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.

Automated summary

This article investigates the problem of adaptive performance guaranteed tracking control for multiagent systems (MASs) with power integrators and measurement sensitivity. A new control approach is proposed to guarantee the convergence of the relative position error between neighboring agents within a preassigned finite time. By utilizing the Nussbaum gain technique and neural networks, a novel control scheme is developed to solve the unknown measurement sensitivity on the sensor, relaxing the restrictive condition. Based on the Lyapunov functional method, it is proven that the relative position error can converge into the prescribed boundary.