This article addresses the optimal stabilization of unknown nonlinear networked industrial systems (NISs) with limited communication resources, proposing an innovative self-triggered approximate optimal control framework fused with generalized fuzzy hyperbolic model (GFHM) and reinforcement learning (RL) gradient descent. To handle unknown dynamics without prior model information, a GFHM-based state identifier is constructed, leveraging its universal approximation, flexible structure, and fewer parameters to adaptively reconstruct nonlinear dynamics. A self-triggered mechanism predicts the next control update instant via software, eliminating continuous hardware monitoring and reducing online computing cost, network data transmission overhead, and actuator energy usage. Within the RL framework, a critic neural network (NN) is established, with weights tuned via gradient descent to approximate the Hamilton-Jacobi-Bellman (HJB) equation solution and derive the approximate optimal control policy. Theoretical analysis proves the closed-loop signals are uniformly ultimately bounded (UUB). Numerical simulations validate the scheme's effectiveness in ensuring optimal control performance while saving resources.
Jian Liu, Jingjing Xia, Lijuan Zha et al.· IEEE Transactions on Industr...· 0 citations
To address the performance degradation in vehicle formation control caused by communication disruptions under denial-of-service (DoS) attacks, this article proposes a secure control method that integrates a nonzero-sum game and inverse reinforcement learning (IRL). At the game-theoretic layer, a dynamic game model accounting for the attacker’s energy cost is constructed to capture the adversarial interaction and strategy spaces between DoS attacks and the formation controller, with an approximate Nash equilibrium solution derived for both attack and defense strategies. At the reward learning layer, an IRL approach is introduced to adaptively learn the weight parameters of multiobjective performance indices from offline demonstration data, thereby mitigating the sensitivity of system performance to manual weight tuning. At the control implementation layer, based on the learned reward weights, a neural network (NN) is employed to approximate the solution of the Hamilton–Jacobi (HJ) equation, thereby constructing a computable feedback control law. Simulation results demonstrate that the proposed method can effectively suppress formation tracking errors in DoS attack scenarios and exhibits superior robustness, adaptability, and energy efficiency compared to traditional methods.
Xiaoping Zhao, Jia Guo, Jinliang Liu et al.· IEEE Internet of Things Jour...· 0 citations
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