Data-driven Iterative Learning For Safe Tracking Contro

In network control systems,the addition of communication network will cause many inevitable network phenomena,such as data dropouts,data quantization,and malicious network attack.Malicious network attacks can be roughly divided into denial-of-service attacks,false data injection attacks and replay attacks.When network control systems are attacked,the system control performance will be reduced even unstable,so that the expected control objectives cannot be achieved.Therefore,this paper mainly studies the security tracking control problem of nonlinear network control systems.The main contents and contributions of this paper include the following aspects:Firstly,the problem of the quantized data-based iterative learning tracking control for nonlinear network control systems with signal quantization and denial-of-service attacks is studied.The quantizer considered here is static with the logarithmic form.An estimate output attack compensation mechanism is designed to compensate for the effect of denialof-service attacks based on the extended dynamic linearization method.Then,a quantized data-based iterative learning tracking control algorithm is developed to guarantee the system tracking performance and the bounded input and bounded output stability in the mean-square sense.The process of designing the quantized data-based iterative learning tracking control algorithm only uses the input and output data of the system,and the proof of which uses the compression mapping principle and the mathematical induction.The effectiveness of the proposed quantized data-based iterative learning tracking control algorithm is illustrated by a numerical simulation.Secondly,the problem of data-based secure fault-tolerant iterative learning control for Nonlinear network control systems under sensor failure and denial-of-service attacks is studied.The radial basis function neural network is used to approximate the sensor failure function and an attack compensation mechanism is proposed in the iterative domain to reduce the impact of denial-of-service attacks.Then,using the dynamic linearization technology,the nonlinear system considering failures and network attacks is transformed into a linear data model.Further,based on the designed linearization model,a new databased secure fault-tolerant iterative learning control algorithm is designed to ensure the satisfactory tracking performance of the system.This process only uses the input and output data of the system,and the stability of the system is proved by using the compression mapping principle.A numerical simulation is used to illustrate the effectiveness of the proposed secure fault-tolerant iterative learning control algorithm.Finally,we propose a model-free adaptive control method to solve the vehiclefollowing problem of multi-vehicle systems.The considered system is affected by the nonlinear acceleration uncertainties and the unknown bounded inputs,which will lead to the performance degradation and the modeling difficulty of the vehicle system.To solve the problem,the model-free adaptive control method is used to establish an equivalent data model based on the input/output data of the vehicle system.Then,an adaptive controller is designed to control the vehicle to keep the distance between vehicles relatively uniform.A numerical example is presented to testify the effectiveness of the developed method.