Adaptive Resilient Event-Triggered Control Design of Autonomous Vehicles With an Iterative Single Critic Learning Framework

Zhang, Kun; Su, Rong; Zhang, Huaguang; Tian, Yunlin

doi:10.1109/tnnls.2021.3053269

Cited by 95 publications

(30 citation statements)

References 42 publications

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“…Hence, similar to the analysis in Reference 21, we have the following lemma: Lemma 1. For the nominal plant (3), when the control gain K is obtained via (18) to fulfill (19), then the optimal control (15) is globally optimal.…”

Section: Ofoc Formulationmentioning

confidence: 99%

“…The above PL algorithm is run in an offline though its convergence can be guaranteed. Since we may not find a globally OFOC solution to generic systems, which means that the gain K cannot be found to fulfill (18) and (19). To address this problem, an extra matrix K 2 is introduced into ( 18) and ( 19) for relaxing the condition, leading to a suboptimal solution.…”

Section: (Policy Update) For Any Calculated K I Update Matrix P I Vi...mentioning

confidence: 99%

“…Among the existing PL technique based optimal control methods, [15][16][17][18] the all system states are required. In fact, the output-feedback optimal control (OFOC) problem has been a challenging problem, especially for the output-feedback robust control (OFRC) problem with uncertain system dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Output‐feedback robust control of systems with uncertain dynamics via data‐driven policy learning

Zhao

2022

Intl J Robust & Nonlinear

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In this article, we develop two online learning techniques to address the output‐feedback robust control problem of systems with uncertain dynamics. For this purpose, an equivalence is first constructed between the robust control problem of the uncertain systems and the optimal control problem of the nominal systems. Then, an output algebraic Riccati equation (OARE) is constructed using its state‐feedback control counterpart, which can be adopted to realize the online learning. To obtain the online solution of the OARE, an online policy learning (PL) algorithm based on the state reconstruction (SR) is first presented, where the unknown system internal states can be reconstructed via using the input–output information. To further relax the requirement on the system internal states, a novel online PL method is designed, where only the system output information is required, thus the observer or SR is removed in this online PL algorithm. Simulations are provided to test the developed online learning methods.

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Section: Ofoc Formulationmentioning

confidence: 99%

Section: (Policy Update) For Any Calculated K I Update Matrix P I Vi...mentioning

confidence: 99%

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Output‐feedback robust control of systems with uncertain dynamics via data‐driven policy learning

Zhao

2022

Intl J Robust & Nonlinear

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“…Additionally, ML-based ETC is applied to systems that are subject to denial of service attacks. For example, in [44] an iterative single critic learning framework is used in conjunction with ETC to consider the denial-of-service attack for autonomous driving systems, which effectively balances the frequency and changes in adjusting the vehicle's control during the running process. A single critic network is designed to approximate the optimal cost function and obtain an HJB solution.…”

Section: A Reinforcement Learning -Critic-onlymentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

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Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

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“…However, all these papers cannot be applied to nonholonomic mobile robots due to the underactuation problem. Although an adaptive resilient event-triggered control method at the kinematic level was proposed for single autonomous vehicles with DoS attacks [19], performance degradation due to uncertainty arising from the dynamic model is inevitable. Moreover, the deception attack problem was not considered in [19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Secure Control for Leader-Follower Formation of Nonholonomic Mobile Robots in the Presence of Uncertainty and Deception Attacks

Park

Yoo

2021

Mathematics

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This paper addresses an adaptive secure control problem for the leader-follower formation of nonholonomic mobile robots in the presence of uncertainty and deception attacks. It is assumed that the false data of the leader robot’s information attacked by the adversary is transmitted to the follower robot through the network, and the dynamic model of each robot has uncertainty, such as unknown nonlinearity and external disturbances. A robust, adaptive secure control strategy compensating for false data and uncertainty is developed to accomplish the desired formation of nonholonomic mobile robots. An adaptive compensation mechanism is derived to remove the effects of time-varying attack signals and system uncertainties in the proposed control scheme. Although unknown deception attacks are injected to the leader’s velocities and the model nonlinearities of robots are unknown, the boundedness and convergence of formation tracking errors of the proposed adaptive control system are analyzed in the Lyapunov sense. The validity of the proposed scheme is verified via simulation results.

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Adaptive Resilient Event-Triggered Control Design of Autonomous Vehicles With an Iterative Single Critic Learning Framework

Cited by 95 publications

References 42 publications

Output‐feedback robust control of systems with uncertain dynamics via data‐driven policy learning

Output‐feedback robust control of systems with uncertain dynamics via data‐driven policy learning

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Adaptive Secure Control for Leader-Follower Formation of Nonholonomic Mobile Robots in the Presence of Uncertainty and Deception Attacks

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