330 results about "Nonlinear control" patented technology

The embodiment of the invention provides a filling production line control method and system based on a virtual reality technology. According to the embodiment of the invention, the method comprises the steps: synchronously collecting a real-time dynamic parameter set of a fluid medium, constructing a dynamic coupling factor, generating a dynamic response coefficient matrix through a fluid coupling identification model, generating a nonlinear control parameter combination according to the dynamic response coefficient matrix, and inputting the nonlinear control parameter combination into a motion trail planner. Generating an optimized motion track instruction, driving an execution mechanism to execute filling operation according to the optimized motion track instruction through a servo controller, monitoring the deviation degree in real time, reconstructing the fluid coupling identification model when the deviation degree exceeds a preset threshold value, and updating a dynamic coupling factor; the nonlinear control parameter combination is corrected through the updated dynamic coupling factor, a closed-loop interlocking control mechanism is generated, and the technical scheme provided by the embodiment of the invention not only improves the intelligent level of a filling production line, but also improves the precision and stability in the filling process.

The invention discloses a wind power operation and maintenance ship embarkation system compensation control method based on active disturbance rejection control and related equipment. The method comprises the following steps: predicting a system external wave disturbance variable of a six-degree-of-freedom embarkation compensation platform according to environmental parameters through a wave disturbance prediction model; the total disturbance quantity in the system is predicted through the extended state observer according to the real-time attitude information and the electric cylinder control signal, and then the total disturbance of the system is obtained; a nonlinear control quantity is calculated through an active disturbance rejection controller according to real-time attitude information, expected attitude information and a preset decoupling matrix, a feed-forward compensation quantity is calculated according to total disturbance of a system, then an output instruction of the controller is calculated, and compensation control is achieved. According to the embodiment of the invention, compensation control can be realized through the wave interference prediction model, the state expansion observer and the active disturbance rejection controller, and high precision and response speed are achieved. The method can be widely applied to the technical field of automation control.

The invention provides an active-disturbance-rejection sliding mode compound control system of a wrist rehabilitation robot, and relates to the technical field of rehabilitation robot control, the wrist rehabilitation robot adopts a folding air bag as a driving element, and the control system comprises a tracking differentiator used for tracking and differentiating an input target angle signal; the extended state observer is used for estimating the system state and the total disturbance in real time; the sliding mode controller is used for designing a nonlinear control rate; the dead-zone nonlinear inverse function control module is used for compensating the nonlinear control rate of the sliding mode controller and eliminating dead-zone nonlinearity of the high-speed switch valve; and the self-adaptive combination valve comprises two high-speed switch valves, a control unit and a pressure sensor and is used for executing the control instruction and monitoring the pressure in the folding air bag. The problems of compressibility of air in the air bag, dead zone nonlinearity of a gas control element, nonlinearity of gas flow and the like can be solved, and high-precision trajectory tracking control over the wrist rehabilitation robot is achieved.

The invention relates to the technical field of data processing, in particular to an activation feeder outlet baffle adjusting device and method, and the device comprises a data collection module, a parameter optimization module, a control decision module and an execution module. The data acquisition module acquires load data of a conveying belt and material state data of a discharge port in real time; the parameter optimization module adopts a genetic algorithm to dynamically optimize first-order control parameters and second-order control parameters, the control decision module triggers an adjustment strategy based on a load deviation step by step, preferentially adjusts the width of a discharge port through the angle adjustment amount of an outlet baffle, and if the deviation is continuous, a vibration amplitude adjustment instruction is generated in combination with material state data; the execution module converts the adjusting quantity into displacement and vibration driving signals. Through hierarchical cooperative control and a closed-loop feedback mechanism, the problem of nonlinear control instability caused by dynamic changes of material humidity, granularity and viscosity is solved, the output matching precision under the multivariable coupling working condition is improved, and the material viscosity and the secondary bridging risk are reduced.

