Static output‐feedback synchronisation of multi‐agent systems: a secure and unified approach

Modares, Hamidreza; Moghadam, Rohollah; Lewis, Frank L.; Davoudi, Ali

doi:10.1049/iet-cta.2017.1068

Cited by 14 publications

(12 citation statements)

References 69 publications

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“…Since Equations (3) and (4) are now both delay free, the LQR result for delay‐free process can then be applied. It is well known that [34–37] the LQR solution to process (4) is

\begin{equation} {\bm {u}^{m}}(t)=-{\bm {R}^{-1}}{\bm {B}^{T}}\bm {P}\bm {X}(t),\ \end{equation}

where

\bm {P}

is the positive definite solution of the Riccati equation:

\begin{equation} {\bm {A}^{T}}\bm {P}+\bm {PA}+\bm {Q}-\bm {PB}{\bm {R}^{-1}}{\bm {B}^{T}}\bm {P}=\bm {0}.\ \end{equation}

Theorem For the linear system without delay Equation (4), the necessary and sufficient conditions for its optimal control signal is Equation (5), which minimizes the performance index in Equation (2). The optimal control signal u m ( t ) exists and is unique.…”

Section: The Design Theory Of Pid Type‐ii and Type‐iii Control Loopsmentioning

confidence: 99%

Extending the LQR to the design of PID type‐ii and type‐iii control loops

Liu

Duan

Zhang

et al. 2022

IET Control Theory & Appl

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This paper concerns the improvement on Proportional-Integration-Derivative (PID) control for standard second-order plus time-delay systems (SOPTD). To achieve higher tracking precision and stronger disturbance rejection, a PID type-ii and type-iii controlloops design method based on the combination of the linear quadratic regulator (LQR) and dominant pole configuration technology is proposed in this paper. The PID type-ii control loop is capable of achieving perfect tracking of step and ramp reference signals with zero steady-state position and velocity error. The PID type-iii control loop is capable of achieving perfect tracking of step, ramp and parabolic reference signals with zero steady-state position, velocity and acceleration error. The effectiveness of the proposed PID type-ii and type-iii controller parameters tuning rules has been demonstrated via simulation of over-damped, critical-damped and under-damped systems. To be specific, compared with the PID type-i control loop, rise time, settling time and disturbance rejection property of the system have been significantly improved while realizing faster reference signal tracking. Finally, the effects of non-dominant pole on the stability and robustness of PID type-ii and PID type-iii closed loop systems have also been discussed.

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“…Since Equations (3) and (4) are now both delay free, the LQR result for delay‐free process can then be applied. It is well known that [34–37] the LQR solution to process (4) is

\begin{equation} {\bm {u}^{m}}(t)=-{\bm {R}^{-1}}{\bm {B}^{T}}\bm {P}\bm {X}(t),\ \end{equation}

where

\bm {P}

is the positive definite solution of the Riccati equation:

\begin{equation} {\bm {A}^{T}}\bm {P}+\bm {PA}+\bm {Q}-\bm {PB}{\bm {R}^{-1}}{\bm {B}^{T}}\bm {P}=\bm {0}.\ \end{equation}

Section: The Design Theory Of Pid Type‐ii and Type‐iii Control Loopsmentioning

confidence: 99%

Extending the LQR to the design of PID type‐ii and type‐iii control loops

Liu

Duan

Zhang

et al. 2022

IET Control Theory & Appl

View full text Add to dashboard Cite

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“…The off-policy RL algorithm presented by iterating on (35) to solve the non-homogeneous game ARE, is listed in Algorithm 1.…”

Section: Model-free Resilient Off-policy Rl For Solving Optimalmentioning

confidence: 99%

“…Resilient control protocols for MASs have been designed in the literature [23]- [35] to mitigate attacks. Most of the existing approaches either use the discrepancy among the state of agents and their neighbors to detect and mitigate attacks, or use an exact model of agents to predict expected normal behavior and, thus, detect an abnormality caused by attacks.…”

Section: Introductionmentioning

confidence: 99%

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments

Moghadam

Modares

2019

IEEE Trans. Cybern.

Self Cite

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An autonomous and resilient controller is proposed for leader-follower multiagent systems under uncertainties and cyber-physical attacks. The leader is assumed nonautonomous with a nonzero control input, which allows changing the team behavior or mission in response to the environmental changes. A resilient learning-based control protocol is presented to find optimal solutions to the synchronization problem in the presence of attacks and system dynamic uncertainties. An observer-based distributed H∞ controller is first designed to prevent propagating the effects of attacks on sensors and actuators throughout the network, as well as to attenuate the effect of these attacks on the compromised agent itself. Nonhomogeneous game algebraic Riccati equations are derived to solve the H∞ optimal synchronization problem and off-policy reinforcement learning (RL) is utilized to learn their solution without requiring any knowledge of the agent's dynamics. A trust-confidence-based distributed control protocol is then proposed to mitigate attacks that hijack the entire node and attacks on communication links. A confidence value is defined for each agent based solely on its local evidence. The proposed resilient RL algorithm employs the confidence value of each agent to indicate the trustworthiness of its own information and broadcast it to its neighbors to put weights on the data they receive from it during and after learning. If the confidence value of an agent is low, it employs a trust mechanism to identify compromised agents and remove the data it receives from them from the learning process. The simulation results are provided to show the effectiveness of the proposed approach.

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“…Therefore, unforeseen adverse conditions such as uncertainties or attacks can easily result in system instability and prohibit the accomplishment of the system objective [12]. There are two types of attacks on MASs, attack to connection links [13,14] and attack to agent's dynamics [15,16]. To mitigate the effects of attacks on agent's dynamics, a weighted mean subsequence-reduced (W-MSR) algorithm is used.…”

Section: Introductionmentioning

confidence: 99%

Distributed resilient flocking control of multi‐agent systems through event/self‐triggered communication

Amirian

Shamaghdari

2021

IET Control Theory & Appl

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This paper proposes a distributed control scheme for multi‐agent systems to achieve resilient flocking behaviour via event‐based communication. The control scheme can provide the required connectivity conditions to form a flock in the presence of cyberattacks in the network. This method is presented in a fully distributed manner to avoid the use of global data. The developed event‐triggered update rules can mitigate the influence of the non‐cooperative agents with the weighted mean subsequence‐reduced algorithm and reduce unnecessary communication among them. It is proved that under the proposed method, convergence is guaranteed and there is no Zeno behaviour exhibited inherently. To relax the requirement of continuous monitoring of self‐state, a self‐triggered mechanism is proposed further. Simulation results are given to illustrate the theoretical analysis and show the advantages of the event‐ and self‐triggered controllers.

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Static output‐feedback synchronisation of multi‐agent systems: a secure and unified approach

Cited by 14 publications

References 69 publications

Extending the LQR to the design of PID type‐ii and type‐iii control loops

Extending the LQR to the design of PID type‐ii and type‐iii control loops

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments

Distributed resilient flocking control of multi‐agent systems through event/self‐triggered communication

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