133 results about "Linear quadratic" patented technology

The invention discloses a wind power unmanned ship path tracking control method based on active disturbance rejection linear quadratic regulation control. The method comprises the following steps: building kinematics and dynamics models based on a ship body physical structure and a system parameter identification method; according to the method, a linear quadratic regulator (LQR) is introduced into an active disturbance rejection control framework, and the strong disturbance compensation capability of the ADRC and the control accuracy of the LQR are combined; an unmanned ship guidance system is constructed based on the model, a dynamic curvature navigation control (DCNC) algorithm is adopted to dynamically adjust the navigational speed, and path tracking is ensured by controlling the course angle. The method for calculating the weight proportion of the LQR coefficient in real time overcomes the system instability caused by environment change in actual engineering application to a certain extent; the ADRC control strategy adopted by the invention effectively improves the robustness and optimality of the system.

The invention provides a method and device for controlling the liquid level of molten glass in a kiln, a terminal and a medium. The method comprises the following steps: acquiring structural parameters and thermal parameters of the kiln; based on the structural parameters and the thermal parameters, constructing a dynamic mathematical model for describing the liquid level change of the molten glass; the target function of the dynamic mathematical model is used for measuring the deviation between the actual liquid level height and the ideal liquid level height of the molten glass in the kiln; calculating an optimal control law that minimizes an objective function of the dynamic mathematical model based on a linear quadratic regulator algorithm; and adjusting the liquid level height of the molten glass in the kiln based on the optimal control law. According to the method, the dynamic mathematical model used for describing the liquid level change of the molten glass is constructed, the deviation minimization between the actual liquid level height and the ideal liquid level height of the molten glass in the kiln serves as the target, the optimal control strategy is determined, and high-precision stable control over the liquid level height of the molten glass in the kiln is achieved.

The invention discloses a hydrogen production electrolytic cell dynamic performance control method based on an improved particle swarm optimization algorithm, which comprises the following steps of: extracting index parameters of an electrolytic cell, constructing a mathematical model which comprises operating voltage, hydrogen production rate and hydrogen production efficiency, and calculating the dynamic performance of the electrolytic cell according to the operating voltage, the hydrogen production rate and the hydrogen production efficiency. The operating voltage, the hydrogen production rate and the hydrogen production efficiency of the mathematical model are calculated to obtain multi-target optimization parameters for constraining the operating temperature of the electrolytic cell, the weight of existing linear quadratic programming control is optimized through a self-adaptive weight multi-target particle swarm algorithm, the dynamic temperature model of the electrolytic cell is constructed, and the optimal temperature of the electrolytic cell is obtained. And controlling the operating temperature of the electrolytic cell to be within a constrained electrolytic cell operating temperature range through the electrolytic cell temperature dynamic model. According to the method, the weight of linear quadratic programming control is optimized through the self-adaptive weight multi-target particle swarm algorithm, and the electrolytic bath temperature dynamic model is constructed, so that stable and rapid feedback control can be performed on the operating temperature of the electrolytic bath.

The invention relates to the technical field of intelligent traffic and vehicle control, in particular to an intelligent networked commercial vehicle fleet collaborative lane changing system and an optimization method, and provides a control strategy for intelligent networked commercial vehicle fleet collaborative lane changing by combining a V2X technology with a distributed model predictive control (DMPC) algorithm. The method comprises the following steps: introducing a timing interval strategy in a V2X environment to realize real-time information interaction so as to dynamically adjust a vehicle interval, predicting a motorcade lane changing track by using a DMPC algorithm, and converting a track optimization problem into a linear quadratic programming solving problem through a quadratic programming method so as to obtain a motorcade lane changing track with an optimal control sequence, so that the aim of dynamically adjusting the vehicle interval is fulfilled. Therefore, cooperative control over lane changing of the motorcade is achieved, and the overall stability, safety and lane changing efficiency of the motorcade are improved.

The invention relates to the technical field of friction welding machines, and discloses a friction welding machine sliding table control method and device.The method comprises the following steps that movement data of a friction welding machine stirring head are collected in real time, and a stirring head kinetic model is established; based on the mobile data and the dynamic model, outputting a preliminary control strategy through an optimal linear quadratic regulation control algorithm; on the basis of the movement data, future movement data of the stirring head are predicted through a prediction control algorithm, and a preliminary control strategy is optimized; a control signal is sent to a servo motor of the sliding table to control the servo motor; and the difference between the real-time movement data of the stirring head and the prediction result is used as feedback data, and control strategy parameters are adjusted. According to the method, the difference between the real-time movement data of the stirring head and the prediction result is used as feedback, a closed-loop control mechanism is formed, and control parameters are continuously monitored and optimized. The position control precision of the stirring head is improved, the problem of response lag in high-speed welding is effectively solved, the consistency of welding line tracks is ensured, and position deviation is avoided.