The invention discloses an oil reservoir polymer flooding process oil-water distribution simulation method and device based on a cellular automaton, equipment, a medium and a product, and relates to the field of oil reservoir engineering and seepage mechanics. The method comprises the steps that rectangular grid division is conducted on an oil reservoir space, three-dimensional coordinates of cellular nodes are determined, and a cellular automaton model is constructed; according to the polymer oil displacement scheme parameters, oil reservoir state parameters at the end moment of water displacement are initialized, and boundary conditions of the mole-type cellular automaton model are established; making an evolution rule for each neighborhood cellular node in the Moire cellular automaton model; according to a numerical value stability rule, adjusting parameters of each neighborhood cellular node in the Mohr-type cellular automaton model; according to the method, a relative permeability nonlinear regulation and control rule is formulated for the Mohr-type cellular automaton model to simulate oil-water distribution in the oil reservoir polymer flooding process of the Mohr-type cellular automaton model, the starting pressure gradient is accurately simulated, and the low-permeability oil reservoir evolution process is accurately described.

The invention discloses a high-precision temperature control method and system for a photoetching machine, and belongs to the technical field of photoetching machine temperature control. The method comprises the following steps: acquiring a temperature array on the lower surface of a mask, heat flux density distribution of a bearing surface of an exposure table and a hot pressure difference signal of an air floating layer, carrying out feature coding, and constructing a high-dimensional thermal coupling situation feature matrix; a dynamic thermal weight correlation map is generated based on the matrix, exposure key nodes serve as anchor points, and explicit modeling is conducted on nonlinear thermal paths among the nodes through a graph neural network; further calculating the spatial gradient change rate of the heat flux per unit area of the mask region according to the thermal weight map, and mapping the spatial gradient change rate to an exposure table temperature control array to generate a nonlinear control function curved surface; and dynamically adjusting power output and response delay parameters of the local heating unit according to the control function, so that dynamic thermal field equalization with nanoscale precision can be realized, thermal drift is effectively inhibited, and the photoetching imaging quality and the yield of an advanced process are improved.

The invention relates to a multi-core implementation method for a nonlinear MPC controller of a fuel cell hydrogen system, and the method comprises the steps: respectively distributing scalar operation and matrix operation involved in a gradient descent method to a CPU and an NPU for processing in a process of solving an optimal control variable according to a target function, and solving the optimal control variable through the target function; the main calculation process comprises the steps of Hessian matrix calculation, target function gradient calculation, linear equation solving, convergence judgment and system state updating. In the calculation, the CPU mainly relates to operations such as integration of output results, condition judgment and the like, and the NPU mainly processes large-scale matrix operation and decomposition tasks. According to the method, when the calculation task of the two cores is completed, data transmission is carried out on the calculation result through a high-speed communication bus between the cores, the real-time performance of data transmission is ensured and delay is avoided through IPC synchronous communication, the purpose of minimizing the total calculation time and resource occupation is finally achieved, and the problem that MPC solving needs much time and resource occupation is effectively solved.

The invention provides a steering-by-wire vehicle variable transmission ratio design method and system based on a fuzzy neural network, and belongs to the field of vehicle steering-by-wire. According to a fixed gain method, the steering angle transmission ratio is too small at a low speed, and the sensitivity is low at a high speed; the existing intelligent algorithm excessively depends on the experience of a designer and the quality of a data sample. The method comprises the following steps: establishing a closed-loop driver-vehicle system, designing a multi-target evaluation method by using a quadratic cost function of a vehicle dynamic state, and obtaining a data relationship between a vehicle ideal variable transmission ratio characteristic and a vehicle longitudinal speed and a steering wheel angle; a nonlinear control model is established through a fuzzy RBF network, and a globally optimal solution is obtained based on nonlinear model learning. The robustness of the control system is improved, the stable and reliable transmission ratio can be provided under various vehicle conditions, and the problem that a steering system is light and flexible is solved, so that the experience feeling of a driver under various vehicle conditions is improved, and meanwhile the operation stability and safety of the vehicle are improved.

