AT present,the world is experiencing profound transfor-mations unseen in a century,with a new round of scien-tific and technological revolution increasingly inter-twined with major-power competition.Against this backdrop,artificial intelligence(AI)has emerged as a key strategic technology that is profoundly reshaping global economic,social,and military systems.Through top-level policy design and national devel-opment planning,China has been continuously promoting the deep integration of AI with industry,energy,education,and other key domains.Therefore,the intelligent era should not be regarded as a future prospect,but rather as an ongoing transformation that is already reshaping technological development and social progress.
Dear Editor, This letter is concerned with the robust tracking control problem of automated vehicles(AVs).First,the dynamics of an AV is con-structed by taking into account model uncertainties.Then,to guaran-tee the successful completion of tracking tasks,an actor-critic learn-ing-based robust tracking control scheme is designed.At last,formal stability analysis and experiment results are provided to verify the tracking performance of the designed control scheme.
Dear Editor, Nonlinear dynamic system is difficult to model by traditional mod-eling methods,because of high nonlinearity and limited precision.Koopman operator,an effective technique,maps nonlinear dynamic systems to a higher-dimensional linear space,allowing complex non-linear behavior to be described linearly.However,conventional Koopman-based approaches often suffer from computational com-plexity,this paper employs a deep neural network with random search to learn feature mappings and estimates the Koopman opera-tor by subspace identification.Experimental results show the approach simplifies modeling and significantly improves modeling accuracy over traditional methods.
Dear Editor, The crafted generalized replay attacks(GRAs)can bypass tradi-tional multiplicative watermarking(MW)-based active detection in networked control systems(NCSs),which will seriously destroy sys-tem stability or even crash.To address this problem,this letter pro-poses a novel secure control method by using MW-based detection and data compensation.First,the limitation of traditional MW-based detection method is analysed,and a novel MW-based active detec-tion scheme is proposed by adding an irreversible watermarking detection unit.Then,according to the detection result,an online data compensation scheme based on cubic spline interpolation algorithm is provided,and the maximum allowed attack rate of GRAs is given to maintain the exponential stability of NCSs.Finally,experimental results confirm the effectiveness of the proposed method.
Dear Editor, This letter deals with the security control for nonlinear cyber-physical systems (CPSs) under mixed deception attacks. Both sensors and actuators are assumed to be injected deception data during the data transmission via networks. In order to identify the unknown dynamics of the attacked system, a neural network (NN) is adopted, on basis of which an NN-based secure observer is designed to diminish the attack impact on state estimation. Then, by resorting to the reinforcement learning approach, the secure control strategy is presented via actor-critic and zero-sum games. At last, the designed control scheme is proved via a numerical simulation.
