Quadrotor unmanned aerial vehicles (UAVs) have emerged as versatile platforms applicable to various fields such as surveillance, reconnaissance, and mapping. However, the limited energy capacity inherent in these vehicles poses a significant challenge. This limitation is a critical concern hindering their widespread adoption across diverse applications. Despite various proposed technological solutions addressing the energy consumption issue, the central challenge for the control community lies in developing controllers that ensure stability, robustness, and energy efficiency. In this paper, we present a passivity-based sliding mode controller (P-SMC) designed specifically for quadrotor UAVs. The suggested controller capitalizes on the robustness of the SMC and the energy efficiency of passivity-based control theory. Feedback passification is employed to create a sliding surface with passivity, ensuring stability. The inclusion of a noncontinuity term in the proposed P-SMC guarantees global asymptotic convergence to the sliding surface. To address the multi-faceted nature of the problem, a multi-objective optimization criterion is introduced. This criterion, which combines tracking error and control energy, is solved using the ant colony optimization (ACO) algorithm. The optimization process aims to identify control parameters that strike a balance between tracking accuracy and energy efficiency. Additionally, a conventional sliding mode method is developed to respond to conditions of attempted reach and sliding. Finally, the effectiveness of the proposed method is demonstrated through simulation results considering piecewise constant external disturbances. The outcomes based on the proposed control strategy indicate a notable reduction in energy consumption (ranging from 23% to 27%), faster reaching times (5–8 times), higher accuracy approximately (99%), and reduced chattering compared to the conventional sliding mode technique.

In this paper, the problem of searching and tracking uncooperative and unidentified mobile ground target using a quadcopter unmanned aerial vehicle (QUAV) is investigated. The proposed strategy is an Image-Based Visual Servoing (IBVS) approach, combined with the virtual camera concept and robust control. This strategy makes use of the rough prior information of the target, in contrast with existing strategies, which will alleviate the problem of altitude estimation noise and enhance the overall accuracy. Hence, a new vision-based sliding mode controller (SMC) is designed to control the quadcopter taking into account the flight phase’s heterogeneity, the external disturbances and parameters uncertainties as well as the target maneuverability. In order to get a better insight about the SMC tuning and adjustment, three different reaching laws are evaluated and compared. The proposed controller allows an automatic execution of the flight strategy whilst the searching phase relies on the Camera Coverage Area (CCA) technique. The vision-based technique allows an automatic QUAV altitude tuning for optimal target observation and tracking. Another contribution of this work is the fact that the designed controller validity and stability overspan the entire scenario to reach the universal and to smoothen out surges generated by control switching. Numerical simulations are conducted to compare the proposed SMC controllers and validate the effectiveness of the whole strategy.