Neural network–based reconfiguration control for spacecraft formation in obstacle environments

Abstract

Summary This paper proposes an adaptive formation reconfiguration control scheme based on the leader‐follower strategy for multiple spacecraft systems. By taking the predesigned desired velocities and the trajectories as reference signals, a set of coordination tracking controllers is constructed by combining the reconstructed dynamic system and the neural network–based reconfiguration algorithm together. To avoid collisions between spacecraft and obstacles during the formation configuration process, the null space–based behavioral control is integrated into the control design. Since the spacecraft dynamics contains unknown nonlinearity and disturbance, it is challenging to make the system robust to uncertainties and improve the control precision simultaneously. To solve this problem, both the adaptive neural network strategy and the finite‐time control theory are employed. Finally, 2 simulation examples are carried out to verify the proposed algorithm, showing that the formation reconfiguration task can be executed successfully while achieving high control precision.

Hyperbolic Tangent Function-Based Finite-Time Sliding Mode Control for Spacecraft Rendezvous Maneuver Without Chattering

“…Besides, controllers with high precision and satisfactory disturbance rejection performance become more and more desirable for different space missions, which is an urgent issue for aerospace applications. Fortunately, fruitful research achievement for spacecraft control has emerged recently, such as adaptive control [1]- [4], [32], backstepping control [5]- [8], neural network-based control [9]- [12], sliding mode control (SMC) [13]- [16], model predictive control [31] as well as control based on hybrid actuators [35].…”

Distributed output‐feedback finite‐time tracking control of nonaffine nonlinear leader‐follower multiagent systems

“…The consensus problem of multiagent systems (MASs) has drawn great attention due to its broad applications in many areas such as formation control of robotic teams, information fusion of sensor networks, attitude alignment of multiple unmanned aerial vehicles, and so on . In the past decade, many works have been represented to address the consensus control problem for linear or affine nonlinear MASs (see, for example, References ).…”

The Attitude Control Algorithm of Agile Optical Satellite Oriented to Nonparallel-Ground-Track-Imaging

“…Due to imaging for the ground target dynamically and continuously, the satellite attitude should be well controlled. To this end, tremendous control methods were applied in the last decades, involving the backstepping control [5], [6], the neural-network control [7]- [11], the adaptive control [12], [13], the event-triggered control [14], [15] and the sliding mode control [16]- [19].…”

“…Considering the wide application of spacecraft formation flying (SFF), backstepping control methods incorporating the second-order sliding mode differentiator and command filter was exploited in [6]. Analogously for the leader-follower satellite formation task, the reconfiguration algorithm in [11] is developed on the basis of neural network, where the finite-time stability could be ensured. Different from the leader follower formation structure, the distributed formation strategies also play a significant role due to the advantages of its flexibility and fault-tolerant ability.…”