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Adaptive critic design for safety-optimal FTC of unknown nonlinear systems with constrained

ISA TRANSACTIONS 2024年 155卷 309-318页

作者： Zhang, Dehua Wang, Yuchen Meng, Lei Yan, Jiayuan Qin, Chunbin Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China

Safe fault tolerant control is one of the key technologies to improve the reliability of dynamic complex nonlinear systems with limited inputs, which is hard to solve and definitely a great challenge to tackle. Thus the paper presents a novel safety-optimal FTC (Fault Tolerant Control) approach for a category of completely unknown nonlinear systems incorporating actuator fault and asymmetric constrained-input, which can guarantee the system's operation within a safe range while showcasing optimal performance. Firstly, a CBF (Control Barrier Function) is incorporated into the cost function to penalize unsafe behaviors, and then we translate the intractable safety-optimal FTC problem into a differential ZSG (Zero-Sum Game) problem by defining the control input and the actuator fault as two opposing sides. Secondly, a neural-network-based identifier is employed to reconstruct system dynamics using system data, and the resolution of handling asymmetric constrained-input with the introduced non-quadratic cost function is achieved through the design of an adaptive critic scheme, aiming to reduce computational expenses accordingly. Finally, through the theoretical stability analysis, it is demonstrated that all signals in the closed-loop system are consistently UUB (Uniformly Ultimately Bounded). Furthermore, the proposed method's effectiveness is also verified in the simulation experiments conducted on a model of a single-link robotic arm system with actuator failure. The result shows that the algorithm can fulfill the safety-optimal demand of fault tolerant control in fault system with asymmetric constrained-input.

关键词： Fault tolerant control Safety-optimal asymmetric constrained-input Adaptive dynamic programming Nonlinear systems

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Event-triggered H∞ control for unknown constrained nonlinear systems with application to robot arm

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APPLIED MATHEMATICAL MODELLING 2025年 144卷

作者： Qin, Chunbin Jiang, Kaijun Wang, Yuchen Zhu, Tianzeng Wu, Yinliang Zhang, Dehua Henan Univ Sch Artificial Intelligence Zhengzhou 450000 Peoples R China

In this paper, an event-triggered safe H-infinity, control approach is investigated for nonlinear continuous-time systems with asymmetric constrained-input and state constraints. The proposed method is based on adaptive dynamic programming and addresses systems with completely unknown dynamics. Firstly, the unknown dynamics is identified using three neural networks. Secondly, a novel nonquadratic type function is introduced to address the asymmetric constrained- input. Next, the intention behind integrating the value function with the control barrier function is to guide the system state to evolve within the safe area. This also leads to a novel safe Hamilton-Jacobi-Isaacs equation. Next, the event-triggered condition is established with a designated threshold, ensuring the system stability. Unlike the classical actor-critic neural network approach, we only require a critic neural network to estimate the safe Hamilton-Jacobi-Isaacs equation, thereby achieving online solution under state constraints. Utilizing the Lyapunov stability approach and considering the joint impact of asymmetric constrained-input and state constraints, the system state and critic neural network weights exhibit uniformly ultimately bounded, effectively eliminating Zeno behavior. In conclusion, the efficacy of the proposed scheme is demonstrated through a simulation example involving a robot arm system.

关键词： asymmetric constrained-input Adaptive dynamic programming H-infinity control Safety Event-triggered control Robot arm system

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