Linear Networked Control Systems Research Articles

Memory‐based event‐triggered control for networked control system under cyber‐attacks

AbstractThis article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources. In this context, a new memory stochastic state feedback controller is proposed to stabilize the closed‐loop networked control system. A new randomly occurring deception attacks model is employed to deal with the security problem of NCSs. Sufficient stability conditions are derived based on a suitable Lyapunov‐Krasovskii functional (LKF). The designed methodology is proposed in terms of linear matrix inequality to synthesize both event‐triggered parameters and controller gains, and to reduce the conservatism of the system some integral lemma are exploited to bind the time derivative of the LKF. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed method which provides a maximal upper bound value of the network‐induced delay and less transmitted packet regarding the maximal value delay obtained in other works, so less conservatism results are obtained, compared to previous ones in the literature.

Sampled-data event-triggered control with sampling-independent positive guarantees on inter-event times

To fit with a digital environment in networked control systems, sampled-data event-triggered control (ETC) has been proposed where the event-triggering mechanisms sense and process information only at discrete sampling instants (not necessarily periodically). One deficiency in the previous study on sampled-data ETC is the lack of analysis on transmission performance, which yields the potential occurrence of an undesirable phenomenon that the minimum inter-event time is always equal to the minimum inter-sampling interval, no matter how small the latter is. To overcome this drawback, in this paper, we propose a novel sampled-data ETC scheme such that the lower bounds of inter-event times have sampling-independent positive guarantees. A linear networked control system is considered under observer-based controllers, external disturbances, and multiple communication channels. The improved transmission performance is due to the utilization of dynamic event-triggering conditions and some mean-rate error signals, which are the ratios between network-induced errors and some time-increasing functions. After modeling the closed-loop dynamics into a hybrid system, sufficient conditions on the upper bound of inter-sampling intervals and parameters in ETC are provided to ensure input-to-state stability (rather than its practical version) and sampling-independent positive guarantees simultaneously. Furthermore, a new tradeoff relationship between the sampling and transmission performance is revealed: a faster sampling frequency is conducive to improving inter-event times. Finally, a linearized model of an inverted pendulum is simulated to illustrate the efficiency and feasibility of the obtained results.

ℒ2 gain tracking control of linear completely unknown discrete‐time networked control systems with dropout

AbstractWe introduce an online, model‐free algorithm to address the gain optimal tracking problems in discrete linear networked control systems. The algorithm is specifically proposed to handle stochastic information dropout in the feedback loop. Our goal is to design a control law that achieves the system output reference tracking while attenuating the effect of the disturbance input. We first construct an augmented system consisting of the original system and the command generator system. Then, the performance index is expressed in a quadratic form taking into account packet loss. Next, we obtain the optimal solution by solving the dropout generalized algebraic riccati equation (GARE). Finally, a ‐learning algorithm is utilized to estimate the control and disturbance feedback gains of the system, using only measurement data with unknown system dynamics in the presence of dropout. Two algorithms are tested on a numerical example to demonstrate the validity and effectiveness of the proposed methodology.

Optimal Control of an Uncertain Linear Networked Control Systems Under Denial of Service Attacks

Optimal Control of an Uncertain Linear Networked Control Systems Under Denial of Service Attacks

Designing Discrete Predictor-Based Controllers for Networked Control Systems with Time-varying Delays: Application to A Visual Servo Inverted Pendulum System

A discrete predictor-based control method is developed for a class of linear time-invariant networked control systems with a sensor-to-controller time-varying delay and a controller-to-actuator uncertain constant delay, which can be potentially applied to vision-based control systems. The control scheme is composed of a state prediction and a discrete predictor-based controller. The state prediction is used to compensate for the effect of the sensor-to-controller delay, and the system can be stabilized by the discrete predictor-based controller. Moreover, it is shown that the control scheme is also robust with respect to slight message rejections. Finally, the main theoretical results are illustrated by simulation results and experimental results based on a networked visual servo inverted pendulum system.

