Estimator-based MLP neuroadaptive dynamic surface containment control with prescribed performance for multiple quadrotors

Abstract

This paper presents an estimator-based minimal learning parameter (EMLP) neuroadaptive dynamic surface containment control design capable of guaranteeing transient and steady-state behavior for multiple quadrotors with uncertainties, such that the followers can be driven into the convex hull constituted by multiple dynamic leaders with prescribed bounded errors. To facilitate the control design, the quadrotor dynamics is decomposed into translational and rotational subsystems. For each subsystem, in order to enable smooth and rapid learning of unknown system uncertainties and reduce the computational load in traditional neural network (NN), the estimation errors, instead of tracking errors, are used to regulate NN weights and only one NN learning parameter is required for adaptive neural approximation with the aid of MLP, which is more feasible for real-time implementation. Additionally, dynamic surface control (DSC) technique is introduced in control design to extract the time derivative of virtual control laws, and thus the issue of “explosion of complexity” can be circumvented. As an extension, prescribed performance function and error transformation technique are utilized to address the preselected containment synchronization error constraints problem. Finally, the stability analysis is established to prove that all error signals are uniformly ultimately bounded (UUB). Simulation results illustrate the effectiveness and superiority of the proposed control scheme.