The invention belongs to the technical field of energy storage systems, and provides a multi-scale energy storage system control method and system, and the technical scheme is that a centralized access mode is adopted, an energy storage device is connected to a wind power grid-connected bus, damping control is adopted, a power oscillation damper is additionally arranged, and damping is increased by controlling the output of a DFIG; mode damping, filtering and phase compensation are improved through a POD control structure installed on the machine side; low-frequency oscillation is suppressed through energy storage, positive damping is provided for frequency oscillation of the system, when low-frequency oscillation occurs in the frequency of the system, the energy storage dynamically adjusts the active output according to the frequency change of the system, the control modes mainly include droop control, inertia control and nonlinear control, power compensation and peak load shifting are achieved, the energy storage utilization rate is high, and the energy storage efficiency is high. Damping control is adopted for a multi-scale energy storage system and a new energy system, a power oscillation damper is additionally arranged, and the small disturbance stability of a high-permeability regional power grid is enhanced.

The embodiment of the invention provides a modeling method and system for aerodynamic efficiency of a nonlinear control surface of a high-speed aircraft, and the method comprises the steps: carrying out the numerical simulation of aerodynamic data of the high-speed aircraft at different control surface deflection angles through employing a computational fluid dynamics (CFD) method, building a control surface aerodynamic efficiency data set in a high-speed flight scene, comprising a sample set and a verification set; taking the sample set as an input variable, and constructing a high-speed aircraft nonlinear control surface aerodynamic efficiency prediction model; and performing verification and error evaluation on the constructed nonlinear control surface efficiency aerodynamic model of the high-speed aircraft by adopting the verification set, and updating model parameters to obtain a final control surface aerodynamic efficiency model. According to the method, the aerodynamic efficiency of the rapid nonlinear control surface of the high-speed aircraft can be rapidly predicted, and a rapid control surface aerodynamic efficiency prediction means is provided for design of a control system in a high-speed flight scene.

The invention relates to a mechanism information analysis method of a nonlinear control system, which combines a space-time diagram convolutional network with mechanism information modeling, and learns a control equation of the system from observation data with noise. Firstly, causal graph coding based on transfer entropy is designed to obtain an adjacent matrix with causal significance between variables. Then, a space-time diagram convolutional network is used to obtain an approximate solution of a system variable. On this basis, automatic differentiation is applied to obtain a basic derivative, a dictionary of candidate algebraic terms is formed, and sparse regression is used to obtain a coefficient matrix to determine an explicit formula of the control equation. The invention also designs a new cross combination optimization strategy to learn heterogeneous parameters including neural network parameters and control equation coefficients. According to the method, the advantages of causal representation, deep learning, automatic differentiation and sparse regression are fused aiming at the limitation of numerical differentiation and redundancy calculation of an existing control equation learning method, the robustness and universality are high, and theoretical and practical significance is achieved for mechanism information discovery of a nonlinear dynamic system.

The invention discloses an unstructured environment-oriented wheel-legged robot motion control method, system and device, a storage medium and computer equipment, and the control method comprises the steps: constructing a wheel-legged robot simulation model, building a learning training scene of a wheel-legged robot, and carrying out the training through a reinforcement learning algorithm, so as to obtain a nonlinear controller model; converting the nonlinear controller model into a nonlinear controller which can be deployed and operated on an actual physical prototype platform; acquiring current motion state data of the wheel-legged robot, and inputting the current motion state data into a nonlinear controller to obtain an expected joint position and an expected wheel speed of the wheel-legged robot; and the expected joint position and the expected wheel speed are input into a low-level controller, and a joint torque used for controlling the wheel-legged robot to execute a control task is output. According to the method, the development period of the wheel-legged robot controller is greatly shortened, and the robustness, the dynamics and the flexibility of the wheel-legged robot moving in the unstructured environment are also improved.

