Information inference for cyber-physical systems with application to aviation safety and space situational awareness

Due to the rapid advancement of technologies on sensors and processors, engineering systems have become more complex and highly automated to meet ever stringent performance and safety requirements. These systems are usually composed of physical plants (e.g., aircraft, spacecraft, ground vehicles, etc.) and cyber components (e.g., sensing, communication, and computing units), and thus called as Cyber-Physical Systems (CPSs). For safe, efficient, and sustainable operation of a CPS, the states and physical characteristics of the system need to be effectively estimated or inferred from sensing data by proper information inference algorithms. However, due to the complex nature of the interacting multiple-heterogeneous elements of the CPS, the information inference of the CPS is a challenging task, where exiting methods designed for a single-element dynamic system (or for even dynamic systems with multiple-homogenous elements) could not be applicable. Moreover, the increasing number of sensor resources in CPSs makes the task even more challenging as meaningful information needs to be accurately and effectively inferred from huge amount of data, which is usually noise corrupted. Many aerospace systems such as air traffic control systems, pilot-automation integrated systems, networked unmanned aircraft systems, and space surveillance systems are good examples of CPSs and thus have the aforementioned challenging problems. The goals of this research are to 1) overcome the challenges in complex CPSs by developing new information inference methodologies based on control, estimation, hybrid systems and information theories, and 2) successfully apply them to various complex and safety-critical aerospace systems such as air transportation systems, space surveillance systems, and integrated human-machine systems, to promote their efficiency and safety

Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating frequency of output signals. The hybrid effects of the time delays, Markov chain, and protocol parameters are apparently reflected in the co-designed estimator which can be solved by a combination of comprehensive matrix inequalities

Asynchronous switching control for fuzzy Markov jump systems with periodically varying delay and its application to electronic circuits

This article focuses on addressing the issue of asynchronous H∞ control for Takagi-Sugeno (T-S) fuzzy Markov jump systems with generally incomplete transition probabilities (TPs). The delay is assumed to vary periodically, resulting in one monotonically increasing interval and one monotonically decreasing interval during each period. Meanwhile, a new Lyapunov-Krasovskii functional (LKF) is devised, which depends on membership functions (MFs) and two looped functions formulated for the monotonic intervals. Since the modes and TPs of the original system are assumed to be unavailable, an asynchronous switching fuzzy controller on the basis of hidden Markov model is proposed to stabilize the fuzzy Markov jump systems (FMJSs) with generally incomplete TPs. Consequently, a stability criterion with improved practicality and reduced conservatism is derived, ensuring the stochastic stability and H∞ performance of the closed-loop system. Finally, this technique is employed to the tunnel diode circuit system, and a comparison example is given, which verifies the practicality and superiority of the method

Quantized passive filtering for switched delayed neural networks

The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account. By choosing Lyapunov functionals and applying several inequality techniques, sufficient conditions are proposed to ensure the filter error system to be not only exponentially stable, but also exponentially passive from the noise interference to the output error. The gain matrix for the proposed quantized passive filter is able to be determined through the feasible solution of linear matrix inequalities, which are computationally tractable with the help of some popular convex optimization tools. Finally, two numerical examples are given to illustrate the usefulness of the quantized passive filter design methods

Energy-efficient time-triggered communication policies for wireless networked control systems

International audienceEnergy-efficient communication protocols have become an important topic over the past two decades due to environmental issues, increased monetary costs of energy consumption , and limited battery capacities of sensors and mobile devices. In this context, we present a novel approach to design energy-efficient time-triggered communication policies for wireless networked control systems, while ensuring a given control performance. We consider a plant, modeled as a deterministic discrete-time linear system, which is controlled through a wireless network by an output-feedback law. We proceed by emulation, i.e., we construct the controller to stabilize the origin of the plant while ignoring communication constraints. Next, the wireless network is taken into account and we assume that the probability of packet drops depends on the transmission signal power. We introduce the notion of stochastic allowable transmission interval (SATI) to characterize stabilizing time-triggered transmission policies. We then explain how to minimize the average energy expenditure of the transmitting devices while satisfying the SATI constraints, thus ensuring the control requirements. Simulations results are provided to illustrate the trade-off between the communication and the control costs

Double Asynchronous Switching Control for Takagi–Sugeno Fuzzy Markov Jump Systems via Adaptive Event-Triggered Mechanism

This article addresses the issue of adaptive event- triggered H∞ control for Markov jump systems based on Takagi-Sugeno (T-S) fuzzy model. Firstly, a new double asynchronous switching controller is presented to deal with the problem of the mismatch of premise variables and modes between the controller and the plant, which is widespread in real network environment. To further reduce the power consumption of communication, a switching adaptive event-triggered mechanism is adopted to relieve the network transmission pressure while ensuring the control effect. In addition, a new Lyapunov-Krasovskii functional (LKF) is constructed to reduce conservatism by introducing the membership functions (MFs) and time-varying delays informa- tion. Meanwhile, the invariant set is estimated to ensure the stability of the system. And the disturbance rejection ability is measured by the optimal H∞ performance index. Finally, two examples are presented to demonstrate the effectiveness of the proposed approach

Switching LPV Approach for Analysis and Control of TCP-based Cyber-Physical Systems under DoS Attack

Cyberphysical systems (CPSs) integrate controllers, sensors, actuators, and communication networks. Tight integration with communication networks makes CPSs vulnerable to cyberattacks. In this paper, we investigate the impact of denial of service (DoS) attack on the stability of cyber physical systems by considering the transmission control protocol (TCP) and extract a sufficient stability condition in linear matrix inequality (LMI) form. To this end, we model the TCP-CPS under DoS attack as a switching LPV time delay system by using the Markov jump model. Then, we design parameter dependent stabilizing controller for CPS under DoS attack, by considering the network parameters