Adaptive event‐triggered control for MIMO nonlinear systems with asymmetric state constraints based on unified barrier functions

Xia, Jianwei; Park, Ju H.; Shen, Hao; Chen, Guoliang

doi:10.1002/rnc.5776

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…However, the MPC method may suffer from a heavy online computational burden for high‐dimensional nonlinear systems 9 . Barrier Lyapunov functions (BLFs) are widely used to control nonlinear systems with output and state constraints 10–12 . However, the system states are not allowed to go beyond the boundary of the constrained region when using BLF methods 13 .…”

Section: Introductionmentioning

confidence: 99%

“…9 Barrier Lyapunov functions (BLFs) are widely used to control nonlinear systems with output and state constraints. [10][11][12] However, the system states are not allowed to go beyond the boundary of the constrained region when using BLF methods. 13 Invariance control, wherein invariance functions can be defined in the entire state space, is an alternative way to handle state constraints.…”

Section: Introductionmentioning

confidence: 99%

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High‐order control barrier functions‐based optimization control for time‐varying nonlinear systems with full‐state constraints: A dynamic sub‐safe set approach

Wang

Peng

et al. 2023

Intl J Robust & Nonlinear

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Summary This study focuses on the problems of safety control and stabilization for time‐varying nonlinear systems in the presence of time‐varying full‐state constraints. High‐order control barrier functions (HoCBFs) without the assumption of forward completeness are introduced to prevent high‐relative degree constraint violations. The activated HoCBFs, which determine the dynamic sub‐safe sets, are proposed to indicate that the state constraints are already or about to be violated. Subsequently, the HoCBFs‐based controller is designed to obtain the admissible control set by utilizing the quadratic programming (QP) with activated HoCBFs conditions to synthesize the nominal controller. An explicit analytical expression is provided for the HoCBFs‐based controller. The input‐to‐state stable (ISS) property of the closed‐loop system under the proposed controller is investigated to ensure that all closed‐loop states remain bounded and achieve asymptotic tracking without violation of the constraint. Moreover, a Lyapunov‐like constraint is added to the HoCBFs‐based QP problem such that the solvability of the QP problem is a sufficient condition for the system ISS. The effectiveness of the proposed HoCBFs‐based control method for time‐varying nonlinear systems is illustrated using a numerical example.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High‐order control barrier functions‐based optimization control for time‐varying nonlinear systems with full‐state constraints: A dynamic sub‐safe set approach

Wang

Peng

et al. 2023

Intl J Robust & Nonlinear

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“…Recently, barrier functions (BFs) are popularly used to improve the safety of the policies in the learning‐based methods, 17 due to the ability that BFs can ensure the system safety by establishing a safe set. In References 18‐21, BFs are used to transform the state constrained system to the state unconstrained system, and then an actor‐critic structure is introduced to learn the safe policy for the equivalent transformed system. But the above BF‐based methods do not consider the conflict between the stability and the safety.…”

Section: Introductionmentioning

confidence: 99%

Safe reward‐based deep reinforcement learning control for an electro‐hydraulic servo system

Liu

et al. 2022

Intl J Robust & Nonlinear

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In this article, a safe deep reinforcement learning (DRL) control method based on a safe reward shaping method is proposed and applied to the constrained control for an electro-hydraulic servo system (EHSS). The proposed control method improves the safety of the constrained control for a nonlinear system with the minimal intervention to the optimization of the performance objective, while the convergence speed of the DRL process has accelerated. By introducing control barrier functions (CBFs) to the reward shaping, a CBF-based potential difference term is designed to shape the safe reward, which not only provides the safe guidance for the DRL process by encoding the safety constraints of the nonlinear system, but also considers effects of the complex safety transformation on the convergence process in the DRL. Then the safe reward-based DRL control method is presented to learn the optimal safety policy of position tracking for the EHSS with position error constraints by planning and optimizing the safety together with the performance objective. Theoretical analysis is given to demonstrate that the proposed control method with the safe reward can achieve the optimal safety performance for the constrained control system.Experimental results of the constrained control for the EHSS with system uncertainties and perturbations are also exhibited, to show that the proposed control method converges fast and performs safer and better than the conventional control methods. K E Y W O R D Sconstrained control, control barrier function, deep reinforcement learning, electro-hydraulic servo system, safe reward shaping INTRODUCTIONDeep reinforcement learning (DRL) has been well used for nonlinear systems with uncertainties and complex dynamics, such as controlling robots 1-3 and self-driving vehicles, 4,5 because of its powerful ability to capture features from high-dimensional data and learn complicated control policies. 6 Several classical DRL algorithms including asynchronous advantage actor-critic (A3C), 7 deep Q-network, 8 and deep deterministic policy gradient (DDPG), 9 have been developedMinling Wu and Lijun Liu are co-first authors.

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Fuzzy adaptive event-triggered finite-time constraint control for output-feedback uncertain nonlinear systems

Wang

Yan

et al. 2022

Fuzzy Sets and Systems

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Adaptive event‐triggered control for MIMO nonlinear systems with asymmetric state constraints based on unified barrier functions

Cited by 6 publications

References 45 publications

High‐order control barrier functions‐based optimization control for time‐varying nonlinear systems with full‐state constraints: A dynamic sub‐safe set approach

High‐order control barrier functions‐based optimization control for time‐varying nonlinear systems with full‐state constraints: A dynamic sub‐safe set approach

Safe reward‐based deep reinforcement learning control for an electro‐hydraulic servo system

Fuzzy adaptive event-triggered finite-time constraint control for output-feedback uncertain nonlinear systems

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