In this paper, a distributed security cooperative control for heterogeneous connected systems based on uncertainty and cyber-attacks identification is proposed. First, for the uncertainty, nonlinear disturbance observer is designed to realize the identification of uncertainty. Then, to compensate for the effects of cyber-attacks, an adaptive identification method of cyber-attacks is proposed through learning-based attack approximation approach. Furthermore, the distributed cooperative control strategy of heterogeneous connected systems under the compensation of cyber-attacks and uncertainty is presented via distributed backstepping approach. Finally, the result analysis shows the effectiveness of the designed algorithm.

This article proposes a distributed finite-time prescribed performance (FTPP) control strategy with the adaptive neural network (NN) compound approximation method to achieve the cooperative control of multiple autonomous aerial vehicle systems (MAAVSs). The deceptive and injective cyber-attacks on sensors and actuators of MAAVS are considered. The attitude controller is designed based on the distributed backstepping law, by which the stepwise virtual control signals were designed to control the attitude states of the AAVs. The FTPP control scheme predefines the error boundary and the convergence time to constrain the cooperative tracking errors in a small neighborhood and the settling time. Then, the MAAVS are controlled to track the given reference trajectory rapidly. In addition, to obtain information on the actuator and sensor attacks, the NN method is synthesized with the adaptive law to approximate the attacks and alleviate the undesirable effects. The Lyapunov method was employed to analyze and prove the stability of distributed cooperative errors under AAV communication network described by graph theory. The conducted experiments verify the effectiveness and the superiority of the proposed control algorithm.

Inspired by the fusion of state optimization and finite-time convergence, the finite-time optimal control (FTOC) for the affine-form nonlinear systems is investigated in this article. To achieve optimal stability with finite response time, a novel finite-time adaptive dynamic programming (FTADP) is presented for the affine-form nonlinear systems. By mapping the value function into finite-time stability space with the transformation function, the Bellman equation with finite-time stability space is first obtained. Then, by solving the Hamilton-Jacobi-Bellman (HJB) equation, the new FTOC strategy is presented with the theoretical finite-time stability description. Furthermore, to solve the above optimal controller with nonlinearity characteristic, the novel adaptive dynamic programming (ADP) based on the finite-time critic-actor offline neural network (NN) approximation algorithm is implemented, and the corresponding finite-time convergence characteristic is illustrated theoretically. Eventually, the application analysis on the circuit systems shows that the proposed FTADP has superiority compared with general optimal control.

The interconnected power systems (IPSs) generally with complex communication topology are sensitive to cyber-attacks. In this article, the problem of cyber-attacks on the IPSs is considered, and distributed remote secure adaptive dynamic programming (DRSADP) is proposed. Considering the cyber-attacks on the nodes of the sensor and actuator networks of the IPSs, the distributed remote estimators (REs) are firstly constructed based on the unscented Kalman filter (UKF) and the cyber-attacks models for the sensor and actuator networks are constructed. Then, to compensate for the influence of cyber-attacks and optimize the system state, the estimator-based distributed optimal control strategy is proposed. By constructing the control objective with the control performance index and estimation performance index, the novel optimal attack compensation approach is proposed. Furthermore, the distributed RE-based adaptive dynamic programming with the online finite-time actor-critic algorithm is implemented to solve the secure control strategy effectively. To illustrate the stability of the IPSs with the proposed control strategy, the property of the proposed control scheme is analyzed theoretically, and the finite-time convergence performance with the finite-time gradient learning algorithm is illustrated, which is different from the asymptotic convergence for the existing work. Finally, the results analysis shows that the proposed secure control strategy for IPSs with 9-machines has superiority compared with existing work and the RTLAB-dSPACE platform is applied to further implement the effect of the proposed method.

The cooperative tracking control of multiple unmanned aerial vehicle systems (MUAVSs) is a hotspot with its extensive applications in many areas, and a higher efficiency and more stable system is urgently demanded. To satisfy the needs, this article proposes a distributed-estimator-based (DE-based) finite-time prescribed performance control strategy to improve the convergence time and reduce the overshoot of tracking errors simultaneously. The finite-time prescribed performance function (FTPPF) constrains the convergence boundary and settling time for the cooperative tracking errors of follower UAVs, and the prescribed performance dynamic procedure can be obtained. To further achieve the cooperative operation of the MUAVS, the finite-time prescribed performance controller based on the distributed backstepping law is designed. The disturbance estimator (DE) is designed based on the adaptive prescribed approximation performance function to compensate for the unfavorable effect. Furthermore, considering the input saturation problem, the filter-based saturation compensation is presented to handle this problem. Eventually, the finite-time cooperative tracking performance with the proposed distributed FTPPF control is verified theoretically, and the proposed scheme can make tracking errors converge to an allowable range around zero rapidly under the disturbances and the input saturation. The experimental results and the comparison examples confirm the effectiveness of the proposed control scheme.

