19 results about "Fir system" patented technology

The invention belongs to the technical field of EL system control, and discloses a preset performance control method considering dynamic and constraint of an execution mechanism. The method comprises the following steps: firstly, establishing a system dynamics model considering dynamic and input saturation constraints of an executing mechanism; then, designing an asymmetric tubular performance boundary to restrain a system tracking error, and defining a conversion error according to the system tracking error; meanwhile, a disturbance observer based on immersion and invariance is designed, and composite disturbance is estimated. Furthermore, a tubular preset performance controller is designed by adopting a backstepping method, a virtual control law and an expected torque are constructed, and a multi-state auxiliary system is introduced to compensate the input saturation influence. And finally, a control instruction is generated by integrating the auxiliary system state, the virtual control law, the expected torque and the interference estimation value, and the control instruction acts on the system after being processed by a saturation function. According to the method, actuator dynamics, input saturation and external disturbance can be processed at the same time, and tracking errors are ensured to strictly follow a preset performance boundary.

The present disclosure relates to methods and apparatuses for generating and reporting a CSI report. The method performed by a UE comprising: receiving (400) a CSI report configuration from a network node (600); determining (401) one or more frequency-domain, FD, components for the set of linear combination coefficients, determining (402) one or more time-domain, TD, components for the set of linear combination coefficients, determining (403) one or more spatial domain, SD, components for the set of linear combination coefficients, determining (404) a set of frequency- / time (FD / TD)-component pairs, each pair comprising an FD component and a TD component, commonly across a subset of spatial domain components for the set of linear combination coefficients, and, generating and transmitting or reporting (405), to the network node (600), a CSI report, the CSI report comprising an indication of the determined SD, FD and TD component(s), FD / TD-component pairs, and combination coefficients of the precoder vector or matrix.

The invention relates to the technical field of automobile steer-by-wire system control, in particular to a trajectory tracking control method based on data-driven fuzzy value iteration. For nonlinearity and uncertainty of a steer-by-wire (SBW) system, a T-S fuzzy technology is adopted to construct a local linear model of the SBW system. Constructing a fuzzy augmented tracking system by introducing a reference signal; secondly, a fuzzy discount coupling algebraic Riccati equation based on a Hamilton-Jacobi-Bellman equation is deduced; and a data-driven fuzzy tracking value iteration (VI) algorithm is designed, and the tracking control problem of the system is solved through online iteration. The algorithm has the prominent characteristics that the dependence on system dynamics information is completely eliminated, and closed-loop trajectory tracking control can be realized without an initial stable control strategy. The method can be widely applied to the fields of intelligent driving, automatic driving steering control and the like, and is particularly suitable for scenes with uncertain system models and difficult control initialization.

This invention discloses an intelligent model predictive control method for intensive mixing processes based on reinforcement learning. Addressing the degradation of control performance caused by model mismatch due to modeling errors, abrupt state changes, and component degradation in traditional model predictive control, this invention introduces reinforcement learning into the model predictive control framework. The method designs an intelligent model predictive control approach based on reinforcement learning to solve a standard quadratic programming problem with respect to optimization variables. Reinforcement learning selects compensation terms to compensate for model deviations caused by model mismatch, combining this with the controller output and applying it to the system. Finally, the reward is calculated and updated to optimize the control compensation selection for future time steps. As the control cycle progresses, new information is continuously integrated, model predictions are updated, and control inputs are optimized to adapt to potential changes in system behavior or external disturbances. This improves system stability and control accuracy, ensuring that the intensive mixing process can track a preset trajectory.

The invention relates to a fault-tolerant control method for preset performance of a nonlinear multi-agent system, and the method comprises the following steps: S1, building a high-order nonlinear multi-agent system model in consideration of a multiplicative fault and an additive time-varying fault of an actuator; s2, aiming at an unknown nonlinear function and a fault item in the high-order nonlinear multi-agent system model, constructing an online identifier by utilizing an RBF neural network, reconstructing a system state and estimating a fault value; s3, designing a sliding mode surface to reduce the system order based on the state information provided by the online identifier; s4, on the basis of the converted error system, defining a performance index function, deducing an HJB equation and a theoretical optimal control law, and converting fault-tolerant control into an optimal adjustment problem; and S5, solving an optimal adjustment problem by adopting Actor-Critic reinforcement learning, obtaining optimal control, and outputting a final control quantity to act on the system. According to the invention, multi-agent adaptive optimal fault-tolerant control is realized.