The invention discloses a control method for ultra-low-altitude flight of a large unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model of the unmanned aerial vehicle, the kinetic model comprising dynamic equations of position, speed, attitude control and control input; performing error tracking control on the attitude and the speed of the unmanned aerial vehicle by adopting a Backstepping nonlinear control method based on the dynamic model; estimating external disturbance and dynamic parameters of the unmanned aerial vehicle in real time by combining an L1 adaptive control method, and adaptively adjusting control input to compensate wind speed change; predicting a future flight state by using a model prediction control algorithm, optimizing a flight path and compensating wind speed disturbance; a safe trajectory is planned in real time through a dynamic window method, and obstacles are avoided in combination with dynamic constraints. According to the invention, through organic combination of Backstepping control, L1 adaptive control, MPC and DWA algorithms, the complex problems of wind speed disturbance, obstacle avoidance, high-precision operation and the like in ultra-low-altitude flight can be solved.

The invention provides a magnetic suspension underwater robot path following control method, a magnetic suspension underwater robot path following control device, magnetic suspension underwater robot path following control equipment and a medium, and relates to the field of underwater robot path following control. The motion control bottleneck of a traditional underwater robot in a complex environment is broken through. In the aspect of dynamic modeling, interaction between magnetic force and fluid resistance is considered, and underwater motion characteristics of the robot are accurately described. A nonlinear control strategy combining the Lyapunov stability theory and sliding mode control is adopted, and a trajectory tracking controller with high robustness is designed to ensure that the robot can stably and efficiently operate along a predetermined trajectory in a complex underwater environment. The maneuverability and the operation precision of the underwater robot are remarkably improved, a more reliable solution is provided for the fields of ocean exploration, resource development, environment monitoring and the like, and huge application potential and market value are shown.

The invention discloses a high-speed stable flight control method for a long-endurance variant unmanned aerial vehicle, and relates to the field of unmanned aerial vehicle control. According to the method, a longitudinal nonlinear kinetic equation after deformation of the variant unmanned aerial vehicle is firstly established, then the longitudinal nonlinear kinetic equation is represented in a nonlinear system form, and then speed and attitude decoupling is performed on a nonlinear control model of the variant unmanned aerial vehicle represented in the nonlinear system form based on a superhelix algorithm. And controlling the deformed variant unmanned aerial vehicle by using the speed control sub-model and the attitude control sub-model obtained by decoupling. According to the method, the aerodynamic performance change after the deformation of the variant unmanned aerial vehicle is accurately described by establishing the longitudinal nonlinear kinetic equation, and the high-speed stable control problem after the deformation of the variant unmanned aerial vehicle is solved through the speed control sub-model and the attitude control sub-model based on the superhelix algorithm; according to the invention, high-speed stable control after deformation of the variant unmanned aerial vehicle is realized.

The invention discloses a single-connecting-rod mechanical arm dynamic memory event triggering fixed time control method and system, and the method comprises the steps: describing a controlled system as a nonlinear system with state time lag and input time lag through modeling; introducing coordinate transformation based on a nonlinear system, and defining a tracking error; designing a virtual control law and an adaptive updating law through the defined tracking error; the stability of the control system is ensured by providing a dynamic memory event triggering mechanism, the system comprises a sensor module, a communication module, a control system and a man-machine interaction module, and the dynamic memory event triggering control mechanism in the method is used as a novel nonlinear control strategy, so that network resources and data transmission can be saved, and the system reliability is improved. The designed fixed time control strategy enables the system to achieve stable control within strictly specified time by accurately presetting the convergence time boundary, the response speed is remarkably improved, and the method is particularly suitable for industrial automation scenes with strict requirements for time precision.