The invention relates to the field of new energy power generation control, in particular to a new energy power generation grid-connected protocol conversion control method. According to the method, the voltage, frequency and communication state of a grid-connected point are monitored in real time, and a support vector machine model is combined to judge whether a system is in a grid loss state; after network loss, the controller is switched to an isolated network operation protocol, a collaborative inertia regulation and control strategy is executed, output of each power generation unit and the energy storage system is dynamically distributed, and frequency and voltage stabilization is achieved; further adjusting the output of each new energy unit based on a linear quadratic Gaussian control algorithm to realize active power balance under the isolated network; after the power supply of the power grid is recovered, the frequency, voltage and phase synchronization of the power generation system and the power grid is realized by adopting self-adaptive fuzzy control through a set synchronization threshold judgment condition; and finally, combining a soft switching-on technology to realize safe grid connection and switching to a grid connection control protocol. The method has the advantages of quick response, high control precision, stable grid connection process and the like, and is suitable for various new energy microgrid scenes.

The intelligent strip steel plate shape regulation and control method based on the self-adaptive fusion model comprises the steps that 1, historical rolling production process data are collected and preprocessed; 2, constructing a rolling production process data set based on the preprocessed data; 3, plate shape quality classification is conducted according to the ratio of the strip steel convexity to the target thickness, and category labels are set; 4, establishing a self-adaptive strip steel outlet strip shape diagnosis model containing a plurality of classifiers based on the DS theory, and training the diagnosis model through the rolling production process data set; 5, inputting rolling production process data under a new rolling schedule into a classifier of the trained adaptive strip steel outlet strip shape diagnosis model to obtain a real-time classification prediction result of the strip steel, and if the prediction classification is under-convexity or over-convexity, executing the step 6; otherwise, the process parameters are not adjusted; and 6, according to a prediction result in the step 5, controlling ILQ to dynamically adjust and optimize process parameters based on an inverse linear quadratic form.

The embodiment of the invention discloses a tractor farm tool unit control method and device, electronic equipment and a storage medium. The method comprises the steps that a state space expression of a control system of a tractor farm tool unit is established; the state variables of the control system comprise the tractor course deviation, the farm tool course deviation and the farm tool transverse deviation of the tractor farm tool unit relative to the target operation path, and the control variable of the control system is the front wheel rotation angle of the tractor; acquiring a state feedback controller of the control system by adopting a linear quadratic regulator based on the state space expression; based on the real-time tractor course deviation, the real-time farm tool course deviation and the real-time farm tool transverse deviation, a real-time target front wheel rotation angle is determined through a state feedback controller; and performing real-time regulation and control on the tractor front wheel rotation angle of the tractor farm tool unit based on the real-time target front wheel rotation angle. According to the embodiment of the invention, the farm tool can better track the target operation path.

The invention provides a trajectory planning method and device of a mechanical arm, a storage medium and a computer program product. The trajectory planning method comprises the following steps: an information acquisition step: acquiring environment information around the mechanical arm and state information of the mechanical arm; a path planning step: according to the acquired environment information and the state information of the mechanical arm, performing path planning by adopting a fast random tree extension method so as to generate a running path of the mechanical arm, and smoothing and optimizing the running path by using a gradient descent method; and track control: converting a control system of the mechanical arm from a nonlinear system into a linear system by using a feedback linearization method, optimizing the control gain of the linear system by using a linear quadratic regulation controller according to the state information and the environment information of the mechanical arm, and generating a control instruction for controlling the mechanical arm to track a running path. According to the invention, the control instruction is dynamically adjusted through real-time sensing information, so that the robot can more flexibly and accurately avoid obstacles in a dynamic environment.

The invention provides a polarization multiplexing metasurface-based depolarization holographic method, which comprises the following steps of: describing a polarization state of light by using Jones calculation, and defining a propagation model of a wave; the pixel pitch of the metasurface and the height of the nanorod are determined, and a function between the length and width (l, w) of the nanorod and the modulation phase is obtained; a strict coupling wave analysis RCWA method is used, and electromagnetic response of the nanostructure is simulated under local period approximate LPA; fitting the libraries of phi xx and phi yy through a linear quadratic polynomial at each wavelength so as to represent a Jones matrix of the metasurface; introducing a noise function fnoise, applying the noise function fnoise to the metasurface, and calculating the complex amplitude of the metasurface by using an agent model; and establishing a joint optimization process, optimizing the metasurface geometric diagram and the SLM phase diagram, generating a focal length stacking hologram of the target image data set, and performing verification by using a CITL calibration model. According to the method, the image quality can be improved.

