7 results about "Nonlinear control law" patented technology

The invention provides an air-ground autonomous landing control method for a fixed-wing unmanned aerial vehicle facing a movable base platform. The air-ground autonomous landing control method comprises the following steps: firstly, establishing a kinetic model of the fixed-wing unmanned aerial vehicle and a relative motion model of the unmanned aerial vehicle and a movable base; secondly, considering the influence of the ground effect, the wake flow of the movable base and the movement of the movable base on the landing precision, and respectively introducing disturbance models; then designing a trajectory control law based on a nonlinear L1 control law, and controlling the unmanned aerial vehicle to slide down to a target point along an optimal trajectory by generating a trajectory acceleration instruction in real time; designing a hierarchical attitude control method based on incremental nonlinear dynamic inverse, designing an angular velocity control law for an outer ring attitude angle by adopting a nonlinear dynamic inverse control method, and obtaining an angular acceleration control law for an inner ring angular velocity by adopting an incremental nonlinear dynamic inverse method; and finally, incremental power compensation accelerator control is designed, speed error feedback is introduced, and dynamic adjustment of a thrust instruction is achieved. The problem of strong coupling interference in the landing process of the unmanned aerial vehicle on the dynamic base platform is solved.

The invention discloses a permanent magnet synchronous motor rotor flux linkage information acquisition method, and relates to the technical field of motor position-less control. An alpha-beta shafting voltage model stator flux linkage at the current moment is obtained based on the back electromotive force; calculating a dq shafting current model stator flux linkage at the current moment, and converting the dq shafting current model stator flux linkage into an alpha-beta shafting current model stator flux linkage through inverse Park conversion; constructing a nonlinear control law, deducing to obtain a compensation amount, and carrying out voltage model stator flux linkage correction; and obtaining the rotor flux linkage at the current moment according to the compensated stator flux linkage of the voltage model, and calculating a rotor position angle and a feedback rotating speed. Through cooperative work of a voltage model and a current model, a nonlinear controller is used for replacing a traditional PI controller, compensation of a stator flux linkage of the voltage model is achieved, then the obtaining precision of rotor flux linkage information is improved, and meanwhile the dynamic response performance and the steady-state characteristic of the system are considered.

This invention discloses a nonlinear control law evolution method based on a large language model and control effect evaluation index, including initializing the control law; converting the weights of the control law operators into sampling probabilities; sampling each control law operator according to the probability distribution of the control law operators in the large language model to generate a control law formula; inputting the set of control law formulas into an aircraft dynamics simulation model to obtain a tracking error sequence and determining the evaluation index matrix of the tracking error sequence; analyzing the relationship between the control law operators and the evaluation index and outputting the update gradient of each control law operator; updating the weights of each control law operator and generating multiple new control law operators, and initializing the weights of the new control law operators; ending the iteration when the number of iterations reaches the maximum limit and outputting the control structure and control parameters of the control law, otherwise resampling and iterating; this invention solves the problem that large language models cannot be embedded in the dynamics simulation environment and evaluate the effect of control laws.

The invention relates to the technical field of Internet of Things data acquisition and monitoring, and discloses a self-adaptive monitoring frequency adjustment method and system based on streaming time series data volatility analysis, and the method comprises the steps: obtaining a time series sensor data stream collected by a sensor; dynamically determining a smoothing coefficient according to the actual arrival time interval between the current data point and the previous data point, and updating the smoothing coefficient based on a quasi-likelihood loss function; inputting the real-time volatility into a nonlinear control law based on a Sigmoid function, and mapping to obtain a target sampling frequency; and on the basis of the real-time volatility, hysteresis judgment is performed by utilizing Schmitt trigger logic, an anti-oscillation frequency control decision is generated in combination with preset cooling time, and the data acquisition frequency of the sensor is dynamically adjusted. According to the method, on-demand sampling can be realized according to the signal fluctuation condition, noise and state mutation are effectively distinguished, and the capture sensitivity of abnormal working conditions and the system stability are remarkably improved while the data storage and transmission cost is reduced.

This invention provides a nonlinear flight control method and system for wind-disrupted unmanned aerial vehicles (UAVs). It utilizes a pre-set weather radar to acquire wind field disturbance characteristics in real time; inputs these characteristics into a pre-set aerodynamic disturbance prediction model to generate predicted aerodynamic torque fluctuation values; applies a pre-set dynamic filtering process to the amplitude characteristics of these predicted aerodynamic torque fluctuation values ​​to generate a control command correction signal; and couples this correction signal with a pre-set nonlinear control law of the UAV flight control system to suppress UAV attitude deviation caused by wind disturbance in real time, thus performing nonlinear flight control of the UAV. This invention overcomes the combined defects of lag and filtering mismatch in traditional linear control under strong wind abrupt changes, achieving stable attitude control of the UAV during low-altitude wind shear and sudden gusts, significantly improving the UAV's anti-disturbance response speed, and greatly reducing the risk of UAV loss of control.

The invention relates to a manned aerial vehicle and unmanned aerial vehicle cooperative formation control method and system. According to the method, a polar coordinate system with a manned aerial vehicle as a pole is established, and formation control is decoupled into a radial distance adjustment subsystem and a tangential azimuth angle tracking subsystem. An incremental model predictive controller is adopted for radial control, and smooth distance tracking is achieved by optimizing an objective function and embedding safety distance and speed constraints; tangential control adopts a non-linear control law based on a barrier Lyapunov function, the azimuth angle of the unmanned aerial vehicle is strictly constrained in a safety sector area behind a pilot, and the unmanned aerial vehicle is allowed to be flexibly adjusted in the area. The method only depends on the relative position of the unmanned aerial vehicle to the manned aerial vehicle and the attitude information of the unmanned aerial vehicle, has strong robustness and adaptability, and realizes the stable, flexible and safe cooperation of the formation configuration under complex maneuvering.

The application discloses a method and device for designing a disturbance rejection controller and a storage medium, and belongs to the field of electromechanics. The method comprises the following steps: building a motor control system based on feedforward field weakening control, building a disturbance rejection controller model and taking the model as a speed loop of the control system; replacing a nonlinear error feedback control law of a traditional disturbance rejection controller with a neural network, combining a Markov decision process with the disturbance rejection control, building a deep reinforcement learning model, taking the motor control system as an environment and the operation condition of the motor as a state, setting a reward according to a steady-state effect of the speed and an anti-interference ability, training the deep reinforcement learning model by using a double-delay deep deterministic gradient descent strategy, enabling an algorithm intelligent agent to autonomously learn the hyperparameter setting of the neural network, completing training of a new disturbance rejection controller model based on deep reinforcement learning, and obtaining an optimal scheme. The above method can solve the problems of a large number of parameters of the disturbance rejection controller and a limited application range of the nonlinear control law.