7results about How to "Shorten convergence time" patented technology

This invention provides a fast Doppler localization method based on opportunistic signals. The method includes: establishing instantaneous Doppler frequency shift measurements for each satellite based on the receiver's initial position and velocity information, the wavelength of the opportunistic signal, and the position and velocity information of each satellite; establishing an instantaneous Doppler localization equation based on the relationship between the instantaneous Doppler frequency shift measurements for each satellite and the receiver's position information; determining regularization parameters using the variance expansion factor method; obtaining constraint parameters based on the regularization parameters and the stable functional; and solving the instantaneous Doppler localization equation under the condition of minimizing the constraint parameters to obtain the instantaneous Doppler localization result based on the opportunistic signal. The localization result includes the receiver's final position information, final velocity information, and frequency offset.

The application discloses a projection figure distortion prediction compensation correction method of a laser scanning projection system, and comprises the following steps: carrying out laser scanning projection system projection; acquiring projection figure distortion data, establishing a neural network training data set and a test data set; establishing an initial topological structure, weights and thresholds of a BP neural network; optimizing the weights and thresholds of the BP neural network based on a particle swarm optimization algorithm; carrying out deviation compensation on the coordinate positions of each projection point according to the training output of the PSO-BP neural network; selecting feature points in each part of a to-be-projected plane; projecting the feature points by using a distortion deviation correction projector based on the PSO-BP neural network; acquiring feature point projection coordinate values; subtracting the ideal projection point coordinates without distortion from the corrected feature point projection coordinates; judging whether the difference value meets an error interval; if yes, it is determined that the projection coordinate value distortion error has been corrected and compensated; if no, returning to step three for feedback type multiple iterations until the difference value meets the error interval.

The invention discloses a photovoltaic MPPT (Maximum Power Point Tracking) control algorithm, which relates to the technical field of photovoltaic control algorithms, utilizes the characteristics of simple structure, high convergence speed, high global optimization capability, wide applicability and the like of a gradient optimization algorithm, and improves the defects of premature convergence and sensitivity to control parameters of the gradient optimization algorithm. Meanwhile, the photovoltaic output energy efficiency and the stability of the photovoltaic output energy efficiency serve as the evaluation basis, an MPPT algorithm performance evaluation model is constructed through an analytic hierarchy process, photovoltaic MPPT performance is evaluated, and GTO performance is verified and improved.

A mechanical arm path planning method and system based on adaptive expansion sampling belong to the technical field of path planning, and solve the problems of slow sampling rate, slow convergence speed and poor initial path quality caused by too random sampling area and unsuitable expansion step length selection when planning the path of the mechanical arm under the prior art; the adaptive expansion sampling strategy provided by the present application introduces a target bias force to correct the growth direction of the random tree based on the F-RRT* optimization algorithm, so that it is biased towards the target point, optimizes the inflection point, makes the generated path smoother, introduces the concept of target point gravity deviation, adaptively expands the strategy according to the complexity of obstacles in the environment, and expands with a dynamic step length, improves the stability and precision of the trajectory, and reduces the length of the generated path, the sampling times and the convergence time.

The application belongs to the technical field of nonlinear system control, and discloses a method and system for introducing composite learning into nonlinear system output feedback adaptive control. The application directly reuses the existing K-filter in standard output feedback backstepping control to construct an extended prediction error, and does not need to establish an additional observer or a state estimation model (such as a series-parallel estimation model, a fuzzy observer, etc.) in parallel with the K-filter. This functional reuse design eliminates the calculation overhead of the additional dynamic system, reduces the storage requirement and real-time calculation burden of the controller, and significantly reduces the hardware implementation cost and system debugging complexity. Meanwhile, through the composite driving of the tracking error and the extended prediction error, the cumulative information is continuously introduced by using the historical memory of the regression quantity, so that the adaptive and self-learning ability of the control system is significantly enhanced, and the parameter estimation convergence and the system response performance are improved.

This invention discloses a seamless switching positioning method integrating RTK and PPP-RTK algorithms. This method achieves seamless switching between RTK and PPP-RTK algorithms by designing a dual-state model within a Kalman filtering framework. When network connectivity is available, the system automatically uses NRTK corrections to achieve high-precision RTK positioning; when the network is interrupted, the system seamlessly switches to satellite-based PPP-RTK corrections for PPP-RTK positioning without requiring a software restart. This invention, through the design of a state transition matrix and an adaptive noise adjustment mechanism, ensures positioning continuity and accuracy stability during the switching process, solving the problem of traditional methods requiring re-initialization during switching, which leads to positioning interruptions. This significantly improves the robustness and availability of GNSS positioning systems in complex environments.