H∞ Control of Linear Networked and Quantized Control Systems With Communication Delays and Random Packet Losses

This article studies the H∞ control problem for linear networked and quantized control systems (NQCSs) with both communication delays and random packet losses. To deal with network-induced constraints and random packet dropouts, a novel discrete-time stochastic system model is developed for continuous-time networked control systems, and further overapproximated to a polytopic system with norm-bounded uncertainty. Based on the overapproximated system model, sufficient conditions are established for linear NQCSs in different cases to guarantee both input-to-state stability and H∞ performance with respect to the network-induced errors. Furthermore, we propose an algorithm to minimize the stability gain and the H∞ attenuation level simultaneously. Finally, a numerical example is given to illustrate the developed results.

Noisy Sensor Scheduling in Wireless Networked Control Systems: Freshness or Precision

In linear wireless networked control systems whose control is based on the system state’s noisy and delayed observations, an accurate functional relationship is derived between the estimation error and the observations’ freshness and precision. The proposed functional relationship is then applied to formulate and solve the problem of scheduling among different wireless links from multiple noisy sensors, where a sliding window algorithm is further proposed. The algorithm’s simulation results show significant performance gain over existing policies, even in scenarios that require high freshness or precision of observations.

Security tracking control for discrete-time stochastic systems subject to cyber attacks

This paper is concerned with the security tracking problem under the quadratic cost criterion for a class of discrete-time stochastic linear networked control systems (NCSs) exposed to cyber attacks, covering false data injection attacks as well as a class of DoS attacks. Taking into account factors such as network congestion and the defensive role of intrusion detection systems, successful attack events are modeled as a Bernoulli random sequence. To describe the transient trajectory of an NCS under the impact of a random attack, a probabilistic definition of secure trackability is taken. Therefore, an observer-based dynamic output feedback controller is designed in order to achieve the specified probabilistic secure trackability. Specifically, the probabilistic safety output tracking problem is transformed into an input-to-state stability problem in the probabilistic sense for closed-loop systems with some new sufficient conditions, provided that an augmented incremental model is utilized Then, the controller parameters and the upper bound of the quadratic cost function are determined by solving matrix inequalities, while easy-solution forms of the matrix inequalities to be solved are presented by the Schur complementary lemma. Both simulation studies and practical experiments demonstrate the effectiveness of the proposed control scheme.

Sensor Faults Isolation in Networked Control Systems: Application to Mobile Robot Platoons.

In networked control systems, sensor faults in a subsystem have a major influence on the entire network as the fault effect reaches the other subsystems through the network interconnections. In this paper, a fault diagnosis-oriented model is proposed for linear networked control systems that can be applied to the robotics platoon. In addition, this model can also be used to design distributed Unknown Input Observers (UIO) in each subsystem to accomplish weak sensor faults isolation by treating the network disturbances and fault propagation through the network as unknown inputs. A case study was developed in which the subsystems were represented by robots that are connected in a wireless communication-based leader-follower scheme. The simulation results show that the model successfully reproduces the expected behaviour of the robotics platoon in the presence of sensor faults. Furthermore, weak sensor faults isolation is also achieved by observing the residual signals produced by the UIOs in each of the subsystems.

Active fault tolerant / intrusion tolerant cooperative control of discrete NCS based on sliding mode variable structure

Aiming at the actuator failure in the linear discrete networked control system (NCS) and the denial of service network attack (DoS) on the actuator side, under the discrete event trigger mechanism, an active fault-tolerant/intrusion-tolerant cooperative control method based on sliding mode variable structure is proposed. This method applies the fading adaptive Kalman filter (AKF) to jointly estimate the system state, fault parameters and attack parameters, obtains the estimated value of each parameter vector, and uses the estimated value satisfying the event trigger condition to reconstruct the controller. In this way, system errors caused by actuator faults and attacks are corrected, fault-tolerant and intrusion-tolerant coordinated control is realized, and the purpose of saving network resources can be achieved. Finally, the effectiveness of the control method is verified by simulation.

