2results about How to "Reduce control energy consumption" patented technology

The application provides a kind of unmanned aerial vehicle vision adaptive control method based on multi-scale space-time network. By constructing multi-branch hollow convolution feature extraction and adaptive scale re-labeling network, the cooperative perception of near and far target features and background noise suppression are realized. On this basis, the dynamic multi-head self-attention mechanism with fusion time decay penalty factor is introduced, and the physical evolution law constraint is carried out on the continuous image sequence, effectively filtering out the instantaneous motion blur and target occlusion interference. In the state estimation link, the system constructs a deep coupling residual injection mechanism of vision and dynamics, and uses high-dimensional space-time perception features to correct the prediction deviation of the rigid body model in real time. Finally, combined with the error adaptive model predictive control and nonlinear robust disturbance observer, the optimal closed-loop control command is generated and directly drives the underlying electromechanical actuator. The application fundamentally breaks through the technical barriers of the separation system architecture, significantly improves the high-frequency state perception accuracy, large maneuver trajectory tracking performance and all-weather operation system robustness of the aircraft in the face of nonlinear gust disturbance and satellite navigation limited extreme working conditions.

The application discloses an unmanned aerial vehicle obstacle avoidance method based on digital twinning in an unstructured scene, which comprises the following steps: S1, constructing a geometric perception digital twinning system; S2, collecting unknown obstacle information and performing geometric synchronization; S3, performing dynamic modeling and discretization on a virtual unmanned aerial vehicle; S4, constructing a pseudo-control input and a linear prediction model; S5, generating a safe flight corridor; S6, performing local re-planning; S7, establishing a disturbed system and an error system; S8, constructing a robust positive invariant set and performing constraint tightening; S9, constructing a space adaptive tube MPC optimization problem; S10, performing online space adaptive adjustment; and S11, outputting a control instruction and maintaining virtual-real synchronization. By mapping unknown obstacles in a physical space to a virtual space in real time, a safe flight corridor is constructed, local re-planning and space adaptive tube MPC are combined, and the unmanned aerial vehicle can realize safe obstacle avoidance, high-precision trajectory tracking and low control energy consumption in an unstructured environment.