9 results about "Longitudinal model" patented technology

This invention discloses a morphing control method for a conventional aircraft based on a disturbance observer. A longitudinal model of the aircraft is established, and variable parameters related to airfoil camber are introduced into the aerodynamic parameters. The relationship between the aircraft's aerodynamic parameters and airfoil camber is fitted, followed by small-disturbance linearization. Using the fitted functional relationship between the aircraft's longitudinal aerodynamic parameters and airfoil camber, a variable-parameter model of the aircraft is established, i.e., a specific state-space model. Based on the constructed state-space model, a disturbance observer is constructed to estimate disturbance dynamics. Combining the disturbance estimate and a PI-type state feedback controller, the controlled model is effectively controlled, improving stability. By combining the Lyapunov stability method and convex optimization methods, the controller gain and observer gain are calculated, thereby ensuring good dynamic performance of the controlled morphing aircraft system.

The invention discloses a vehicle speed decision-making method and system, a vehicle and equipment. The speed decision-making method of the vehicle comprises the following steps: determining a target obstacle according to a planned path of the vehicle; establishing a longitudinal model of the vehicle; performing forward simulation of the vehicle according to the state information of the vehicle and the state information of the target obstacle to obtain an input acceleration at a first moment; the input acceleration at the first moment is input into the longitudinal model, so that state variables of the vehicle at a second moment are obtained according to the input acceleration at the first moment and the position, speed, acceleration and jerk of the vehicle at the first moment, and the second moment is the next moment of the first moment; and determining a speed planning curve of the vehicle according to the obtained state variable of the vehicle. By adopting the method and the device, the state of the vehicle is updated by applying the vehicle longitudinal model, so that the lane forward simulation is closer to the real state of the kinematics of the vehicle, and the speed decision performance is improved.

This invention discloses a longitudinal anti-interference control method for a small fixed-wing unmanned aerial vehicle (UAV) based on the Actor-Critic framework. The steps are as follows: First, a longitudinal model of the UAV under disturbance is established; second, a performance function is designed to evaluate the control performance of the UAV, the Critic approximates the nonlinear performance function, and the Actor learns the optimal control law that minimizes the performance function; third, a disturbance neural network is added to estimate the disturbance of the UAV in the worst-case scenario, and a momentum neural network is used to improve the learning speed. This invention uses reinforcement learning technology and a neural network to approximate the performance function with the optimal control law, achieving adaptive control of the UAV under disturbance. This invention not only enhances the anti-interference capability of the UAV but also accelerates the convergence speed of the reinforcement learning control law.

The invention provides systems and methods for computing interpersonal compatibility using multi-modal data fusion that integrates genomic, immunologic, psychometric, biometric, contextual, and reproductive information. Genomic analysis includes variant calling, polygenic scoring, carrier-status evaluation, and HLA / KIR immune-compatibility modeling. Biometric inputs from wearable devices are processed to determine emotional synchrony and autonomic co-regulation. Psychometric and behavioral data are converted into latent-trait embeddings. A machine-learning fusion engine combines all modality-specific feature vectors to generate unified compatibility embeddings and dual outputs representing soulmate-stage suitability and family-planning compatibility. Additional embodiments include donor and surrogate matching, offspring-risk simulation, embryo-viability prediction, and longitudinal model refinement using real-world relational, biometric, or reproductive outcomes.

The application discloses a kind of intelligent decision support system and method based on longitudinal model conduction chain, belong to data processing system technical field, wherein, the system includes: model conduction engine, for building ordered model sequence, the output vector of each model in sequence as the input of subsequent model, form continuous decision conduction chain;Dynamic weight module, for realizing the weighted transmission of predecessor model output to subsequent model input by conduction matrix, the conduction matrix is based on model confidence error and dynamically adjusted;Closed-loop control unit, for monitoring the actual achievement situation of decision target, realizes the global optimization of longitudinal conduction chain by optimizing full-stack target function.By the application, it avoids the repeated processing of original data, realizes the ordered conduction of knowledge between models, improves the consistency, efficiency and robustness of decision.