Active Fault‐Tolerant/Active Passive Intrusion‐Tolerant H∞ Cooperative Control of Discrete NCS under the Background of Big Data

For the linear discrete networked control system (NCS) which may suffer DoS attack on both sides of the controller, when the actuator has time‐varying failure, the intelligent sensor unit uses wireless sensors to collect data. According to the large amount of data collected, the active fault tolerance/active passive capacity of linear discrete NCS under the discrete event‐triggered communication mechanism (DETCS) is studied. The problem of cooperative controller design is discussed. Firstly, a linear discrete NCS model integrating DETCS, actuator fault, and network attack is established. Then, based on the idea of integral sliding mode control, an active fault‐tolerant/attack active passive intrusion‐tolerant cooperative controller is designed, and the actuator attack side network attack and sensor side network attack are extended to the state to obtain a new state vector. Then, an adaptive Kalman filter estimator (AKF) estimates the fault and attack information and then adjusts the initial fault‐tolerant/intrusion‐tolerant cooperative controller in real time according to the estimated information obtained by the adaptive Kalman filter estimator; finally, the MATLAB simulation example is used to verify the improvement of system performance by the designed control law and the saving of network resources by the introduction of DETCS.

Switched Stackelberg game analysis of false data injection attacks on networked control systems

This paper is concerned with a security problem for a discrete-time linear networked control system of switched dynamics. The control sequence generated by a remotely located controller is transmitted over a vulnerable communication network, where the control input may be corrupted by false data injection attacks launched by a malicious adversary. Two partially conflicted cost functions are constructed as the quantitative guidelines for both the controller and the attacker, after which a switched Stackelberg game framework is proposed to analyze the interdependent decision-making processes. A receding-horizon switched Stackelberg strategy for the controller is derived subsequently, which, together with the corresponding best response of the attacker, constitutes the switched Stackelberg equilibrium. Furthermore, the asymptotic stability of the closed-loop system under the switched Stackelberg equilibrium is guaranteed if the switching signal exhibits a certain average dwell time. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method in this paper.

Delta-Modulator-Based Quantised Output Feedback Controller for Linear Networked Control Systems

This article proposes a $\Delta $ -Modulator ( $\Delta $ -M) based quantised output feedback controller for linear networked systems. The proposed $\Delta $ -M is essentially a 2-level quantiser, in contrast to some of the existing quantisers such as $2^{p}$ level ( $p \geq 1$ ) uniform-interval-nearest-neighbour quantiser, and offers various advantages which include lower design complexity, less noisy and lower cost. The three key components of the control system: the controller , the filter and the quantiser are designed to achieve the desired performance. The stability conditions of the $\Delta $ -M are derived and conditions for the existence of zig-zag behaviour in steady-state are determined. The performance of the proposed controller is illustrated through simulations considering practical communication network based on ZigBee protocol. The results of the simulation demonstrate that the proposed controller could effectively achieve desired performance under various imperfections of the practical communication network.

Model-Free Optimal Output Regulation for Linear Discrete-Time Lossy Networked Control Systems

In this article, a new model-free approach is proposed to solve the output regulation problem for networked control systems, where the system state can be lost in the feedback process. The goal of the output regulation is to design a control law that can make the system achieve asymptotic stability of the tracking error while maintaining the stability of the closed-loop system. The solvability of the output regulation problem depends on the solvability of a set of matrix equations called the regulator equations. First, a restructured dynamic system is established by using the Smith predictor; then, an off-policy algorithm based on reinforcement learning is developed to calculate the feedback gain using only the measured data when dropout occurs. Based on the solution to the feedback gain, a model-free solution is provided for solving the forward gain using the regulator equations. The simulation results demonstrate the effectiveness of the proposed approach for discrete-time networked systems with unknown dynamics and dropout.

An optimal hybrid learning approach for attack detection in linear networked control systems

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems &#x0028 NCS &#x0029 , wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold, followed by an estimation scheme for the signal injected by the attacker. A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics. The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator. The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived. Simulations have been performed to demonstrate the effectiveness of the proposed schemes.

