11 results about "Error equation" patented technology

The application provides a kind of nonlinear system fault diagnosis method based on sliding mode observer and DCNN, it is related to fault diagnosis technical field.The method first constructs the mathematical model of nonlinear system, the mathematical model considers concurrent situation of system actuator and sensor fault and nonlinear term and external interference, and sliding mode observer is designed for nonlinear system;Then define the residual error of sliding mode observer, establish residual error equation;Determine the residual error threshold of sliding mode observer;Nonlinear system is monitored using sliding mode observer, residual error information is obtained and residual error information is preprocessed based on residual error threshold to establish residual error signal data set;Finally, the residual error signal generated by sliding mode observer is diagnosed in depth using deep learning model DCNN, and the fault diagnosis of nonlinear system is realized.

The application discloses a disturbance-resistant multi-agent discrete-continuous hybrid specified-time state estimation method, and belongs to the field of distributed state estimation of multi-agent systems, and comprises the following steps: S1, considering the state dynamics of the multi-agent system and the influence of external disturbance, a dynamic equation of the multi-agent system is constructed; S2, based on the dynamic equation of the multi-agent system, an augmented state estimation error equation and an extended state observer of the multi-agent system are constructed; S3, an estimation error dynamic equation in a discrete time domain is constructed, and the convergence of the augmented state estimation error is analyzed; S4, a discrete sampling interval is designed, so that the augmented state estimation error converges to zero within a specified time. The above-mentioned disturbance-resistant multi-agent discrete-continuous hybrid specified-time state estimation method can effectively cope with external disturbance, fuse the advantages of discrete and continuous time, be faster, more accurate and robust, and ensure that the state estimation of the multi-agent system is efficient and reliable.

This invention relates to the field of automatic control technology, and in particular to a method and system for asymmetric preset performance control of a cascade continuous stirred reactor without initial conditions. The method includes establishing a mechanistic model of the cascade continuous stirred reactor, transforming it into an error equation for the cascade continuous stirred reactor, pre-specifying that the change in reactant concentration error at the output of the cascade continuous stirred reactor satisfies a banded constraint space, converting the banded constraint space into a symmetric constraint space, constructing a radical-type algebraic saturation function to map the symmetric constraint space into a bounded symmetric constraint space, superimposing an exponential finite-time decay function on the bounded symmetric constraint space to construct a bounded symmetric constraint space without initial conditions, constructing a barrier Lyapunov function based on the bounded symmetric constraint space without initial conditions, and designing an asymmetric preset performance controller without initial conditions using the backstepping method. This invention enables the stable, safe, and rapid operation of the cascade continuous stirred reactor.

The application relates to an echo state network-based adaptive fault-tolerant control method for a non-strict repetitive system, which comprises the following steps: constructing a nonlinear dynamic system with an actuator fault, and setting a hypothesis condition; introducing a desired error trajectory, constructing a dynamic error equation, and defining a nonlinear function in the equation; approximating the nonlinear function by using an echo state network; proposing an adaptive iterative learning fault-tolerant algorithm, combining a barrier Lyapunov function, deducing a control input, constructing a controller, and filtering redundant batches; and constructing a barrier composite energy function to verify the stability and convergence of the designed adaptive iterative learning fault-tolerant algorithm. The nonlinear dynamic system constructed by the application can still realize effective tracking and control of the system state to the desired trajectory under the conditions that the actuator has additive or multiplicative faults, the system state is limited, and external disturbances exist.

The invention provides a nonlinear system fault diagnosis method based on a sliding-mode observer and a DCNN, and relates to the technical field of fault diagnosis. The method comprises the following steps: firstly, constructing a mathematical model of a nonlinear system, considering system actuator and sensor fault concurrency and nonlinear term and external interference by the mathematical model, and designing a sliding-mode observer for the nonlinear system; then defining a residual error of the sliding mode observer, and establishing a residual error equation; determining a residual error threshold value of the sliding mode observer; the method comprises the following steps: monitoring a nonlinear system by using a sliding mode observer, obtaining residual information, and preprocessing the residual information based on a residual threshold to establish a residual signal data set; and finally, a deep learning model DCNN is adopted to carry out deep diagnosis on residual signals generated by the sliding-mode observer, and fault diagnosis of a nonlinear system is realized.