The application discloses a kind of inclined shaft anisotropy has the fast calculation method of invasion stratum induction logging response, it is related to the technical field of electrical logging in oil exploration and development, comprising: constructing three-dimensional heterogeneous stratum model containing inclined shaft anisotropy invasion;Extracting invasion zone longitudinal model and original stratum longitudinal model;Considering induction logging instrument parameters, using semi-analytical algorithm and 0-dimensional inversion, obtain equivalent stratum model;Point by point construction radial heterogeneous model;In turn calculate the response of induction logging instrument different source distance and frequency operating mode, each mode response combination obtains induction logging synthetic apparent resistivity curve;Through all measurement depth points point by point cycle calculation, finally obtain the logging response of whole well section.The inclined shaft anisotropy has the hierarchical superposition of stratum decomposition for invasion, by combining semi-analytical solution and neural network algorithm, induction logging response calculation speed is improved more than three orders of magnitude.

The invention discloses an intelligent decision support system and method based on a longitudinal model conduction chain, and belongs to the technical field of data processing systems, and the system comprises a model conduction engine which is used for constructing an ordered model sequence, taking the output vector of each model in the sequence as the input of a subsequent model, and forming a continuous decision conduction chain; the dynamic weight module is used for realizing weighted transmission from the output of the precursor model to the input of the subsequent model through a conduction matrix, and the conduction matrix is dynamically adjusted based on a model confidence error; and the closed-loop control unit is used for monitoring the actual achievement condition of the decision target and realizing global optimization of the longitudinal conduction chain by optimizing a full-stack target function. Through the method and the device, repeated processing of original data is avoided, ordered conduction of knowledge among models is realized, and consistency, efficiency and robustness of decision making are improved.

The invention discloses a dangerous driver longitudinal model construction method based on Levy random characteristics. The method comprises the following steps: firstly, collecting vehicle mass driving data in an actual road traffic environment, deriving driving intensity by using a driving condition Markov state equation, fitting the driving intensity by using Levy distribution, and generating a driving intensity random sample; secondly, calculating a driving intensity boundary of the current acceleration, finding an index of a driving intensity random sample at the boundary, reserving a long tail value of the index to ensure a rapid transfer path of a large and small step length of the driving intensity, randomly selecting the long tail value as the driving intensity at the current moment, obtaining the acceleration at the next moment, constraining the acceleration and calculating the speed at the next moment; and finally, updating the current speed and the current acceleration, and judging whether the maximum duration is reached or collision occurs or not until a dangerous longitudinal driving behavior sequence is output. According to the method, the random dangerous driver longitudinal behavior can be quickly generated, and a reference is provided for simulation test of an accelerated automatic driving longitudinal auxiliary driving system.

A method for safe platooning of multiple vehicles on roads based on model predictive control includes: establishing a discrete kinematic model of multiple vehicles considering vehicle spacing constraints and sideslip disturbances; designing a longitudinal model predictive controller based on the model, introducing a one-sided hinge function and time-varying weights to construct an asymmetric vehicle spacing penalty term in the cost function, and adding vehicle spacing and velocity / acceleration boundary constraints in rolling optimization to obtain longitudinal control quantities; designing a nonlinear extended state observer to estimate sideslip disturbances online; designing an integral sliding mode controller based on lateral tracking error, and combining a nonlinear fal(·) function to suppress chattering and output steering control quantities; this invention solves the problems of insufficient safety margin, decreased coordination accuracy, and lateral control chattering that easily occur in multi-vehicle platooning under strict vehicle spacing constraints and sideslip disturbances on curved roads, improves safe platooning capability under vehicle spacing constraints, enhances steering accuracy and trajectory tracking robustness on curved roads, and improves the smoothness of control commands.