Distributed Intelligent Security Control For Nonlinear Multi-agent Systems Under The Two Kinds Of Network Attacks

Due to the advantages of strong robustness,easy scalability and flexible and simple controller design,multi-agent systems are widely applied in practical engineering.Especially,in the distributed control of multi-agent systems,the data transmission between agents in the same network topology is essential.Because of the complexity of the network topology environment,the data transmission between agents is exposed to the risk of network attacks.Therefore,it is very important to propose a distributed secure control algorithm to keep the multi-agent systems stability under network attacks.To sum up,this thesis uses the backstepping method based on the fuzzy logic systems(FLSs)technology/neural networks(NNs)technology to design different distributed security control algorithms for the nonlinear multi-agent systems under denial of service(DoS)attacks/false data injection(FDI)attacks.The main results are shown as follows:(1)The adaptive secure containment control problem for a class of uncertain nonlinear multi-agent systems with the output constraint requirements under DoS attacks is researched.At first,the barrier Lyapunov functions are introduced in the backstepping process to deal with the output constraints problem of each agent.Then,a state estimator is designed,which reconstructs the immeasurable states of the multi-agent systems and approximates the completely unknown nonlinearities arising from the agents.In addition,the dynamic surface control technique is used to solve the “explosion of complexity” problem.To overcome the difficulty that the tracking performance of the nonlinear multi-agent systems under the DoS attacks is disturbed seriously,a novel adaptive secure containment control approach is presented by applying a DoS attacks detection mechanism,which enables the system to achieve the security control objective that the output of each agent eventually converges to the convex hull spanned by the dynamic leaders’ outputs,while never violating the output constraints.It is demonstrated that the proposed anti-attack controller ensures that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded.Finally,a simulation example is provided to illustrate the effectiveness of the theoretical results.(2)This paper considers the distributed finite-time consensus tracking control problem for nonlinear multi-agent systems with false data injection(FDI)attacks.Firstly,since instability is inevitable when false data is injected into the researched system,a security control algorithm combining the modified coordinate transformation and the dynamic surface control(DSC)method is developed to eliminate the negative impact of FDI attacks and reduce the burden caused by iterative computations of virtual controls.Besides,there are unknown nonlinearities in the dynamics of each agent,which can not be directly handled by the traditional control design.Thus,the neural-network-based approximation technique is introduced to compensate the unknown nonlinearities.In summary,a novel controller is constructed for each agent,which guarantees the consensus of the outputs of all agents and that all the signals of the studied system are bounded within a finite-time period.To prove the correctness of the designed security control algorithm,a simulation example of the multi-agent system composed of multiple single-link robots is presented.The intelligent security control of nonlinear multi-agent systems is still in the preliminary exploration stage.This thesis only proposes the effective intelligent security control algorithms for the nonlinear multi-agent systems under the DoS attacks /FDI attacks.There are still many scientific problems to be solved in the study of the nonlinear multi-agent systems under network attacks,such as the security control of the systems under replay attacks,the security control of the systems under multiple network attacks.