11 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 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.

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.

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.

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 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.