Xiaoming Tang

Model Predictive Control of Linear Systems over Networks with State and Input Quantizations

Although there have been a lot of works about the synthesis and analysis of networked control systems (NCSs) with data quantization, most of the results are developed for the case of considering the quantizer only existing in one of the transmission links (either from the sensor to the controller link or from the controller to the actuator link). This paper investigates the synthesis approaches of model predictive control (MPC) for NCS subject to data quantizations in both links. Firstly, a novel model to describe the state and input quantizations of the NCS is addressed by extending the sector bound approach. Further, from the new model, two synthesis approaches of MPC are developed: one parameterizes the infinite horizon control moves into a single state feedback law and the other into a free control move followed by the single state feedback law. Finally, the stability results that explicitly consider the satisfaction of input and state constraints are presented. A numerical example is given to illustrate the effectiveness of the proposed MPC.

Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays.

This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method.

A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.

H ∞ control for uncertain linear system over networks with Bernoulli data dropout and actuator saturation

This paper investigates the H∞ control problems for uncertain linear system over networks with random communication data dropout and actuator saturation. The random data dropout process is modeled by a Bernoulli distributed white sequence with a known conditional probability distribution and the actuator saturation is confined in a convex hull by introducing a group of auxiliary matrices. By constructing a quadratic Lyapunov function, effective conditions for the state feedback-based H∞ controller and the observer-based H∞ controller are proposed in the form of non-convex matrix inequalities to take the random data dropout and actuator saturation into consideration simultaneously, and the problem of non-convex feasibility is solved by applying cone complementarity linearization (CCL) procedure. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed new design techniques.

Improved predictive control approach to networked control systems based on quantization dependent Lyapunov function

This paper considers model predictive control (MPC) for the linear discrete-time systems in the presence of packet loss, quantization and actuator saturation. Compared with the previous work ([45]), this paper presents an improved networked MPC approach for networked control systems (NCSs) by applying the quantization dependent Lyapunov function (QDLF) method which leads to less conservative results. The additional improvement is made by placing the heavier weighting on the system corresponding to the actual linear feedback law and choosing the relative weighting on the actual and auxiliary feedback laws which further improves the control performance over the existing method. It is shown that the closed-loop stability is guaranteed and a quantized state-feedback controller is derived by solving the infinite horizon optimization problem. Moreover, this method is further extended to multiple-input case. A numerical example is given to illustrate the effectiveness of the proposed approach.

Output Feedback Predictive Control of Interval Type-2 T–S Fuzzy Systems With Markovian Packet Loss

This paper is mainly concerned with the output feedback model predictive control (MPC) of nonlinear networked control systems (NCSs) with data quantization and packet loss. Affected by the parameter uncertainties, which can be captured with the lower and the upper membership functions, the nonlinear system is turned into the linear one by the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model. Stochastic variables with Markov jump linear model are exploited to represent the defective communication links with packet loss, and sector bound uncertainties are introduced to express the data quantization by applying the sector bound approach. The design of output feedback MPC scheme involves an off-line obtained state observer using the linear matrix inequality (LMI) technique and an on-line MPC optimization problem based on the designed estimation state. A new technique for refreshing the estimation error bound, which plays the key role of guaranteeing the recursive feasibility of optimization problem, is provided in this paper. A numerical example is given to demonstrate the effectiveness of the proposed output feedback MPC approach.

Incorporating unsupervised learning into intrusion detection for wireless sensor networks with structural co-evolvability

Abstract Wireless sensor networks (WSNs) are vulnerable to many security threats because of the open and unreliable communication channels, the highly dynamic network structure as well as the decentralized management scheme. It is therefore, quite challenging to build an intrusion detection system that can detect various unknown attacks, reach better balance between detection rate and false alarm rate and increase the adaptivity to network dynamics, particularly for a resource-constraint WSN. In this paper, we proposed a knowledge-based intrusion detection strategy (KBIDS) to bridge the gap. We firstly used the Mean Shift Clustering Algorithm (MSCA), an unsupervised learning scheme to distinguish undefined abnormal patterns which reflect the abnormal behavior of a WSN being attacked from the normal context; then we employed a support vector machine to maximize the margin between abnormal and normal features so that the classification error can be minimized, which in turn to effectively enhance the detection accuracy; finally, we adopted a feature updating strategy to reflect network dynamics so that the system can co-evolve with the network change. Then, the validation of KBIDS in both network emulator and the real environment were conducted and analyzed. Results showed that KBIDS had achieved the highest detection rate and the lowest false alarm rate among several state-of-the-art intrusion models. In addition to that, we also conducted some parameter sensitivity analyses to help identifying the optimal configuration which can be used to parameterize KBIDS in real applications.

An Adaptive Intrusion Detection Method for Wireless Sensor Networks

Current intrusion detection systems for Wireless Sensor Networks (WSNs) which are usually designed to detect a specific form of intrusion or only applied for one specific type of network structure has apparently restrictions in facing various attacks and different network structures. To bridge this gap, based on the mechanism that attacks are much likely to be deviated from normal features and from different shapes of aggregations in feature space, we proposed a knowledge based intrusion detection strategy (KBIDS) to detect multiple forms of attacks over different network structure. We firstly, in the training stage, used a modified unsupervised mean shift clustering algorithm to discover clusters in network features. Then the discovered clusters were classified as an anomaly if they had a certain amount of deviation from the normal cluster captured at the initial stage where no attacks could occur at all. The training data combined with a weighted support vector machine were then used to build the decision function that was used to flag network behaviors. The decision function was updated periodically after training by merging newly added network features to adapt network variability as well as to achieve time efficiency. During network running, each node uniformly captured their status as feature vector at certain interval and forwarded them to the base station on which the model was deployed and run. Using this way, our model can work independently of network structure in both detection and deployment. The efficiency and adaptability of the proposed method have been tested and evaluated by simulation experiments deployed on QualNet. The simulations were conducted as a full-factorial experiment in which all combinations of three forms of attacks and two types of WSN structures were tested. Results demonstrated that the detection accuracy and network structure adaptability of the proposed method outperforms the state-of-the-art intrusion detection methods for WSN.