This paper deals with the robust optimal three-axis attitude tracking problem of spacecrafts. To this aim, the exceptional features of two advanced control approaches, i.e., nonlinear terminal sliding mode control (TSMC) and linear model predictive control (MPC), are combined in a compound double layer structure. The proposed approach addresses the global stability and robust attitude tracking of near-polar orbit spacecrafts actuated by a set of reaction wheels and subject to unknown disturbances, uncertainties, and actuators' saturation. The TSMC controller is designed to guarantee the robust three-axis attitude tracking goal in the presence of disturbances and uncertainties provided that proper target points are available. The next part of the compound controller takes the advantages of the MPC technique (i.e., preknown attitude maneuvers and constraints) into account to provide the required optimal target points for the lower level TSMC controller. Despite the fact that the spacecraft has nonlinear nature, we have analytically derived conditions under which the actuators' constraints satisfaction is guaranteed a priori. Finally, a set of simulation results are provided to illustrate the effectiveness and performance of the proposed method.

Purpose: Human or machine, which one is more intelligent and powerful for performing computing and processing tasks? Over the years, researchers and scientists have spent significant amounts of money and effort to answer this question. Nonetheless, despite some outstanding achievements, replacing humans in the intellectual tasks is not yet a reality. Instead, to compensate for the weakness of machines in some (mostly cognitive) tasks, the idea of putting human in the loop has been introduced and widely accepted. In this paper, the notion of collective hybrid intelligence as a new computing framework and comprehensive. Design/methodology/approach: According to the extensive acceptance and efficiency of crowdsourcing, hybrid intelligence and distributed computing concepts, the authors have come up with the (complementary) idea of collective hybrid intelligence. In this regard, besides providing a brief review of the efforts made in the related contexts, conceptual foundations and building blocks of the proposed framework are delineated. Moreover, some discussion on architectural and realization issues are presented. Findings: The paper describes the conceptual architecture, workflow and schematic representation of a new hybrid computing concept. Moreover, by introducing three sample scenarios, its benefits, requirements, practical roadmap and architectural notes are explained. Originality/value: The major contribution of this work is introducing the conceptual foundations to combine and integrate collective intelligence of humans and machines to achieve higher efficiency and (computing) performance. To the best of the authors’ knowledge, this the first study in which such a blessing integration is considered. Therefore, it is believed that the proposed computing concept could inspire researchers toward realizing such unprecedented possibilities in practical and theoretical contexts.

This paper proposes a novel adaptive intelligent global sliding mode control for the tracking control of a DC-DC buck converter with time-varying uncertainties/disturbances. The proposed control law is formulated using a switching surface that eliminates the reaching phase and ensures the existence of the sliding action from the start. The control law is derived based on the Lyapunov stability theory. The effectiveness of the proposed approach is illustrated via high-fidelity simulations by means of Simscape simulation environment in MATLAB. Satisfactory tracking accuracy, efficient suppression of the chattering phenomenon in the control input, and high robustness against uncertainties/disturbances are among the attributes of the proposed control approach.