13 results about "Uncertain systems" patented technology

A computer implemented method for obtaining a real-time map, h(x), of a distribution in a D-dimensional space of a physical quantity, ƒ(x), describing a true phenomenon characterizing a complex uncertain system, wherein said physical quantity, ƒ(x), is inaccessible to direct measurements. The method can be utilized to obtain real time maps characterizing complex uncertain systems from scarce and indirect measurements, using non-invasive techniques.

This invention discloses an integrated optimization control method for multi-axle vehicles under hub motor failure and in-wheel motor malfunction, belonging to the field of multi-axle vehicle multi-wheel independent drive lateral dynamics control technology. First, an ideal dynamic model and reference behavior of the multi-axle vehicle are established. Then, based on the uncertainty of the vehicle dynamics system caused by tire cornering stiffness changes due to tire nonlinearity, the dynamics system of the multi-axle vehicle is rewritten to obtain an uncertain system. Next, based on the uncertain system, top-level and bottom-level controller designs are performed for the multi-axle vehicle under hub motor failure and in-wheel motor malfunction, realizing the yaw moment reconstruction and allocation constraints for each wheel, thus obtaining the optimal control method for the multi-axle vehicle under hub motor failure and in-wheel motor malfunction. This invention achieves optimal tracking performance under hub motor failure, realizing driving safety and energy efficiency for multi-axle vehicles.

The present application relates to the field of underwater robot automatic control, and aims to provide a kind of underwater robot variable parameter self-disturbance trajectory tracking control method.It includes: the dynamics model of underwater robot is established, four degrees of freedom extended state observer is designed based on dynamics model, the estimated value of corresponding motion parameter is calculated using each degree of freedom extended state observer respectively, error calculation is carried out, and real-time parameter value is obtained using adaptive function calculation;According to the trajectory tracking control law obtained by calculation, the state and output of each extended state observer are updated, and the underwater robot is controlled.The present application is based on active disturbance rejection control algorithm, can effectively estimate and compensate internal and external disturbances of system, is very suitable for the control problem of underwater robot nonlinear and uncertain system.Even in complex underwater environment, efficient and stable trajectory tracking effect can be achieved;Using adaptive parameter adjustment eliminates manual adjustment work and improves the performance of control system.

The invention relates to the technical field of intelligent power grid dispatching, in particular to application of an optimal control method based on a Hurwicz criterion in intelligent power grid dispatching. The method comprises the following steps: a pessimistic value optimal control research framework is based on an uncertain system; introducing an uncertain singular non-causal system, and outputting an equivalent transformation form; establishing an optimal control problem unification framework of the Hurwicz criterion; the Hurwiz type optimal control method aims at the Hurwiz type optimal control problem of a linear and nonlinear uncertain singular non-causal system. And the Hurwicz type optimal control obtained in the step S5 is applied to intelligent power grid dispatching. According to the application of the optimal control method based on the Hurwicz criterion in intelligent power grid dispatching, dynamic programming and uncertainty theories are combined, a recursion equation is constructed to solve the optimal control problem in intelligent power grid dispatching, an analytical expression and an optimal value of optimal control are deduced for linear and nonlinear uncertain singular non-causal systems, and the optimal control method based on the Hurwicz criterion is applied to intelligent power grid dispatching. And the practical applicability of the provided framework is verified through a calculation example.

This invention discloses an elastic control method for linear uncertain systems based on multi-rate state measurement information. The method includes establishing a linear dynamic model and a state estimation model based on multi-rate state measurement information of the controlled system; acquiring state measurement information of the controlled system; resetting the state of the state estimation model based on the state measurement information; designing an elastic control law based on the linear dynamic model and the state estimation model; inputting control commands based on the elastic control law into the actuator to stabilize the state of the controlled system; and controlling the system with a period T. s The above process is repeated at intervals until control ends. This invention can maintain stable system operation even when there are parameter uncertainties, actuator errors, and irregular updates of state measurement information.

The invention relates to the technical field of electronic manufacturing, and discloses an AOI detection collaborative optimization method and system applied to an AI re-judgment and intelligent screening mechanism, and the method comprises the following steps: S1, automatic optical detection data collection; s2, establishing AI server communication; s3, data extraction and transmission; s4, a data caching and delay control mechanism; s5, checking bad data; s6, carrying out secondary re-judgment by the re-judgment station equipment; s7, data file generation and tracing query; and S8, performing closed-loop feedback and model optimization. According to the AOI detection collaborative optimization method based on the AI re-judgment and intelligent screening mechanism, efficient transmission and scheduling of detection data, intelligent screening of AI results and closed-loop feedback of manual re-check are achieved, the problems that in the prior art, the re-judgment process depends on manual work, the judgment opportunity is uncertain, and system response is lagged are effectively solved, and the AOI detection collaborative optimization method based on the AI re-judgment and intelligent screening mechanism is achieved. And the intelligent level and the production efficiency of production line quality management are obviously improved.

This invention discloses a worst-case Lyapunov stable reinforcement learning control algorithm for uncertain systems, comprising the following steps: S1, modeling the system dynamics and uncertainty boundary using a ReLU activation network; S2, determining robustness conditions and using them to pre-determine the area of ​​the attraction domain; S3, determining the robustness guarantee RL under dynamic model uncertainty and state estimation; S4, establishing network parameterization; and S5, performing numerical simulations on an inverted pendulum and a quadcopter UAV. This invention employs the aforementioned worst-case Lyapunov stable reinforcement learning control algorithm for uncertain systems, still accurately finding the most inverse state, thereby forcing its stability under uncertainty. A geometric view of the existence of robust RL solutions is provided to explain robustness and its capability. Numerical simulations on an inverted pendulum and a quadcopter under various uncertainties demonstrate the effectiveness of the proposed method.