The invention relates to the field of measurement error theoretical models, in particular to an additional parameter residual convergence method incorporating gross error and application thereof, and innovatively provides the additional parameter residual convergence method incorporating gross error by incorporating gross error into model precision evaluation so as to derive the additional parameter residual convergence method incorporating gross error. The gross error is incorporated into an error equation, residual errors are converged step by step through distribution of the residual errors, analysis of rule errors existing in the residual errors and additional parameter constraints, the gross error also has the capability of responding to a true value, the capability of weakening gross error contribution result deviation is finally achieved, and good error evaluation precision is achieved. The problems that in the prior art, gross error processing in the measured value is considered to be caused by misoperation in the measuring process or external environment change, and the gross error utilization rate is low are solved.

The application discloses a kind of vehicle formation adaptive fault-tolerant control methods based on predetermined performance, it is related to the adaptive sliding mode control technical field of vehicle formation, it includes, obtaining vehicle dynamic equation, set brake safety distance between vehicles, obtain spacing error equation, and set spacing error constraint, to obtain conversion spacing error equation;Nonlinear dynamics in third-order vehicle model is fitted using radial basis neural network, according to the error equation after conversion, design parameter adaptive law, obtain vehicle fault estimation value, neural network weight estimation value, the estimation value of interference and error, obtain adaptive fault-tolerant controller, realize fault-tolerant control to vehicle formation.The application can effectively ensure the safety of vehicle formation driving, improve fault tolerance rate when fault occurs.The application can be applied in the technical field of vehicle formation fault-tolerant control.

The application provides a data-driven lever arm error compensation method, and belongs to the technical field of inertial navigation. The application is used for solving the problem that the time-varying equivalent lever arm cannot be directly measured in the inertial navigation of a submerged vehicle, and it is also difficult to accurately separate the lever arm induced error from other sensor errors. The application comprises the following steps: S1, analyzing and modeling the errors affecting transfer alignment; S2, establishing a lever arm error equation and a data-driven compensation model; and S3, velocity error prediction and compensation.

The invention discloses a universal six-axis mechanical arm inverse kinematics solving method based on homotopy continuation, which comprises the following steps: acquiring a target pose of an end effector of a six-axis mechanical arm, and constructing a pose error equation about six joint variables based on the target pose and connecting rod parameters of the six-axis mechanical arm; constructing a homotopy equation based on the pose error equation and a preset auxiliary function; performing solution path tracking on the homotopy equation by adopting a numerical iteration method according to a preset change sequence of the homotopy parameters from a starting value to a stopping value, and sequentially substituting each value in the preset change sequence into the homotopy equation and solving to obtain a group of inverse kinematics solutions of the pose error equation; on the basis of a low-difference sequence sampling method, a plurality of groups of different initial joint variable values are generated in the six-dimensional joint space, the total group number of the initial joint variable values serves as the number of cycles, the iteration steps are repeatedly executed for each group of initial joint variable values, and multiple groups of different inverse kinematics solutions of the pose error equation are obtained.

The invention relates to the technical field of data compensation, in particular to a high-precision data compensation method and system. According to the technical scheme, the method comprises the following steps: data acquisition: synchronously acquiring target data and environment parameters, preprocessing the acquired original data to eliminate abnormal values and suppress random noise, and constructing a measurement matrix M and an environment matrix E; error modeling: calculating an integral nonlinear error INL and a differential nonlinear error DNL of the data based on the measurement matrix M, and establishing a nonlinear error fitting function fINL (x); and environment compensation: constructing an error equation containing the environment matrix E. Through organic combination of dynamic self-adaption, multi-source collaboration, intelligent learning and closed-loop control, a data compensation technology is upgraded from a static, isolated and passive traditional mode to a dynamic, collaborative and intelligent advanced mode, the compensation precision is extremely high, and the dynamic response speed and long-term environmental adaptability are excellent.

The present disclosure provides an IMU and LiDAR calibration method and system, and a map construction method. In addition to constructing characteristic lines, characteristic surfaces and GNSS residual error equations, the present disclosure further utilizes GNSS data to perform optimization and conversion operations in absolute coordinates, and further extracts three-dimensional structure information such as ground and wall surfaces in an initialized map, and according to the geometric characteristics of the flat ground and the vertical wall, corresponding constraint conditions are applied to construct plane and surface constraint equations and GNSS constraint equations, and the constructed constraint equations are used to further optimize the pose of laser data and further optimize the relative relationship between the IMU and the LiDAR until the optimized relative relationship converges.