This invention provides an event-driven communication method and apparatus for a multi-threshold quantization system, relating to the field of communication technology. The method includes: acquiring the input vector of an FIR system; obtaining the output vector through the FIR system based on the input vector, and then obtaining a measurement vector through a multi-threshold quantizer with m thresholds; dynamically adjusting the trigger reference value of an event trigger based on the data frequency of the completed transmission using the measurement vector independently counted by both the sender and receiver; obtaining a trigger state value by comparing the measurement vector with the reference value; and generating a transmission value by multiplying the trigger state value with the measurement vector. The sender is a multi-threshold quantizer, and the receiver is a remote estimation center, which estimates the unknown parameters of the FIR system based on the transmission value. This invention reduces the number of communication operations while ensuring data integrity without increasing additional computation or feedback communication.

According to the invention, the system twinning (101) is optimized in terms of robustness, the system twinning (101) comprising a behavioral model (104) describing an undisturbed behavior of the system (102) and a behavior expected under the influence of one or more predefined disturbances (105, 107, 108), a model of the environment (113) being provided, the system (102) being implemented for interacting with the environment (113), and the system (102) being implemented for interacting with the environment (113). The system (102) is connected to the environment (113) such that the environment (113) supplies at least one input signal (106) to the system (102) and receives at least one output signal (109) from the system (102), where the system (102) together with the environment (113) forms an overall system (115), the system twinning (101) and the environment model (114) are linked to form an overall model (116), the overall model (116) is executed at least twice to obtain simulation data (119), the total model (116) in the undisturbed state is used in the first execution; and synchronously simulating at least one interference (105, 107, 108) of the system (102) during a second execution of the total model (116) in order to simulate a behavior of the total system (115) under the influence of the interference (105, 107, 108) acting on the system (102); analyzing the simulation data (119) to determine an expected effect (121) of the at least one disturbance (105, 107, 108) on the overall system (115) from a comparison of data determined during a first execution of the overall model (116) with data determined during a second execution of the overall model (116); at least one operation is determined and communicated to the user as a function of the expected effect (121).

The application discloses a pseudo-parameterized model establishment method based on a GIS system and belongs to the technical field of engineering modeling. The application obtains a to-be-assembled component by analyzing an original engineering model by using a GIS system, obtains control points and structural relationships between the control points according to the to-be-assembled component, connects the control points according to the structural relationships between the control points to form an engineering structure skeleton model, establishes a static component based on the engineering structure skeleton model, and hangs the static component on corresponding control points of the engineering structure skeleton model to establish an engineering structure static model, so that a bridge structure model applicable to a GIS system is finally obtained, the size of the model is reduced, the user experience of an engineering digital model is improved, and the requirement for hardware resources is reduced.

The invention belongs to the technical field of wireless communication, and relates to a self-adaptive Gaussian message passing signal detection method for an OTFS system, which comprises the following steps: the system receives a signal transmitted by a channel, and initializes parameters of a GA-MP algorithm; executing the GA-MP algorithm on the to-be-estimated symbol of the current iteration round according to the received signal based on the parameters of the GA-MP algorithm of the current iteration round to obtain the posterior probability of the symbol; adjusting a damping factor in the parameter according to the posterior probability of the symbol to obtain a parameter of a next iteration round; s2, judging whether iteration is continued or not, if yes, obtaining a to-be-estimated symbol of a next iteration round, and returning to the step S2 to execute the next iteration on the symbol; otherwise, obtaining the final posterior probability of the sent signal; calculating an estimation result of the sent signal according to the final posterior probability, and performing post-processing optimization; according to the method, the damping factor is adaptively adjusted according to the posterior probability variation and the residual trend, and the convergence speed and the stability are effectively balanced.

