Cooperative Adaptive Dynamic Surface Control for a Class of High-Order Stochastic Nonlinear Multiagent Systems

Mixed-Zero-Sum-Game-Based Memory Event-Triggered Cooperative Control of Heterogeneous MASs Against DoS Attacks

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

Neuroadaptive Performance Guaranteed Control for Multiagent Systems With Power Integrators and Unknown Measurement Sensitivity

This article investigates the adaptive performance guaranteed tracking control problem for multiagent systems (MASs) with power integrators and measurement sensitivity. Different from the structural characteristics of existing results, the dynamic of each agent is a power exponential function. A method called adding a power integrator technique is introduced to guarantee that the consensus is achieved of the MASs with power integrators. Different from existing prescribed performance tracking control results for MASs, a new performance guaranteed control approach is proposed in this article, which can guarantee that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. By utilizing the Nussbaum gain technique and neural networks, a novel control scheme is proposed to solve the unknown measurement sensitivity on the sensor, which successfully relaxes the restrictive condition that the unknown measurement sensitivity must be within a specific range. Based on the Lyapunov functional method, it is proven that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. Finally, a simulation example is proposed to verify the availability of the control strategy.

Adaptive Neural Consensus for Fractional-Order Multi-Agent Systems With Faults and Delays

This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

Neural-Network-Based Finite-Time Bipartite Containment Control for Fractional-Order Multi-Agent Systems

This article focuses on the adaptive bipartite containment control problem for the nonaffine fractional-order multi-agent systems (FOMASs) with disturbances and completely unknown high-order dynamics. Different from the existing finite-time theory of fractional-order system, a lemma is developed that can be applied to actualize the aim of finite-time bipartite containment for the considered FOMASs, in which the settling time and convergence accuracy can be estimated. Via applying the mean-value theorem, the difficulty of the controller design generated by the nonaffine nonlinear term is overcome. A neural network (NN) is employed to approximate the ideal input signal instead of the unknown nonaffine function, then a distributed adaptive NN bipartite containment control for the FOMASs is developed under the backstepping structure. It can be proved that the bipartite containment error under the proposed control scheme can achieve finite-time convergence even though the follower agents are subjected to completely unknown dynamic and disturbances. Finally, the feasibility and validity of the obtained results are exhibited by the simulation examples.

Prescribed Performance Bipartite Consensus Control for Stochastic Nonlinear Multiagent Systems Under Event-Triggered Strategy

In this article, the event-triggered bipartite consensus problem for stochastic nonlinear multiagent systems (MASs) with unknown dead-zone input under the prescribed performance is studied. To surmount the influence of the dead-zone input, the dead-zone model is transformed into a linear term and a disturbance term. Meanwhile, the prescribed tracking performance is realized by developing a speed function, which means that all tracking errors of MASs can converge to a predefined set in a given finite time. Moreover, the unknown nonlinear dynamics are approximated by fuzzy-logic systems. By combining the dynamic surface approach and the Lyapunov stability theory, we design an adaptive event-triggered control algorithm, such that the bipartite consensus problem of stochastic nonlinear MASs can be achieved, and all signals are semiglobally uniformly ultimately bounded in probability of the closed-loop systems. Finally, simulation examples are proposed to verify the feasibility of the algorithm.