The invention relates to flight control of a tilt rotorcraft, in particular to a nonlinear super-spiral robust control method for the tilt rotorcraft, and aims to realize nonlinear control of the tilt rotorcraft on the basis of not simplifying or linearizing a dynamical model and quickly track expected speed and attitude instructions, and the technical scheme adopted by the invention is that the nonlinear super-spiral robust control method for the tilt rotorcraft comprises a nonlinear super-spiral robust control method for the tilt rotorcraft. Establishing a tilting rotorcraft dynamic model; and designing a super-spiral attitude control law based on the rotation matrix to obtain a control moment of the aircraft, then carrying out real-time approximation on an inverse model of the dynamic model by using an approximation characteristic of an RBF neural network, and calculating an actual control quantity of the aircraft by using the control moment so as to realize automatic control of the tilt rotorcraft. The method is mainly applied to design, manufacturing and flight control occasions of tilt rotorcrafts.

The invention discloses a cross-domain motion planning method and system for an air-ground amphibious robot, and relates to the technical field of robot control. The method comprises the following steps: acquiring operation constraints of the air-ground amphibious robot in a working space; performing path planning according to the operation constraint by using a particle swarm optimization algorithm; constructing a kinetic model, and constructing a wing folding angle smooth switching track by using a Gaussian pseudo-spectrum method and taking the minimum flight height decrease in the modal switching process as a target; and setting a nonlinear controller according to the kinetic model and a fuzzy algorithm, controlling the air-ground amphibious robot by using the nonlinear controller according to the optimal air-ground cross-domain path, and in addition, performing smooth switching in the flight process according to the wing folding angle smooth switching track. According to the method, an adaptive fuzzy position controller is designed to process nonlinear uncertainty in a dynamic model, and cross-domain motion planning is performed based on an energy optimal path and smooth mode switching control, so that the air-ground amphibious robot can realize stable motion and reliable operation in a cross-domain manner.

The invention discloses an ultra-supercritical unit rapid dynamic response method based on multiple models, and relates to the technical field of thermal power generation. According to the method, a nonlinear control method which unifies internal model control and generalized predictive control is provided, and nonlinear control over an ultra-supercritical unit (USC) system is effectively achieved: firstly, through internal model control based on LSTM-Transform, the USC system achieves rapid convergence to be close to an expected value, and the response speed of a load is remarkably increased; secondly, the model is further optimized by enhancing a composite weighted human learning optimization network (CWHLO), and the accuracy of the model is improved; and finally, a generalized predictive control (CWHLO-GPC) algorithm based on an enhanced composite weighted human learning optimization network is adopted to control the USC system, so that high-precision load tracking performance is realized, and the problem of slow dynamic response of the ultra-supercritical unit system is solved.

The invention belongs to the field of power electronic control, and relates to a parallel pulse power decoupling system control device and method, and the device comprises a second-order low-pass filter, a sampling module, an outer-loop nonlinear ADRC controller, an inner-loop nonlinear ADRC controller, a current feedforward module, a comparator, and a dual-inductor bidirectional Buck / Boost converter. All the devices are connected in sequence to form the parallel pulse power decoupling system control device; according to the invention, the dual-inductor bidirectional Buck / Boost converter is adopted as a pulse power compensation unit and is connected in parallel with the output port of the pre-stage power supply system, so that the transient process of inductor current commutation can be eliminated, and the bus current peak generated during pulse load state switching can be effectively reduced.

The invention relates to an ultra-wideband high-performance radio frequency power amplifier based on gate source nonlinearity. One end of an SMA input connecting line is connected with a radio frequency signal source; the other end of the SMA input connecting line is connected with the input end of the input matching circuit; the output end of the input matching circuit is connected with the input end of the gate-source nonlinear control circuit; the output end of the gate-source nonlinear control circuit is connected with the gate of the power amplifier tube; the gate bias circuit is connected in parallel between the output end of the input matching circuit and the input end of the gate-source nonlinear control circuit; the drain electrode of the power amplifier tube is connected with the input end of the tuning circuit; the output end of the tuning circuit is connected with the input end of the output fundamental wave and harmonic wave matching circuit; the drain biasing circuit is connected in parallel between the output end of the tuning circuit and the input end of the output fundamental wave and harmonic wave matching circuit; the output end of the output fundamental wave and harmonic wave matching circuit is connected with one end of the SMA output connecting line; and the other end of the SMA output connecting line is a radio frequency signal output end. According to the invention, the ultra-wideband / multi-octave high-performance power amplifier is designed.

