Bin Li

This paper proposes a Koopman-based stochastic model predictive control (SMPC) approach for unknown nonlinear systems by exploiting partial probabilistic information. Unlike existing Koopman-based SMPC methods that primarily rely on the first- and second-order moments of stochastic Koopman modeling error, the proposed approach further incorporates available support information into the controller design. By jointly utilizing the mean, covariance, and support information of the resulting uncertainty, the chance-constrained optimization problem is reformulated into a tractable deterministic optimization problem with reduced conservatism. Moreover, the proposed approach is theoretically shown to be no more conservative than the corresponding RMPC in terms of constraint tightening under identical constraint requirements. Furthermore, recursive feasibility and closed-loop quadratic stability of the proposed control scheme are established theoretically. Simulation studies on spacecraft attitude control demonstrate that the proposed method reduces the performance index by 6.7% and 17.0% compared with a Koopman-based SMPC using mean and covariance information and RMPC, respectively, while maintaining a comparable average computation time of approximately 0.010 s.

Trajectory planning is a key enabling technology for UAV swarms operating in complex and obstacle-rich environments. This paper proposes a distributed nonlinear model predictive control (NMPC)-based trajectory planning method for UAV swarms, where terminal target reaching, prescribed formation maintenance, obstacle avoidance, and inter-UAV collision avoidance are incorporated into a unified predictive optimization framework. To reduce the online computational burden, a control parameterization strategy is introduced to describe the control input using M control segments, thereby reducing the dimension of the online decision variables. Furthermore, an exact-penalty-based constraint transcription method is developed to transform the original constrained optimal control problem into a lower-complexity finite-dimensional nonlinear programming problem, while efficiently handling velocity constraints, obstacle avoidance constraints, and inter-UAV collision avoidance constraints. Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within a certain formation error. Furthermore, real UAV swarm flight experiments were conducted to further validate the practical feasibility and online applicability of the proposed distributed NMPC framework for UAV swarm trajectory planning.

In this article, a prescribed-time output feedback controller is proposed for a class of uncertain nonlinear systems with unknown control coefficients and mismatched nonvanishing disturbances. Both unknown control coefficients and mismatched disturbances are tricky to address by the existing prescribed-time output feedback control frameworks. Differently, a novel prescribed-time control criterion in conjunction with Nussbaum functions is proposed, and prescribed-time stability is achieved. Furthermore, design methods for a state observer and a prescribed-time output feedback controller are developed. With the proposed control design, both the system output and observer errors are rigorously proved to converge to zero within a prescribed time. Moreover, the unified prescribed performance (UPP) of the system output and the satisfaction of output constraints are simultaneously achieved. Numerical simulations and experiments are provided to illustrate the effectiveness of the proposed control design.

To address the inadequate coverage of fixed-orbit constellations for emergent tasks, this paper proposes a Multi-Stage Integrated Optimization Framework (M-IOF) that couples discrete task assignment with continuous orbital maneuver planning. In this framework, candidate task chains generated by the scheduler are evaluated through inverse orbit design, and the corresponding feasibility information is fed back to the fitness function. Spatial-functional task clustering is first used to reduce the dimensionality of large-scale heterogeneous tasks. Then, a target-driven inverse orbit solving method maps spatio-temporal constraints into feasible resonant phasing conditions. By considering [ ] perturbations, a one reconfiguration maneuver plan strategy is formulated, enabling satellites to sequentially observe multiple targets through passive drift after one initial maneuver. The scheduling model is solved using an adaptive genetic algorithm with feasibility penalties. Comparative simulations under three task-structure scenarios show that M-IOF improves target completion and task efficiency while reducing unnecessary maneuvering. The results demonstrate the effectiveness of the proposed framework for responsive constellation reconfiguration.

Unmanned aerial vehicle (UAV) is regarded as a key enabling platform for low-altitude economy, due to its advantages such as three-dimensional (3D) maneuverability, flexible deployment, and line-of-sight (LoS) air-to-air/ground communication links. In particular, the intrinsic high mobility renders UAV especially suitable for operating as a movable antenna (MA) from the sky. In this paper, by exploiting the flexible mobility of UAV swarm and antenna position adjustment of MA, we propose a novel UAV swarm enabled two-level MA system, where UAVs not only individually deploy a local MA array, but also form a larger-scale MA system with their individual MA arrays via swarm coordination. We formulate a general optimization problem to maximize the minimum achievable rate over all ground user equipments (UEs), by jointly optimizing the 3D UAV swarm placement positions, their individual MAs’ positions (or local positions), and receive beamforming for different UEs. To gain useful insights, we first consider the special case where each UAV has only one antenna, under different scenarios of one single UE, two UEs, and arbitrary number of UEs. In particular, for the two-UE case, we derive the optimal UAV swarm placement positions in closed-form that achieves inter-UE interference (IUI)-free communication when the uniform plane wave (UPW) model holds, where the UAV swarm forms a uniform sparse array (USA) satisfying minimum safe distance constraint. While for the general case with arbitrary number of UEs, we propose an efficient alternating optimization algorithm to solve the formulated non-convex optimization problem. Then, we extend the results to the case where each UAV is equipped with multiple antennas. Numerical results verify that the proposed low-altitude UAV swarm enabled MA system significantly outperforms various benchmark schemes, thanks to the exploitation of two-level mobility to create more favorable channel conditions for multi-UE communications.

In this article, we propose a self-triggered distributionally robust model predictive control algorithm for linear discrete systems with state chance constraints and unbounded stochastic disturbances. Assuming that only the first and second moments of the disturbance are accessible, we transform the objective function into a compact quadratic form and reformulate the state chance constraints into linear inequalities, which is more tractable when solving. In order to reduce communication and sampling times of the system, we propose a self-triggered update scheme, in which the state sampling and the control input sequence are updated when the control performance predicted based on the current sampling exceeds that of the periodic sampling scheme. We demonstrate that the optimization problem in the proposed self-triggered model predictive control (MPC) method is recursively feasible and stable. Numerical simulation results verify the effectiveness of the proposed algorithm.

This brief presents a systematic overview on the development for distributionally robust model predictive control (DRMPC). The existing works on robust model predictive control (RMPC), stochastic model predictive control (SMPC), and DRMPC are introduced and their distinctive features have been summarized. Finally, promising future research directions are discussed.

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.