5 results about "Self-stabilization" patented technology

The invention relates to the technical field of multi-degree-of-freedom motion control and self-stabilization platforms, in particular to an error compensation control method for a self-stabilization holder under multi-branch-chain redundancy collaboration, which comprises the following steps of: acquiring a pose and a disturbance signal based on branch chain physical topology, constructing a disturbance propagation path, and identifying a main error source branch chain and a secondary error branch chain; performing orthogonal decomposition on the error component of the main error source branch chain, and mapping the error of each secondary error branch chain to a unified error coordinate system; extracting time domain features of the direction and amplitude error, and generating a corresponding compensation signal; distributing a branch chain weight according to the disturbance intensity ratio, and constructing a redundant branch chain cooperative control instruction; synchronously driving each branch chain to execute compensation, and verifying whether the attitude error is converged or not; and if not, correcting the error identification parameter and the disturbance model, and feeding back to the control cycle. According to the invention, multi-branch chain high-dynamic coordination and error self-learning compensation can be realized, and the attitude stability and anti-disturbance capability of the holder are improved.

This invention discloses a consensus decision-making method, system, and storage medium based on distributed feedback, belonging to the field of internet collaborative decision-making technology. The method constructs a fully implicit autonomous decision-making system, including: collecting feedback data from network nodes on a target object; weighting the feedback data based on the implicit real-time reputation weights of the nodes to generate a consensus quantification result; silently updating the system state of the target object according to the consensus quantification result; and dynamically and silently adjusting the implicit reputation weights of the nodes in the background according to the state update result. This invention severs the direct causal chain between the perceptible behavior of nodes and system feedback, encapsulating consensus decision-making as a background environmental law unknown to the nodes. This fundamentally reduces the motivation for strategic gaming and malicious attacks by nodes, thereby improving the system's anti-attack capability, stability, and decision quality. It achieves game-resistant, self-stabilizing, and sustainably optimizing group decision-making, effectively solving governance problems such as information overload and the proliferation of low-quality content in large-scale digital communities.

The present invention discloses a method for fixed-point spraying operation of an unmanned aerial vehicle (UAV) based on the KLMS algorithm, comprising: the UAV carrying a laser transmitter is lifted into the air and performs laser marking to confirm the coordinates of the spray landing point; when the coordinates of the spray landing point are confirmed, the initial operating position of the UAV is calculated in combination with preset parameters including the horizontal operating distance and the spraying speed; the UAV reaches the initial operating position and starts the spraying operation after being permitted by ground monitoring personnel; during the operation, the KLMS-based landing point self-stabilization algorithm is executed to adjust the attitude and orientation of the UAV to ensure the stability of the spray landing point. The present invention focuses on the stability of the spray landing point to meet the needs of actual working scenarios. Taking into account the disturbances such as the recoil force generated by the UAV fixed-point spraying operation, in order to achieve a stable landing point, the KLMS-based landing point self-stabilization algorithm is used to adjust the attitude and orientation of the UAV, which greatly improves the positioning accuracy of the spraying operation and the efficiency of the spraying operation.

The invention discloses a time sequence anomaly detection method based on multi-window clustering symbolization, and relates to the technical field of anomaly detection, and the method comprises the following steps: building a cross-window feature drift capture baseline, segmenting an input time sequence according to sliding time windows, extracting the feature distribution of each time window, and building a continuous mapping model; and extracting the transition gradient of the clustering centroid according to the characteristic distribution change between the adjacent time windows, and generating a dynamic drift monitoring matrix for describing the dynamic change of the centroid. Through cross-window feature drift capture and centroid synchronous remapping, space-time continuous updating of symbol mapping is realized, faults and failures caused by centroid mutation are eliminated, and the accuracy and stability of anomaly detection are improved; and through abnormal mapping freezing control and symbol consistency closed loop, a self-healing regulation and control mechanism is constructed, so that the system has self-recovery and self-stabilization capabilities, and continuity, reliability and robustness of a detection process are ensured.

The invention provides an equipment self-stabilization control system and method, and the system comprises a data collection module and a control module, and the control module comprises a data preprocessing unit, a speed loop feedforward control unit, a speed loop instruction generation unit, a position loop feedback control unit, and a carrier-pointing motion coupling model calculation unit. The speed loop control comprises carrier gyroscope feedforward input, fuzzy self-tuning and first-order low-pass filtering processing; position loop control comprises fuzzy self-adaptive PID and gradient descent method optimization, collaborative optimization of motion compensation and accurate pointing of an equipment station body is achieved through independent control over a speed loop and the position loop, the station body pointing overshoot is reduced to 12% from 35%, the adjusting time is shortened by 60%, the steady-state error is reduced to 0.12 mrad from 0.75 mrad, and the speed loop and the position loop are independently controlled. The problems of insufficient dynamic response and reduced pointing precision of a traditional system in a complex motion environment are solved. According to the invention, a position-speed double-loop control architecture is adopted, and Kalman filtering and fuzzy parameter self-tuning technologies are combined, so that the stability and adaptability of the system are improved.