The invention discloses a model-free adaptive control method based on UKF pseudo partial derivative estimation, and belongs to the technical field of automatic control. The method mainly solves the problems that an accurate mathematical model is difficult to establish in a nonlinear control system in the actual industry, and various uncertainties, complex unknown disturbances and the like influence the control accuracy. The control scheme comprises the following steps: designing a model-free adaptive controller based on input and output data; designing an unscented Kalman filter (UKF) to estimate a pseudo partial derivative; establishing a model-free adaptive control scheme based on UKF pseudo partial derivative estimation; according to the model-free adaptive control method based on UKF pseudo partial derivative estimation disclosed by the invention, better control precision and stronger anti-interference capability can be realized in an industrial nonlinear process under the influence of unknown disturbance, and the control precision requirement of industrial production is met.

Aspects herein describe new methods of determining optimal actions to achieve high-level objectives based on an optimized chosen statistic. At least one high-level objective, along with various observational data about the world, is identified by a computational unit. The computational unit determines, through a particle method, an optimal course of action. The particle method is doubly-exponentially accelerated based on one or more acceleration methods. The doubly-exponentially accelerated particle method comprises alternating backward and forward sweeps of a coupled induction loop to optimize a selection policy and test for convergence to determine said optimal course of action. In one embodiment a user inputs a high-level objective into a cell phone which senses observational data. The cell phone communicates with a server that provides instructions. The server determines an optimal course of action via the doubly-exponentially accelerated particle method, and the cell phone then displays the instructions to the user.

The invention relates to the technical field of unmanned aerial vehicles, and discloses a UWB-based finite time unmanned aerial vehicle cooperative control method, which comprises the following steps: acquiring relative position data of an unmanned aerial vehicle; designing a virtual piloting target, and calculating the relative position expectation of each unmanned aerial vehicle at each moment; designing a finite time controller model; designing a trajectory tracking controller model; an unmanned aerial vehicle cooperative control system is divided into an outer ring and an inner ring for respective control. According to a task flight planning application data structure, it is guaranteed that a certain distance is kept during cooperation of multiple unmanned aerial vehicle formations, nonlinear control convergence of relative trajectory errors is achieved by designing a finite time controller, the unmanned aerial vehicles are controlled to be converged to be kept at the relative distance within set time, and the unmanned aerial vehicle formations are controlled to be coordinated. Therefore, formation operation in a complex scene is realized. According to the invention, the unmanned aerial vehicle can quickly reach a task target area and realize formation keeping or changing in finite time, and task development is supported.

The invention discloses a wet-type explosion-proof multifunctional dust remover and an intelligent control system thereof, the intelligent control system comprises a processor and a memory, and the processor executes the following steps: obtaining a water level monitoring device; calculating the current water level height based on the monitoring data of the water level monitoring device; judging whether the current water level height meets a warning value condition or not; calculating the current actual flow based on the monitoring data of the water flow monitoring device; calculating a comprehensive risk value: calculating a control parameter adjustment amount through a nonlinear mapping function based on the comprehensive risk value; and controlling the operation states of a water pump, a fan and a valve according to the control parameter adjustment amount. The dust remover adopts the intelligent control system. The method has the following beneficial effects that through multi-dimensional parameter monitoring, dynamic interlocking protection and a nonlinear control strategy, the problems of existing wet dust removal equipment in the aspects of explosion-proof safety, dust removal efficiency and resource utilization are solved.