Motivated by finite-time optimization, this article concentrates on the finite-time stability of the complex interconnected systems under the topology attacks on communication edges. First, the models of the complex interconnected system and the topology attacks constrained by the limitation of the attack duration and frequency are described. Then, by mapping the value function into finite-time stability space, the distributed finite-time optimal consensus control (FTOCC) framework is designed. The tolerance limits of the proposed FTOCC for the topology attacks are analyzed under finite-time stability. Furthermore, a signal finite-time critic network and the control input approximation are presented to implement the secure optimal control strategy with finite-time convergence characteristics. And the practical finite-time stability of the closed-loop systems is guaranteed theoretically. Finally, the effectiveness of the proposed distributed finite-time adaptive dynamic programming-based optimal control scheme is illustrated by the comparison experiments.

This article proposes a dual-mode multiobjective Lyapunov-based economic model-predictive control (MOLEMPC) for constrained nonlinear time-delay systems with external disturbances. First, considering the conflicting objectives, the Pareto compromise approach is used to calculate the compromised economic stability point and the multiobjective optimization function. To compensate for the impact of input delay, a model-based estimator is designed by using the actual state, historical inputs, and nominal models of the system. By the proposed dual-mode Lyapunov-based economic model-predictive control (LEMPC) scheme, the feasible domain of the initial point of a closed-loop system is increased, in the meantime, the stability of the closed-loop system with disturbances and delay is guaranteed. The recursive feasibility and stability analysis of the MOLEMPC controller is given in detail. We illustrate that the closed-loop system state converges in a robust invariant set after a finite time. Finally, simulations demonstrate the reliability and advantages of the proposed strategy.

This article aims to investigate the problem of fixed-time fuzzy adaptive fault-tolerant tracking control for strict-feedback nonlinear systems (SFNSs) suffering from actuator fault and input delay. In practical engineering applications, actuator fault and input delay are two common problems, which are easy to affect system performance and cause adverse consequences. To rapidly deal with these two problems, a fixed-time fuzzy adaptive fault-tolerant control (FTC) algorithm is proposed. Specifically, fuzzy logic systems (FLSs) and Pade approximation method are applied to address the unknown nonlinearity and the input delay issues, respectively. Besides, command filter technique is applied to replace the traditional backstepping method to eliminate “complexity explosion” issue in calculation process. Under the role of fixed-time controller, the effects of input delay and actuator fault are rapidly suppressed, and the controlled system can obtain the better control performance on convergence speed and steady-state error. The comparative simulation examples demonstrate that the controller utilizing fixed-time stability has better control and fault tolerance performance, thus verifying the superiority of the proposed method.

In this article, we concentrate on a class of networked control systems (NCSs) under the unknown nonlinear attacks, which target the sensors and actuators with remote communication channels. To attenuate the attacks remotely, the remote secure control scheme based on the adaptive dynamic programming (ADP) is proposed. First, the dynamic process of NCSs with nonlinear sensors and actuators attacks is analyzed and an adaptive remote estimator is designed to obtain the state information of the NCSs from a long distance, which is the key to achieve the purpose of remote control. Moreover, to compensate for the unknown nonlinear attacks, the adaptive law of the remote estimator is presented based on recursive least square. Furthermore, we consider the influence of the estimation errors, the minimax control theory is applied to improve the design of the policy iteration algorithm, and the ADP-based remote secure control strategy is proposed to rely on the remote estimation information. Then, the neural networks are built to implement the online algorithm. Finally, results’ analysis illustrates on two networked power systems the effectiveness of the proposed method.

In this paper, a water injection cooling molding system is designed. Water injection cooling molding is an effective material molding method, which has a wide range of applications in the field of industrial production. The accurate control of water temperature is very important for the molding process, which can avoid a variety of defects, such as bubbles, deformation or gaps with the inner wall of the cavity. In order to further understand the influence of water temperature on the quality of molded products, the mathematical model is established according to the conservation of mass and energy, and analyze the influence of system parameters on the change of cooling temperature. The results show that the system input and parameter configuration have a great impact on the system performance and the final product quality. In order to obtain better molding quality, the system process parameters should be set reasonably. The model and research results in this paper play a certain reference role for the design of related systems, and lay a model foundation for the design of cooling water temperature control method.