8 results about "Nonlinear approximation" patented technology

The invention relates to the technical field of intelligent sensing systems, in particular to an algorithm and system for avoiding water surface floating objects and improving monitoring precision based on radar water level monitoring, and the method comprises the following steps: collecting radar echoes, constructing spatial-temporal characteristics based on waveform asymmetry and coherent attenuation gradient, and calculating the spatial-temporal characteristics; according to the method, the central moments of the front edge and the rear edge of the echo are calculated, the waveform asymmetry is quantified, the dynamic stability is analyzed in combination with the attenuation gradient of the multi-time-lag coherence coefficient, the floating object interference is recognized from the double dimensions of the spatial form and the time memorability, and the actual water level is inverted. An effective scattering trailing interval is positioned, nonlinear approximation operation is performed on trailing data in combination with an index physical model, a shielded water surface energy distribution curve is reconstructed, a derivative zero point is solved, distance measurement deviation caused by floating objects is corrected, the real water level height is effectively restored, and the monitoring anti-interference capability and the data accuracy in a complex hydrological environment are improved.

This invention provides a method for predicting material removal power in drilling-expansion hybrid machining based on SR-TELM, relating to the field of CNC machine tool power prediction technology. It delves into the physical mechanism between machining parameters and material removal power in drilling-expansion hybrid machining by using a symbolic regression algorithm. By embedding the mechanism model into the hidden layer neurons of TELM, it organically combines the advantages of symbolic regression in analyzing physical mechanisms with the powerful nonlinear approximation capability of TELM. This effectively improves power prediction accuracy under limited data conditions while ensuring the physical interpretability of the model, providing a new solution for modeling material removal power in drilling-expansion hybrid machining of machine tools.

The invention discloses an electric ship power adjusting device based on self-adaptive control. The electric ship power adjusting device comprises a sensor module, a data processing module, a self-adaptive control module, an actuator driving module and a power output module. The sensor module collects state parameters, operation parameters and environment disturbance information, and the state parameters, the operation parameters and the environment disturbance information are input into the self-adaptive control module after being standardized by the data processing module. The self-adaptive control module processes disturbance, non-linear terms and actuator faults through cooperative work of built-in disturbance observation, unknown non-linear approximation, fault-tolerant control and preset performance constraint units, generates self-adaptive control instructions, converts the instructions into driving signals through the actuator driving module, controls the power output module to adjust power, rotating speed and energy distribution, and controls the power output module to output power. Dynamic power adaptation of the electric ship is achieved, and navigation stability is guaranteed.

The invention discloses an unmanned aerial vehicle interference observer design method based on an RBF neural network. According to the method, the nonlinear interference observer is combined with the RBF neural network, and the real-time online learning and nonlinear approximation capabilities of the RBF neural network are utilized to perform online approximation on an estimation error generated by the nonlinear interference observer in actual operation, so that the influence of model parameter drift on the interference estimation precision is effectively weakened, and the interference estimation accuracy is improved. And the problem of noise amplification caused by high gain is avoided. According to the method, on the premise that a huge offline sample library does not need to be constructed, the improved interference observer can adaptively track various uncertain interferences in the flight process of the unmanned aerial vehicle, interference estimation errors are remarkably reduced, the real-time performance and accuracy of interference estimation are improved, a more reliable interference compensation basis is provided for an unmanned aerial vehicle control system, and the interference compensation efficiency is improved. The flight stability and control precision of the unmanned aerial vehicle in a complex dynamic environment are guaranteed, and the requirement for efficient and safe operation of the unmanned aerial vehicle is met.

This invention discloses a reinforcement learning-based attack detection method for quadcopter drones, comprising: estimating the state values ​​of the drone during flight using Kalman filtering; obtaining the corresponding residual correlation terms; dividing the entire detection space into several detection intervals; setting reward values ​​for two actions: continuing detection and detecting an attack; setting different simulated attack times; training Q-tables for different simulated attack times using the Saras algorithm; fusing the trained Q-tables for different simulated attack times using a weighted average; fitting the weighted average fused Q-table using a neural network; and detecting attacks on the drone online using the Q-table fitted by the neural network. This invention utilizes reinforcement learning technology and integrates the nonlinear approximation capability of neural networks to achieve the detection of subtle and covert attacks on quadcopter drones. It not only improves the recognition ability of subtle and covert attacks but also enables online detection of new attack patterns and reduces attack detection latency.

The application discloses a kind of nonlinear system dynamic event triggering optimization control methods of reinforcement learning, it is related to triggering tracking control field, comprising the following steps: step S1, establish nonlinear multi-agent system model;Step S2, construct double-layer MLP nonlinear approximation model;Step S3, design consensus reinforcement learning control law;Step S4, design dynamic event triggering mechanism;Step S5, design weight update law and verify stability.The application adopts the nonlinear system dynamic event triggering optimization control method of reinforcement learning described above, while significantly saving communication and computing resources by using adaptive dynamic event triggering mechanism, the optimal control performance of the system is realized;Closed-loop system stability is strictly guaranteed, and Zeno behavior is effectively excluded, the effectiveness of the proposed strategy is verified by numerical testing on a multi-motor system.

The present application belongs to the technical field of hypersonic vehicle control, and relates to a reusable carrier flight-propulsion-missile coupling fault-tolerant control method. The purpose of the present application is to realize stable tracking control of the reusable carrier. The method comprises the following steps: constructing a longitudinal dynamics model of the reusable carrier; constructing a longitudinal dynamics simplified model of the reusable carrier flight-propulsion-missile; designing a sliding mode controller of the reusable carrier; and designing a RBF neural network of the reusable carrier. Through the powerful nonlinear approximation capability of the RBF neural network, the sliding mode fault-tolerant control is designed, the sensor and actuator fault-tolerant compensation is considered, and the stable tracking control of the reusable carrier is realized. The method is a reusable carrier flight-propulsion-missile coupling fault-tolerant control method, and has a wide application prospect.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to a flight-push-missile coupling fault-tolerant control method for a reusable vehicle. The invention aims to realize stable tracking control of a reusable vehicle. The method comprises the following steps: constructing a reusable vehicle longitudinal dynamic model; the invention relates to a vertical dynamics simplified model of a flying bomb of a reusable launch vehicle. Designing a reusable vehicle sliding mode controller; the invention relates to a vehicle RBF neural network design capable of being repeatedly used. The sliding-mode fault-tolerant control is designed through the powerful nonlinear approximation capability of the RBF neural network, and the stable tracking control of the reusable vehicle is realized by considering the fault-tolerant compensation of the sensor and the actuator. The method is a coupling fault-tolerant control method for the flying-pushing missile of the reusable launch vehicle, and is wide in application prospect.