A sliding mode controller is proposed for the attitude control of spacecraft reorientation with attitude constraints. By introducing a potential function, a novel sliding function is designed. With the structure of the proposed sliding function, uniqueness of the equilibrium and fixed-time convergence are rigorously proven. Another advantage of the proposed controller is that the feasibility of attitude constraints can be proven not only on the sliding surface but also outside it. Moreover, the singularity issue of the proposed controller is resolved. Effectiveness of the proposed method is demonstrated by carrying out two numerical simulation.

Unmanned Aerial Vehicle (UAV)-aided communication is a promising technology for future wireless systems by providing communication access from the sky. However, existing mobility models for UAV-aided communication with fixed-wing UAVs do not characterize the full UAV dynamics that needs to consider the forces manipulating the UAV as the model inputs. As a result, the planned trajectories are usually a sequence of non-smooth piece-wise line segments which are difficult to implement in practice for fixed-wing UAVs. To tackle this problem, a control-based design framework is proposed in this paper for fixed-wing UAVs by modeling their realistic dynamics in the three-dimensional (3D) space. By leveraging the state-space model, the trajectory optimization problem for fixed-wing UAV communication is formulated as a state-constrained optimal control problem, subject to the practical constraints on the UAV's flying speed, altitude and flight path angle. A solution method based on control parametrization is proposed, which converts the original infinite-dimensional optimization problem into a new finite-dimensional nonlinear program. With the proposed method, a smooth 3D UAV trajectory is obtained, which is easy to realize in practice by directly implementing the designed control variables such as thrust force and lift force. Extensive numerical examples are provided to illustrate the effectiveness of the proposed solution as compared to existing solutions.

This paper utilizes the distributed model predictive control (DMPC) method to investigate the formation control problem of unmanned aerial vehicles (UAVs) in the obstacle environment and establishes cooperative capability evaluation metrics of the swarm. Based on the DMPC approach, the formation cost function is constructed to adjust the relative positions and velocities of UAVs, ensuring the desired formation. Additionally, to address the obstacle avoidance problem in the formation, the obstacle avoidance function is designed to provide safe formation control in the obstacle environment. To evaluate the cooperative capability of UAVs, we design evaluation metrics from multiple dimensions to reflect the swarm’s cooperative capability. Finally, the simulation results show the effectiveness of the formation control method with obstacle avoidance and the applicability of the swarm’s cooperative capability evaluation metrics.

This article presents an adaptive neural network-based sliding mode control (SMC) strategy aimed at achieving predefined-time and predefined-accuracy (PTPA) convergence of tracking errors. Notably, the proposed approach does not require prior knowledge of the upper bounds of uncertainties or the control direction. To ensure PTPA convergence and prevent singularity, a novel piecewise PTPA sliding mode manifold incorporating a nonlinear compensating term is introduced. This compensating term is specifically designed to address the bounded sliding-mode errors induced by model uncertainties and external disturbances. Furthermore, by enforcing a PTPA constraint on the sliding mode function and utilizing the Nussbaum function, the system states can converge to a predefined neighborhood of the sliding mode surface within a predefined time. An adaptive neural network-based PTPA SMC law is then developed, eliminating discontinuous terms and effectively mitigating the chattering issue. The proposed control scheme is rigorously proven to achieve PTPA convergence and asymptotic convergence. The efficacy of the designed control strategy is validated through two numerical examples, demonstrating its superior performance.

This article studies the global asymptotic neural network (NN) tracking problem for full-state error constrained spacecraft attitude systems with actuator faults, inertia uncertainties, and external disturbances. In the literature, most existing NN control schemes can only achieve semiglobally bounded stability since the approximation capability of NNs is confined to a compact domain called the approximation domain. Differently, an attitude tracking control strategy in conjunction with a modified smooth switching mechanism is proposed to ensure the global asymptotic stability. Specifically, an adaptive NN controller is developed within the approximation domain to address unknown nonlinearities, and a robust controller is activated outside the approximation domain to drive back the system states. With the proposed design, both attitude and angular velocity errors (collectively defined as the full-state errors) are rigorously proven to globally asymptotically converge to zero. Moreover, the full-state errors are preserved within the unified prescribed performance constraints, which are uniform with respect to any initial conditions, thereby eliminating the requirement for offline computation of the performance boundary. In addition, the undesirable feasibility conditions on virtual control laws are completely eliminated. Theoretical analysis and numerical simulations validate the effectiveness of the proposed method.