Event-Based Adaptive NN Fixed-Time Cooperative Formation for Multiagent Systems

Observer-Based Consensus Control for MASs With Prescribed Constraints via Reinforcement Learning Algorithm

In this article, an adaptive optimal consensus control problem is studied for multiagent systems (MASs) with external disturbances, unmeasurable states, and prescribed constraints. First, by using neural networks (NNs), a composite observer is constructed to estimate the unmeasurable states and disturbances simultaneously. Then, the consensus error is guaranteed within a prescribed boundary by presenting an improved prescribed performance control (PPC) technique, and the initial conditions for the error are eliminated. In addition, the updating laws of actor-critic NNs are established by using a simplified reinforcement learning (RL) algorithm based on the uniqueness of optimal solution, and the asymmetric input saturation is resolved by designing auxiliary system instead of using nonquadratic cost functions in other optimal control methods. Finally, the boundedness of all signals in the closed-loop system is proved by using Lyapunov stability theory. The effectiveness of the proposed control method is verified by a simulation example.

Cluster formation tracking of networked perturbed robotic systems via hierarchical fixed-time neural adaptive approach

This paper investigates the fixed-time cluster formation tracking (CFT) problem for networked perturbed robotic systems (NPRSs) under directed graph information interaction, considering parametric uncertainties, external perturbations, and actuator input deadzone. To address this complex problem, a novel hierarchical fixed-time neural adaptive control algorithm is proposed based on a hierarchical fixed-time framework and a neural adaptive control strategy. The objective of this study is to achieve accurate CFT of NPRSs within a fixed time. Specifically, we design a distributed observer algorithm to estimate the states of the virtual leader within a fixed time accurately. By using these observers, a neural adaptive fixed-time controller is developed in the local control layer to ensure rapid and reliable system performance. Through the use of the Lyapunov argument, we derive sufficient conditions on the control parameters to guarantee the fixed-time stability of NPRSs. The theoretical results are eventually validated through numerical simulations, demonstrating the effectiveness and robustness of the proposed approach.

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.

Distributed State Observer for Systems with Multiple Sensors under Time-Delay Information Exchange

The issues of state estimations based on distributed observers for linear time-invariant (LTI) systems with multiple sensors are discussed in this paper. We deal with the scenario when the information exchange has known time delays, and aim at designing a distributed observer for each subsystem such that each distributed observer can estimate the system state asymptotically by rejecting the time delay. To begin with, by rewriting the target system in a connecting form, a subsystem which is affected by the time-delay states of other nodes is established. And then, for this subsystem, a distributed observer with time delay is constructed. Moreover, an equivalent state transformation is made for the observer error dynamic system based on the observable canonic decomposition theorem. Further, in order to ensure that the distributed observer error dynamic system is asymptotically stable even if there exists a time delay, a linear matrix inequality (LMI) which is relative to the Laplace matrix is elaborately set up, and a special Lyapunov function candidate based on the LMI is considered. Next, based on the Lyapunov function and Lyapunov stability theory, we prove that the error dynamic system of the distributed observer is asymptotically stable, and the observer gain is determined by a feasible solution of the LMI. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

Switching ETM-based neural adaptive output feedback control for nonaffine stochastic MIMO nonlinear systems subject to deferred constraint

This article focuses on the neural adaptive output feedback control study related to nonaffine stochastic multiple-input, multiple-output nonlinear plants. First, a K-filter state observer based on a radial basis function neural network is designed to estimate the remaining unavailable states. Then, a novel adaptive command-filtered backstepping output feedback control framework is established, where an improved command filter with a fractional-order parameter is applied to conquer the calculation size problem. Specifically, the highlight of this work is that it designs a modified error compensation signal and incorporates the concept of deferred constraint to eradicate the negative effect caused by the filter errors. In addition, the network bandwidth resources, control impulse, and control accuracy are synthesized using an amended switching event-triggered mechanism. The theoretical analysis proved that the proposed control approach guarantees that the tracking error can converge to a preassigned region within a user-defined time while the violation of the deferred output constraint can be excluded. Two illustrative studies are provided to demonstrate the validity and superiority of the developed control method.