In this paper, we present a method for synthesizing optimal policies for stochastic systems under high-level mission specifications while maintaining information security. The stochastic systems are modeled as probabilistic labeling Markov decision processes (PLMDPs), and the high-level mission specifications are represented by linear temporal logic (LTL) formulas, which must be satisfied repeatedly, infinitely often. Meanwhile, an intruder is modeled as an observer who knows the exact structure of the system and passively monitors its behavior, aiming to infer the high-level mission specifications. Our objective is to synthesize an optimal policy that minimizes the mean payoff cost while preventing the intruder from definitively determining the given mission specifications. We begin by extending the PLMDP model by incorporating an observation function and the corresponding probability function, and we introduce the concept of LTL opacity for stochastic systems. Next, by proposing a new structure called the synchronous accepting maximally end component, we identify the subset of the accepting end components that satisfy the LTL opacity requirement. Finally, we develop a modified linear program with information security constraints to synthesize an optimal policy that ensures both formal correctness and LTL opacity.

To improve the robustness of SVM models to noise and outliers, fuzzy support vector machine (FSVM) has been proposed. However, many existing FSVM models have limitations such as their dependence on assumptions, limited optimization, and unreasonable handling of noise. To address these problems, we propose a novel approach called curriculum learning-based FSVM. Our approach employs a curriculum-learning strategy, where the model initially learns easy samples to avoid noise interference and obtain a good initial solution, before proceeding to learn all samples, including hard ones. To distinguish between easy and hard samples, we introduce an adaptive density-based clustering model, which is extended to kernel feature space. Moreover, we propose a slack variable-based fuzzy membership function to evaluate the importance of samples. Additionally, our model adaptively adapts the importance of samples based on feedback during the learning process. Finally, our experimental results on popular benchmarks demonstrate that our proposed model outperforms existing competitors in terms of accuracy and robustness.

This article investigates the problem of finite-time consensus tracking for incommensurate fractional-order nonlinear multiagent systems (MASs) with general directed switching topology. For the leader with bounded but arbitrary dynamics, a neighborhood-based saturated observer is first designed to guarantee that the observer's state converges to the leader's state in finite time. By utilizing a fuzzy-logic system to approximate the heterogeneous and unmodeled nonlinear dynamics, an observer-based adaptive parameter control protocol is designed to solve the problem of finite-time consensus tracking of incommensurate fractional-order nonlinear MASs on directed switching topology with a restricted dwell time. Then, the derived result is further extended to the case of directed switching topology without a restricted dwell time by designing an observer-based adaptive gain control protocol. By artfully choosing a piecewise Lyapunov function, it is shown that the consensus tracking error converges to a small adjustable residual set in finite time for both the cases with and without a restricted dwell time. It should be noted that the proposed adaptive gain consensus tracking protocol is completely distributed in the sense that there is no need for any global information. The effectiveness of the proposed consensus tracking scheme is illustrated by numerical simulations.

By applying the fractional Lyapunov direct method, we investigate the robust consensus tracking problem for a class of uncertain fractional-order multiagent systems with a leader whose input is unknown and bounded. More specifically, multiple fractional-order systems with heterogeneous unknown nonlinearities and external disturbances are considered in this paper, which include the second-order multiagent systems as its special cases. First, a discontinuous neural network-based (NN-based) distributed robust adaptive algorithm is designed to guarantee the consensus tracking error exponentially converges to zero under a fixed topology. Also the derived results are further extended to the case of switching topology by appropriately choosing multiple Lyapunov functions. Second, a continuous NN-based distributed robust adaptive algorithm is further proposed to eliminate the undesirable chattering phenomenon of the discontinuous controller, where the consensus tacking error is uniformly ultimately bounded and can be reduced as small as desired. It is worth noting that all the proposed NN-based robust adaptive algorithms are independent of any global information and thus are fully distributed. Finally, numerical simulations are provided to validate the correctness of the proposed algorithms.

This paper investigates the frequency properties of linear active disturbance rejection control. For stable or critical stable second-order LTI plants, it proposes a new parameter tuning approach. The approach is proved that can guarantee the closed-loop has a given gain crossover frequency and a phase margin no less than a given value between 32 degrees and 73 degrees.

This paper investigates to control the double integrator with linear active disturbance rejection control and reports two results. First, there is a separation principle which means the dynamics of the state feedback and the error of extended state observer are completely decoupled. Second, with bandwidth method in which the observer bandwidth equals to the controller bandwidth, the closed-loop always has a phase margin of 31.89 degrees and a gain crossover frequency equals to the observer bandwidth, which can be set arbitrary.

Considering the limitation of off-line simulation and the high cost of many real-time simulation equipments used in aeronautical and astronautical fields, a low-cost embedded real-time simulation system has been developed for the research of satellite attitude control. By using the mature ARM/DSP chip as the processor and the embedded Linux operating system in the target machine, and the monitoring and management interface based on the graphical programming language LabVIEW in the host computer, this system has successfully addressed key technical issues of real-time simulation. It has optimized the four essential components of the satellite attitude control closed-loop system. Initial application of the system has validated its effectiveness, and therefore proved that it will be significant in the engineering applications.

The existing approaches to the discrete-time nonlinear output regulation problem rely on the offline solution of a set of mixed nonlinear functional equations known as discrete regulator equations. For complex nonlinear systems, it is difficult to solve the discrete regulator equations even approximately. Moreover, for systems with uncertainty, these approaches cannot offer a reliable solution. By combining the approximation capability of the feedforward neural networks (NNs) with an online parameter optimization mechanism, we develop an approach to solving the discrete nonlinear output regulation problem without solving the discrete regulator equations explicitly. The approach of this paper can be viewed as a discrete counterpart of our previous paper on approximately solving the continuous-time nonlinear output regulation problem.

This paper addresses a global stabilization problem for a class of nonminimum phase nonlinear systems. The nonlinearities of the system, which depend on the system output, can be unknown, but satisfy some linear growth conditions. The given system is first transformed into a special coordinate basis, in which the system zero dynamics is divided into a stable part and an unstable part. A sufficient solvability condition is then established for solving the global stabilization problem. Finally, the obtained result is utilized to solve a stabilization problem on a rotational/translational actuator (RTAC) system