The invention relates to the technical field of electric power system operation and control, in particular to application of a time-delay uncertain singular system based on an alpha-pessimistic value criterion in electric power. The method comprises the following steps: establishing a time-delay uncertain singular system model of a power system; based on an alpha-pessimistic value criterion, constructing an index function for evaluating the operation performance of the power system; constructing a zero-sum game framework; and equivalently converting the time-delay uncertain singular system model into a standard uncertain system model through model transformation to obtain an optimal source side control sequence and an optimal load side control sequence. According to the application of the time-delay uncertain singular system based on the alpha-pessimistic value criterion in power, time delay, uncertainty and system singularity are considered in power system optimization, and the alpha-pessimistic value criterion is introduced, so that a decision maker can flexibly express the risk preference of the decision maker by adjusting the confidence level alpha; extreme risks caused by uncertainty are avoided according to the alpha value, and the toughness of the power system in an uncertain environment is enhanced.

The invention is suitable for the technical field of power systems, and relates to an SC-PID-based distributed photovoltaic power station reactive voltage regulation method, which comprises the following steps of: converting a non-affine nonlinear uncertain system into a linear uncertain affine system through a grid-connected inverter SC-PID control algorithm; an SC-PID control model under integral separation is established through an SC-PD algorithm for separating integral terms and an SC-PID algorithm for retaining the integral terms; setting a dead zone on the basis of the SC-PID control algorithm, and establishing an SC-PID control model with the dead zone; an SC-PID control model of segmented fixed coefficient differential is established through an SC-PID algorithm of retaining a differential item and an SC-PI algorithm of removing the differential item, and cooperative control of proportion, integration and differential is completed by using a speed factor in a controller. The method is simple in process and convenient to operate, and the dynamic quality and the steady-state precision of the distributed photovoltaic power station are improved.

This invention discloses a method for predicting the dynamic response and reliability of a hybrid uncertain system, belonging to the field of uncertain system dynamics and reliability prediction. The implementation method of this invention is as follows: A polynomial surrogate model of the hybrid uncertain system is represented based on the PCLM method. An orthogonal polynomial basis corresponding to the random variables is established based on data-driven methods: a one-dimensional orthogonal polynomial basis is represented based on the origin moments of the random variables; a tensor product operation is performed to obtain a multi-dimensional orthogonal polynomial basis corresponding to the random variables; and Legendre polynomials are used as the orthogonal polynomial basis corresponding to the interval parameters. The coefficient matrix γ in the polynomial surrogate model of the hybrid uncertain system is solved using a double-weighted least squares method. Based on the established polynomial surrogate model, the dynamics and reliability of the hybrid uncertain system are predicted respectively. This invention can improve the accuracy and efficiency of predicting the dynamic response and reliability of hybrid uncertain systems.

The invention relates to an automatic driving fleet transverse dynamics inclusion control method based on output feedback, and the method comprises the steps: constructing a transverse dynamics model of an automatic driving vehicle, abstracting the transverse dynamics model as a leader following multi-agent system, and constructing a communication topological graph; designing an open-loop reference model and a closed-loop reference model for the follower agent; an open-loop reference model is constructed, the open-loop reference model comprises a controller, reference control input of a follower agent is solved, and the open-loop reference model is converged into a convex hull formed by the system state of the leader vehicle; and constructing a decentralized model reference adaptive output controller, updating the gain matrix and resolving the control input of the follower vehicle, so that the follower vehicle tracks the open-loop reference model, and the inclusion control of the automatic driving vehicle is realized. According to the method, when uncertain system parameters, unknown input gains and directed communication topology exist, the stability of a closed-loop system can still be ensured, a multi-agent inclusion control target is realized, and the engineering realizability and robustness of the method are improved.

1. A computer-implemented method for obtaining a real-time map h(x) of the distribution in a D-dimensional space of a physical quantity f(x) describing a real phenomenon (1) characterizing a complex and uncertain system, the physical quantity f(x) being inaccessible to direct measurements, the method comprising: The spatial distribution of the physical quantity f(x) using a set of parameters (s) [Number 104] describing the complex and uncertain system using a mathematical model to simulate JPEG2025537042000106.jpg1423; A particular set of values ​​s n a library of approximations of the physical quantity f(x) by performing a plurality of simulations through the assignment of [Number 105] Steps to build JPEG2025537042000107.jpg1471; formula [Number 106] calculating the real-time map h(x) representing a real-time approximation of the physical quantity f(x) in JPEG2025537042000108.jpg2667, wherein a n , n=1,...,N is the magnitude of the difference |ν l is an expansion factor determined by solving a system of L equations with N unknowns to minimize |, where l=1,...,L, and the difference is [Number 107] JPEG2025537042000109.jpg1397, where x l is a D-dimensional point, [Number 108] JPEG2025537042000110.jpg1241 is a measurement process [Number 109] The impact on JPEG2025537042000111.jpg1214 l is the value measured at [Number 110] JPEG2025537042000112.jpg1114 is the measurement process for the first measurement involved [Number 111] This is the mathematical model of JPEG2025537042000113.jpg1113, [Number 112] JPEG2025537042000114.jpg1340 is the mathematical model applied to the function g [Number 113] JPEG2025537042000115.jpg1312 is point x l , where l=1,...,L is a value that the computer-implemented method is described.