Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach

Liu, Derong; Wang, Ding; Li, Hongliang

doi:10.1109/tnnls.2013.2280013

Cited by 304 publications

(79 citation statements)

References 49 publications

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“…The main bottleneck in deploying successful distributed MASs is designing secure control protocols that can learn about system uncertainties while showing some level of functionality in the presence of cyber-physical attacks. Reinforcement learning (RL) [7]- [9], inspired by learning mechanisms observed in mammals, has been successfully used to learn optimal solutions online in single agent systems for both regulation and tracking control problems [10]- [16] and recently for MASs [17]- [19]. Existing RL-based controllers for leader-follower MASs assume that the leader is passive and without any control input.…”

Section: Introductionmentioning

confidence: 99%

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments

Moghadam

Modares

2019

IEEE Trans. Cybern.

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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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Section: Introductionmentioning

confidence: 99%

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments

Moghadam

Modares

2019

IEEE Trans. Cybern.

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“…Recently, the optimal control problems of nonlinear interconnected systems have got some important results, such as in related works [33][34][35][36][37][38][39][40][41][42] and references herein. Other works [33][34][35] have proposed a decentralized optimal stabilization control strategy for a class of continuous-time nonlinear interconnected large-scale systems. Qu et al 36 developed the state-feedback decentralized tracking problems for a class of nonlinear large-scale interconnected systems.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based fuzzy adaptive optimal control for nonlinear continuous‐time saturation interconnected systems

Wang

Tong

2018

Optim Control Appl Methods

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Summary A fuzzy adaptive optimal output‐feedback control problem is investigated for nonlinear continuous‐time interconnected systems with saturation constraints. The system dynamics and the mismatched interconnections of each subsystem are unknown, and each subsystem contains unavailable states. Based on fuzzy logic systems, a fuzzy state observer is designed to approximate the unknown system states and the interconnections of the nonlinear interconnected system. By using adaptive dynamic programming technology, the decentralized optimal controller of each subsystem is given and the saturation control input is updated via the critic fuzzy logic systems. The proposed fuzzy adaptive observer‐based optimal control scheme can guarantee that the closed‐loop interconnected system is ultimately uniformly bounded stable. A simulation example validates the effectiveness of the presented scheme.

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“…It is well known that optimal control problems have received increasing attention and some valuable results have been developed (see other works [13][14][15][16][17][18][19][20][21][22][23] and the references therein). In traditional optimal control of nonlinear systems, the Hamilton-Jacobi-Bellman (HJB) equation usually needs to be solved, but obtaining the solution of the HJB equation is challenging and difficult because a closed-form analytical method does not exist in it.…”

Section: Introductionmentioning

confidence: 99%

“…Then, Liu et al 22 extended the SISO nonlinear system to a nonlinear large-scale system and proposed a decentralized optimal control scheme. In addition, the control approaches in the aforementioned works [18][19][20][21][22] can be applied only to nonlinear systems with known functions. Although Yang et al 23 proposed an ADP-based optimal control method for nonlinear systems with unknown functions, this adaptive optimal control method limits the controlled nonlinear systems from satisfying the matching condition [18][19][20][21][22] and other optimal methods, eg, H ∞ control 24 and reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Adaptive‐dynamic‐programming‐based fuzzy control for triangular structure nonlinear uncertain systems with unknown time delay

Sui

Tong

Sun

2017

Optim Control Appl Methods

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Summary This paper develops a novel fuzzy adaptive backstepping dynamic surface control scheme for a class of strict‐feedback nonlinear systems with unknown time delay based on adaptive dynamic programming. The whole control scheme is composed of 2 parts, ie, feedforward control and feedback optimal control. Fuzzy logic systems are used to identify nonlinear systems. The control design method, which combined the backstepping design technique and the dynamic surface control approach, is applied to convert triangular structure systems to affine nonlinear systems; it not only can solve the problem of “explosion of complexity” but also can avoid the appearance of unknown terms. The closed‐loop system is guaranteed to be uniformly ultimately bounded based on the Lyapunov and optimal control theory, and the system output can track the desired reference signal in an optimal manner. The simulation results of numerical examples are presented to show the effectiveness of the proposed approach.

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Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach

Cited by 304 publications

References 49 publications

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments

Observer‐based fuzzy adaptive optimal control for nonlinear continuous‐time saturation interconnected systems

Adaptive‐dynamic‐programming‐based fuzzy control for triangular structure nonlinear uncertain systems with unknown time delay

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