculated and adjusted according to these sampled data. Theoretical analysis on the convergence and simulation results demonstrate the feasibility of this data-based control method.

In this brief, we develop data-based methods for analyzing the controllability and observability of linear discrete-time systems which have unknown system parameters. These data-based methods will only use measured data to construct the controllability matrix as well as the observability matrix, in order to verify the corresponding properties. The advantages of our methods are threefold. First, they can directly verify system properties based on measured data without knowing system parameters. Second, our calculation precision is higher than traditional approaches, which need to identify the unknown parameters. Third, our methods have lower computational complexities when constructing the controllability and observability matrices.

Sequential Monte Carlo methods, namely particle filters, are popular statistic techniques for sampling sequentially from a complex probability distribution. Sampling is a key step for particle filters and has vital effects on simulation results. Since degeneracy of particles in samples sometimes is very severe, there exist only a few particles with significant weights. Thus the sample diversity is reduced significantly so that only a few particles are used to represent the corresponding probability distribution. Therefore, resampling has to be used very often during the whole procedure. This paper addresses a new method which can avoid the phenomenon of particle degeneracy. A backpropagation neural network is used to adjust low weight particles in order to increase their weights and particles with high weights may be split into two small ones if needed. Our simulation results on a typical tracking problem show that not only the phenomenon of particle degeneracy is effectively avoided but also tracking results are much better than those of the traditional particle filter.