Game-Theoretic and Inverse Reinforcement Learning-Based Control for Vehicle Formation Under DoS Attacks

Abstract

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.