This invention discloses a semi-global consistency control method for heterogeneous nonlinear multi-agent systems. The method includes considering nonlinear terms related to the absolute and relative velocities of the agents, and heterogeneously constructing first-order and second-order agents to obtain a heterogeneous nonlinear multi-agent system. Based on the heterogeneous nonlinear multi-agent system, the method obtains the state equations of the heterogeneous nonlinear multi-agent system. Based on the state equations, the method obtains initial constraints that satisfy the semi-global consistency of the heterogeneous nonlinear multi-agent system. Based on the initial constraints, the method constructs an open-loop transfer function for system controller design, thereby constructing a system controller to achieve semi-global consistency control of the heterogeneous nonlinear multi-agent system. This invention solves the problem that traditional methods, when dealing with such systems, struggle to ensure coordinated consistency among agents under complex operating conditions, and that the nonlinear factors of the system easily lead to instability or failure to converge to a consistent state.

The present application relates to the technical field of automobile steer-by-wire system control, in particular to a trajectory tracking control method based on data-driven fuzzy value iteration. In view of the nonlinearity and uncertainty of the steer-by-wire (SBW) system, a local linear model of the SBW system is constructed by using T-S fuzzy technology. By introducing a reference signal, a fuzzy augmented tracking system is constructed. Then, a fuzzy discount coupled algebraic Riccati equation based on Hamilton-Jacobi-Bellman equation is derived. Further, a data-driven fuzzy tracking value iteration (VI) algorithm is designed to solve the tracking control problem of the system through online iteration. The outstanding feature of the algorithm is that it completely eliminates the dependence on system dynamics information, and it can realize closed-loop trajectory tracking control without an initial stable control strategy. The method can be widely applied to intelligent driving, automatic driving steering control and other fields, and is particularly suitable for scenes where the system model is uncertain and control initialization is difficult.

The invention belongs to the technical field of indoor positioning, and discloses a robust indoor positioning method and system based on a weighted LP norm, and the method comprises the steps: firstly constructing a linearization measurement model based on a TOA distance measurement value; then constructing an outer-layer weight matrix by calculating the covariance of a noise propagation error, and forming a weighted LP norm objective function in combination with an inner-layer weight matrix constructed by a current residual error; and solving the objective function by adopting an iterative optimization algorithm until the final coordinate of the user equipment is obtained through convergence. According to the invention, the influence of impulse noise and outliers is adaptively suppressed through a double-layer weighting mechanism, and high positioning precision and stability can still be kept in a non-Gaussian noise environment. The method does not need to add extra hardware, is high in calculation efficiency, is easy for engineering deployment, is suitable for a UWB system, and can also be popularized to other wireless positioning scenes based on distance measurement.

A method includes determining, by an analysis system, a system aspect of a system for a protection evaluation. The method further includes determining, by the analysis system, at least one evaluation perspective for use in performing the protection evaluation on the system aspect. The method further includes determining, by the analysis system, at least one evaluation viewpoint for use in performing the protection analysis on the system aspect. The method further includes obtaining, by the analysis system, protection data regarding the system aspect in accordance with the at least one evaluation perspective and the at least one evaluation viewpoint. The method further includes calculating, by the analysis system, a protection rating as a measure of protection maturity for the system aspect based on the protection data, the at least one evaluation perspective, the at least one evaluation viewpoint, and at least one evaluation rating metric.

The present disclosure discloses a system and method for controlling the operation of a system affected by uncertainty in an operational variable of the system. The method includes collecting a plurality of samples of the uncertainty of the operational variable, constructing an empirical quantitative function associated with the uncertainty of the operational variable based on the collected plurality of samples, determining confidence limits for the empirical quantitative function to constrain an approximation error between the empirical quantitative function and the true quantitative function, determining an uncertainty set based on the empirical quantitative function constrained by the confidence limits, reformulating chance constraints into deterministic constraints based on the uncertainty set, solving an optimal control problem affected by the deterministic constraints to generate one or more control commands for one or more actuators of the system, and controlling the operation of the system based on the control commands.

