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This paper is concerned with the security control problem with quadratic cost criterion for a class of discretetime stochastic nonlinear systems subject to deception attacks. A definition of security in probability is adopted to account for the transient dynamics of controlled systems. The purpose of the problem under consideration is to design a dynamic output feedback controller such that the prescribed security in probability is guaranteed while obtaining an upper bound of the quadratic cost criterion. First of all, some sufficient conditions with the form of matrix inequalities are established in the framework of the input-to-state stability in probability (ISSiP). Then, an easy-solution version on above inequalities is proposed by carrying out the well-known matrix inverse lemma to obtain both the controller gain and the upper bound. Furthermore, the main results are shown to be extendable to the case of discretetime stochastic linear systems. Finally, two simulation examples are utilized to illustrate the usefulness of the proposed controller design scheme.

Industrial cyber-physical systems (CPSs) are largescale, geographically dispersed and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators and power systems. For real-time monitoring, some typical Kalmanbased distributed algorithms are summarized and their performances on calculation burden, communication burden as well as scalability are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing, and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research. Index Terms-Industrial cyber-physical systems; distributed filtering; distributed control; power schedule; droop characteristics. I. INDUSTRIAL CYBER-PHYSICAL SYSTEMS A cyber-physical system (CPS), a fast-growing research area, is a highly integrated system of physical components involving sensors, actuators and various equipments, as well as cyber possessing ubiquitous computation and efficient communication. From the engineering point of view, CPSs are considered as the most promising industrial systems including transportation networks, energy systems, water/gas distribution networks, and unmanned factories. For example, multiple industrial robots with an inertial navigation device or various sensors are programmed for the movement along a programmed trajectory to cooperatively complete production tasks [1], [2]. The main advantage of these systems is that the tight