Practical Prescribed-Time Resource Allocation of NELAs With Event-Triggered Communication and Input Saturation

This paper investigates the practical resource allocation of networked Euler-Lagrange agents (NELAs) with event-triggered communication and input saturation. A novel prescribed-time resource allocation control (PTRAC) algorithm is proposed, which includes a resource allocation estimator and a prescribed-time NN-based local controller. The former is designed based on the time-based generator (TBG) and event-triggered mechanism to achieve the optimal resource allocation within the prescribed-time. Additionally, the prescribed-time NN-based local controller is designed using the approximation ability of RBF neural network to force the states of NELAs to track the optimal values within the prescribed-time. The most significant feature of the PTRAC algorithm is that the complex problem can be analyzed independently in chunks and converges within the prescribed-time, greatly reducing the number of triggers and communication costs. Subsequently, the validity is verified by simulation and several sufficient conditions are established via the Lyapunov stability argument.