This invention provides a flexible phase compensation method suitable for CVQKD systems, comprising: S1, constructing N random number sequences and M phase drift check sequences at the transmitting end; S2, modulating the M phase drift check sequences; S3, transmitting the modulated M phase drift check sequences through the channel and detecting and acquiring them at the receiving end; S4, applying Wiener filtering to the detection and acquisition results of the M phase drift check sequences; S5, calculating the phase drift value of the Wiener-filtered phase drift check sequences; and S6, compensating the calculated phase drift value onto the corresponding random number sequence. This invention designs multiple sets of shorter check sequences based on the Wiener filtering principle, performs time-domain denoising, and calculates the phase drift value, which can significantly shorten the check sequence length, flexibly adjust the data frame length, and split and combine long and short frames, thereby effectively improving data utilization and obtaining a higher security code rate.

This invention belongs to the field of indoor positioning technology and discloses a robust indoor positioning method and system based on the weighted LP norm. The method first constructs a linearized measurement model based on TOA ranging values; then, it constructs an outer weight matrix by calculating the covariance of noise propagation error, and combines this with an inner weight matrix constructed from the current residuals to form a weighted LP norm objective function; an iterative optimization algorithm is used to solve this objective function until convergence yields the final coordinates of the user device. This invention adaptively suppresses the influence of impulse noise and outliers through a two-layer weighting mechanism, maintaining high positioning accuracy and stability even in non-Gaussian noise environments. This method requires no additional hardware, has high computational efficiency, and is easy to deploy in engineering. It is not only applicable to UWB systems but can also be extended to other ranging-based wireless positioning scenarios.

The present disclosure provides a SerDes parameter adjustment system and method. The SerDes parameter adjustment system comprises an adjustment device, a cooperative component and a target component. The present disclosure adopts a plurality of component cooperation modes, sends a first instruction from the cooperative component to control the target component to adjust the SerDes parameter, and obtains state information after configuration, sends a request from the cooperative component to obtain confirmation information from the target component to judge the channel quality, and then determines a second instruction for adjusting the SerDes parameter according to the state information and the channel quality. In this way, the SerDes parameter with the least bit error rate suitable for the system and the environment can be quickly iterated and found, and the reliability problem of the basic physical path can be solved, and the problem of difficult coordination of signal integrity in the high-speed serial path and the difficulty of SerDes parameter adjustment can be solved.

The application provides a kind of adaptive PI type nonlinear control method, equipment and medium suitable for MIMO system, method includes: establishing MIMO system state equation;According to MIMO system state equation, establish MIMO system error equation;According to MIMO system error equation, design system error function S;According to system error function S, establish performance index function J z ;Select Liapunov function V1;Design system controller u d ;The stability of MIMO system controlled by system controller u d Is proved by using Liapunov function V1.The application solves the problem of being unable to effectively observe the entire operation process by using performance index function, solves the problem of being limited by square matrix and non-square matrix of MIMO system matrix by designing the controller, solves the problem of being difficult to select parameters of PID control method, and solves the problem of being unable to handle nonlinear terms by PID control method in MIMO system.

The application discloses a kind of based on reinforcement learning's mixing temperature iterative learning prediction control method.It contains: S1, based on reaction energy conservation principle establishes mixing process system temperature control model;S2, establish the system temperature control model of state space expression form;S3, the batch characteristic of process is designed iterative learning control law, establishes iterative axis error model;S4, design two-dimensional system iterative learning model prediction control method, determine cost performance index function;S5, solve the standard quadratic programming problem about optimization variable;S6, set the hyperparameter of Soft Actor-Critic algorithm, the policy network of training completion is combined with ILMPC controller output, and act on system;S7, according to the sample data of buffer area updates network parameter.The application optimizes the control robustness of control system under non-repetitive disturbance and unknown dynamic change, effectively improves the stability of mixing process glue discharge temperature.