Publications

The guaranteed cost control problem for a class of linear systems with norm-bounded time-varying parameter uncertainties and input constraints is considered. A sufficient condition for the existence of guaranteed cost state feedback controllers is derived in terms of the solvability to a certain system of linear matrix inequalities (LMIs), and the feasible solutions to the system of LMIs provide a parametrized representation of a set of guaranteed cost controllers. Furthermore, the characterization of guaranteed cost controllers is exploited to solve the optimal guaranteed cost control problem. An example is given to illustrate the effectiveness of the proposed results.

This paper addresses the problem of distributed secondary control for islanded AC microgrids with external disturbances. By using a full-order sliding-mode (FOSM) approach, voltage regulation and frequency restoration are achieved in finite time. For voltage regulation, a distributed observer is proposed for each distributed generator (DG) to estimate a reference voltage level. Different from some conventional observers, the reference voltage level in this paper is accurately estimated under directed communication topologies. Based on the observer, a new nonlinear controller is designed in a backstepping manner such that an FOSM surface is reached in finite time. On the surface, the voltages of DGs are regulated to the reference level in finite time. For frequency restoration, a distributed controller is further proposed such that a constructed FOSM surface is reached in finite time, on which the frequencies of DGs are restored to a reference level in finite time under directed communication topologies. Finally, case studies on a modified IEEE 37-bus test system are conducted to demonstrate the effectiveness, the robustness against load changes, and the plug-and-play capability of the proposed controllers.

With the rapid development of computer technology, automatic control technology and communication technology, research on unmanned aerial vehicles (UAVs) has attracted extensive attention from all over the world during the last decades. Particularly due to the demand of various civil applications, the conceptual design of UAV and autonomous flight control technology have been promoted and developed mutually. This paper is devoted to providing a brief review of the UAV control issues, including motion equations, various classical and advanced control approaches. The basic ideas, applicable conditions, advantages and disadvantages of these control approaches are illustrated and discussed. Some challenging topics and future research directions are raised.

This paper is concerned with robust sampled-data fuzzy control for Takagi-Sugeno (T-S) model-based fuzzy systems with interval time-varying delay and norm-bounded parameter uncertainties. By constructing a new Lyapunov-Krasovskii functional, a less conservative sufficient condition on the existence of the sampled-data fuzzy controller for the fuzzy system is formulated in the form of matrix inequalities, which is dependent on both the size of delay-derivative and allowable upper bound of time-varying sampling period, by using a new tighter bounding technique. A feasible solution can be obtained by solving a minimization problem in terms of linear matrix inequalities. Finally, the backing-up control of a computing simulated truck-trailer is discussed to show the effectiveness of the obtained design approach.

This article presents a novel model-free distributionally robust framework for a challenging equilibrium-seeking problem (ESP) under fully unknown coupled dynamics. We consider a scenario in the ESP where the state transitions of players are governed by an unknown coupled dynamic system, and each player aims to minimize its own cost function. By predicting the stochastic distribution of player states through Gaussian process regression, we propose a novel distributionally robust approximation (DRA) that transforms the complex ESP with unknown coupled dynamic system into a solvable distributionally robust optimization problem. The gradient of the DRA's objective function is quantified, ensuring solvability. The effectiveness of the proposed DRA framework is evaluated through a nonlinear system, demonstrating comparable performance to model-based methods without requiring any dynamic model.

Networked control systems (NCSs) are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators, and so on). NCSs have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical development of NCSs. In-depth analysis and discussion is made on sampled-data control, networked control, and event-triggered control. More specifically, existing research methods on NCSs are summarized. Furthermore, as an active research topic, network-based filtering is reviewed briefly. Finally, some challenging problems are presented to direct the future research.

The guaranteed cost control problem via memory less state feedback controllers is studied in this paper for a class of linear singular systems with delayed state and norm-bounded time-varying parameter uncertainties. A sufficient condition for the existence of guaranteed cost controllers is derived, and it is shown that the condition is equivalent to the solvability of a certain linear matrix inequality (LMI). Furthermore, a convex optimization problem with LMI constraints is formulated to design the optimal guaranteed cost controller, which minimizes the guaranteed cost of the closed-loop uncertain system.

This paper is concerned with the distributed target tracking for a moving target of discrete time-varying nonlinear dynamics over a wireless sensor network. A number of spatially distributed sensors are deployed to measure the state of the target, calculate local state predictions as well as local state estimates, and further exchange local information with their underlying neighboring sensors. Due to adversarial attacks, a subset of sensors are deliberately manipulated and thus misbehaving. Accordingly, information exchanges from the misbehaving sensors to their neighbors become antagonistic rather than cooperative as in normal operation. First, a novel distributed target tracking scheme in terms of local state predictors and state estimators is developed for each sensor over a partially misbehaving sensor network. Second, criteria for designing the desired distributed target tracking scheme and the time-varying adjacency matrix are derived such that two ellipsoidal prediction and estimation sets can be recursively computed. It is analytically proved that these two sets guarantee the containment of the true target state at every instant of time regardless of misbehaving sensors as well as unknown-but-bounded process and measurement noises. Third, based on the proposed design criteria, optimization methods are put forward to minimize the calculated ellipsoids. Finally, an application to vehicle tracking is given to show the effectiveness of the results.

This paper is concerned with the problem of robust ${H}_{\infty}$ control of an uncertain discrete-time Takagi-Sugeno fuzzy system with an interval-like time-varying delay. A novel finite-sum inequality-based method is proposed to provide a tighter estimation on the forward difference of certain Lyapunov functional, leading to a less conservative result. First, an auxiliary vector function is used to establish two finite-sum inequalities, which can produce tighter bounds for the finite-sum terms appearing in the forward difference of the Lyapunov functional. Second, a matrix-based quadratic convex approach is employed to equivalently convert the original matrix inequality including a quadratic polynomial on the time-varying delay into two boundary matrix inequalities, which delivers a less conservative bounded real lemma (BRL) for the resultant closed-loop system. Third, based on the BRL, a novel sufficient condition on the existence of suitable robust ${H}_{\infty}$ fuzzy controllers is derived. Finally, two numerical examples and a computer-simulated truck-trailer system are provided to show the effectiveness of the obtained results.

This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.

This paper investigates network-based H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> fuzzy control for a self-balancing two-wheeled inverted pendulum via a T-S fuzzy model. Due to the insertion of a communication network between the pendulum and a fuzzy controller, the fuzzy control is implemented in an asynchronous way. Unlike the existing works, asynchronous constraints on fuzzy membership functions are introduced in deriving some delay-dependent criteria for H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> performance analysis and controller design. A numerical example shows that the proposed method using asynchronous constrains can stabilize the pendulum and achieve a better H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> performance.

No abstract is provided for this article.

Cyber code analysis is fundamental to malware detection and vulnerability discovery for defending cyber attacks. Traditional approaches resorting to manually defined rules are gradually replaced by automated approaches empowered by machine learning. This revolution is accelerated by big code from open source projects which support machine learning models with outstanding performance. In the context of a data-driven paradigm, this paper reviews recent analytic research on cyber code of malicious and common software by using a set of common concepts of similarity, correlation and collective indication. Sharing security goals in recognizing anomalous code that may be malicious or vulnerable. The ability to do so is not determined in isolation, rather drawn for code correlation and context awareness. This paper demonstrates a new research methodology of data driven cyber security (DDCS) and its application in cyber code analysis. The framework of the DDCS methodology consists of three components, i.e., cyber security data processing, cyber security feature engineering, and cyber security modeling. Some challenging issues are suggested to direct the future research.