The invention relates to the technical field of sliding mode control, in particular to a double-sliding-mode sensorless control method based on a PMSMS, and the method comprises the steps: setting a terminal sliding mode surface function, optimizing the rotating speed through a nonlinear control item, and introducing a variable gain coefficient; a Sigmoid function is introduced in combination with an approaching law in the design of a speed controller; the terminal sliding mode controller takes the given speed and the estimated rotating speed error of the motorized spindle as the input and takes the q-axis current as the output; carrying out stability analysis on the speed ring controller; the sliding-mode observer takes an actual current value and an observer current error as input, takes the speed and position information of an electric spindle rotor as output, improves buffeting of a sliding-mode control law in combination with a Sigmoid function, and is integrated with a phase locking ring for extracting the position and speed of the rotor; and carrying out stability judgment on the design of the sliding-mode observer. The problems of overshoot, buffeting, slow response and the like of a control system in practical application of a traditional sensorless control scheme are solved.

The invention belongs to the technical field of nonlinear position and attitude control of a side-rotor unmanned aerial vehicle, and aims to improve the strong nonlinear coupling effect, rotor slip flow interference and anti-interference capability of external unknown wind interference of the side-rotor unmanned aerial vehicle in a helicopter mode and realize high-precision position and attitude tracking control of the side-rotor unmanned aerial vehicle. Therefore, the technical scheme adopted by the invention is as follows: according to the adaptive robust control method for the side-rotor unmanned aerial vehicle, a nonlinear control strategy under a three-dimensional track of the side-rotor unmanned aerial vehicle is constructed by utilizing an adaptive backstepping method and robust integral symbol error control, and the nonlinear control strategy is divided into a position loop control strategy and an attitude loop control strategy; wherein a position loop control strategy is constructed by using an adaptive backstepping method, and an attitude loop control strategy is constructed by using robust integral symbol error control. The method is mainly applied to the manufacturing occasion of the side rotor unmanned aerial vehicle.

A real-time machine perception system and vehicles with real-time perception systems can be adapted for navigation in conflict zone environments and include an optical sensor and an optical processing component coupled to the optical sensor. The optical processing component can be configured to solve a non-linear control problem comprising solving a multi-vehicle merging or multi-vehicle synchronization problem. The optical processing component can perform optical processing on a signal from the optical sensor according to a deep learning algorithm having weights determined by solving the non-linear control problem.

The invention discloses a hydraulic system intelligent control method considering full-state performance constraints, and the method fuses a radial basis function neural network real-time learning technology, innovatively introduces full-state preset performance function transformation, and designs a nonlinear controller considering the full-state performance constraints. Aiming at the problem of position tracking control of the hydraulic system, the method can ensure that the full-state transient performance and the steady-state performance of the hydraulic system are converged to a preset performance index range, ensures safe and reliable operation of the hydraulic system, and can learn the unknown dynamic state of the system in real time by using the radial basis function neural network to realize high-precision motion control performance. And the problem of differential explosion in traditional backstepping control of the hydraulic system can be avoided, and the influence of measurement noise on control precision is reduced.

The invention discloses an unmanned ship attitude stabilization and intelligent anti-interference method and system under a complex sea condition, and relates to the technical field of unmanned ship intelligent control and marine environment perception, and the method comprises the steps: building a sea condition model, and judging the sea surface disturbance level; performing nonlinear adaptive control on the unmanned ship based on the sea surface disturbance level; and based on a state prediction algorithm, combined control is carried out on the attitude of the unmanned ship in combination with nonlinear adaptive control. According to the method disclosed by the invention, the accuracy of attitude control pre-judgment and the environment adaptability are improved; the comprehensive performance of the unmanned ship between energy efficiency utilization and dynamic control is improved; combined control is performed by combining a state prediction algorithm and nonlinear control, so that the control system has the ability of pre-judging the attitude change trend in advance and performing dynamic correction, the control strategy conversion from passive response to active pre-control is realized, and finally, the attitude stability, the control foresight and the task continuity of the unmanned ship under the complex sea condition are remarkably improved.