Periodic event-triggered control for networked control systems based on non-monotonic Lyapunov functions

This article considers exponential stabilization of linear Networked Control Systems with periodic event-triggered control for a given network specification in terms of a maximum number of successive dropouts and a constant transmission delay. Based on stability results using non-monotonic Lyapunov functions for discontinuous dynamical systems, two sufficient results for stability of the general model of a linear event-triggered Networked Control System are derived. Those results are used to derive robust periodic event-triggered control strategies. First, a static triggering mechanism for the case without delay is derived. Afterwards, two dynamic triggering mechanisms are developed for the case without and with delay. It is shown how a degree of freedom, being contained in the dynamic triggering mechanisms, can be used to shape the resulting network traffic. The applied adaption technique is motivated by existing congestion control mechanisms in communication networks. The properties of the derived mechanisms are illustrated in a numerical example.

Fuzzy-model-based fault detection for nonlinear networked control systems with periodic access constraints and Bernoulli packet dropouts

In this paper, the problem of fault detection for nonlinear networked control systems subject to both communication constraints and packet dropouts is investigated. Despite the huge bulk of studies in the area of linear networked control systems, nonlinear networked control systems have been less involved. In the proposed method, Takagi–Sugeno fuzzy model is employed to represent nonlinear networked control systems. To model the communication network, access constraints and packet dropouts are respectively considered as periodic sequences and Bernoulli processes. Based on the optimal periodic residual generator theory, a fuzzy-model-based observer is proposed to deal with the problem of fault detection for nonlinear networked control systems. Both residual evaluation and correspondence threshold are also investigated to generate confident fault alarms. Finally, the effectiveness of the proposed fault detection scheme for nonlinear networked control systems is illustrated and some simulation results are carried out to validate the proposed approach.

Distributed Secure Filtering for Discrete-Time Systems Under Round-Robin Protocol and Deception Attacks.

This paper addresses the problem of designing a distributed filter for linear discrete-time networked control systems that suffer from randomly occurring deception attacks and bounded disturbances. A Round-Robin-type protocol is applied to schedule communication between filters due to the communication resource constraint. The discrete-time Wirtinger's inequality is used to derive a sufficient condition that ensures H∞ performance of the closed-loop system. A corresponding filter that guarantees the security is designed. Finally, a numerical example of an inverted pendulum system is provided to demonstrate the feasibility of the proposed filter.

A comparison of compensation methods for random input data dropouts in networked iterative learning control system

In this paper, for a class of linear networked iterative learning control (ILC) systems, methods to compensate dropped input data in time or iteration domain are compared. Specifically, the transition matrices of input error at the controller side with the two methods are derived first, respectively. After that, the varieties of eigenvalues and elements in the lower triangular of the transition matrices are analyzed. Through analyzing the varieties, it can be easily found that the two methods guarantee the convergence of input error at the controller side, while only the compensation in iteration domain guarantees the convergence of input error at the actuator side. Due to the introduction of networks, the convergence of output error is determined by the input error at the actuator side. Hence, a conclusion could be made naturally that the output error converges to zero with compensation in iteration domain, while compensation in time domain cannot guarantee that. Finally, numerical experiments are given to corroborate the theoretical analysis.

Robust sampled‐data model predictive control for networked systems with time‐varying delay

SummaryIn this paper, the problem of sampled‐data model predictive control (MPC) is investigated for linear networked control systems with both input delay and input saturation. The delay‐induced nonlinearity is overapproximatively modeled as a polytopic inclusion. The nonlinear behavior of input saturation is expressed as a convex polytope. The resulting closed‐loop systems are represented as linear systems with polytopic and additive norm‐bounded uncertainties. The aim is to determine a robust MPC controller that asymptotically stabilizes the uncertain system at the origin with a certain level of quadratic performance. The effectiveness of the proposed algorithm is demonstrated by a numerical example.