The invention relates to the technical field of power buoys, in particular to a power buoy self-positioning control method based on an MPC algorithm and a power buoy. The method comprises the following steps: firstly, establishing a kinematic model of a buoy, converting the kinematic model into a continuous state space model, secondly, establishing a continuous time system equation according to the kinematic model, and then, discretizing the continuous state space model to obtain a continuous time system equation; discretizing a state transition matrix and an input influence matrix in the continuous state space model according to a continuous time system equation, then establishing an objective function, constraining control parameters in a kinematic model, and finally solving an optimal solution of the objective function in a constraint range by adopting a linear quadratic programming function, so as to obtain a continuous state space model. Optimal control parameters are obtained, and optimal positioning and navigation control are achieved. A positioning control system and a power propelling system are arranged, the posture and position of the buoy can be rapidly and automatically adjusted, and the adaptability and flexibility of the buoy are greatly improved.

The invention belongs to the field of flexible power distribution network optimization scheduling, and provides a flexible power distribution network voltage and power coordination control method and system.The method comprises the steps that firstly, according to a multi-port SOP control strategy and a linear power flow model, a flexible interconnection power distribution network voltage and power coordination control principle based on a multi-port SOP is analyzed; secondly, constructing a discrete time state space model of the flexible power distribution network based on a multi-port SOP, further establishing a voltage and transformer load coordination optimal control model, and adopting a linear quadratic regulator for solving; finally, simulation tests prove that the control method can quickly and effectively optimize the power flow distribution of the interconnected power distribution network, node voltage and transformer heavy load are improved, and the problems of power distribution network voltage out-of-limit and transformer heavy overload caused by high-proportion distributed power supply access are solved.

The invention provides a multi-task real-time scheduling optimization method based on dependency perception information age, and provides a brand-new optimization scheme for overcoming the defects of a traditional information age model in the aspects of processing multi-task dependency and multi-source heterogeneity. By defining a dependency perception information age model, the comprehensive influence of the freshness of task data and dependency task completion delay is comprehensively quantified, and the scheduling performance of the system in a complex industrial scene is remarkably improved. The method combines the dependency relationship of the task chain, dynamically optimizes the scheduling strategy, preferentially processes the critical path task, and adjusts the task priority and the resource allocation strategy in real time. Under a discrete time linear time-invariant system framework, a linear quadratic Gaussian control method is combined to evaluate the overall performance of a scheduling scheme. In addition, a multi-agent deep reinforcement learning framework is utilized, the task sampling frequency and the resource allocation and transmission strategy are optimized, and the robustness and overall performance of the system are further improved.

The invention provides a train virtual reconnection operation control method and system based on an interference compensation linear quadratic regulator, and belongs to the technical field of train virtual reconnection operation optimization control. A longitudinal train dynamic model of train operation basic resistance and additional resistance is established; establishing a train operation safety envelope considering train speed measurement positioning errors and communication time delay error factors, and establishing a dynamic target interval between adjacent trains during virtual reconnection operation of the trains; setting a cost function of the controller to be the weighted sum of the quadratic form of the system state variable and the quadratic form of the control quantity of the minimum infinite time domain; an auxiliary matrix is introduced to convert an optimization problem into solving of an algebraic Riccati equation, the auxiliary matrix is obtained through solving in a numerical calculation mode, the auxiliary matrix is substituted into a state feedback controller model with interference compensation, an optimized control quantity is obtained, and the optimization control process of safe operation of the train in the virtual reconnection mode is achieved. The train virtual reconnection operation optimization control method can realize train virtual reconnection operation optimization control.

The invention discloses a robust distributed game control method and system for a multi-robot system, relates to the technical field of multi-robot control, and aims to overcome the defect that a multi-robot optimization control algorithm based on Bayesian online estimation cannot ensure that a cost function of each robot realizes Nash equilibrium optimization in literatures. And kinetic model parameters of the system are required to be known. According to the technical key points, firstly, a multi-robot cluster dynamic model containing modeling uncertain parameters is constructed, and the modeling uncertain parameters in the model are decomposed based on a matrix decomposition theory; aiming at the constructed multi-robot cluster system dynamic model, designing a cluster tracking error system based on a navigation-tracking mechanism, and constructing a dynamic performance cost function of each robot based on a non-cooperative game optimization idea; aiming at the constructed cluster tracking error system and the dynamic performance cost function, based on the constructed cluster tracking error system and the dynamic performance cost function, solving a robust distributed game controller by utilizing an optimal control theory; deducing controller parameters capable of ensuring global Nash equilibrium of the dynamic performance cost function of the system by using a linear quadratic regulation control theory; the Lyapunov stability theory is utilized to prove that the designed robust distributed game controller can ensure the asymptotic stability of a cluster tracking error system.

The invention discloses an intelligent shafting vibration control system and method. The method comprises the steps that system hardware is set up, an eddy current displacement sensor is installed at a key position to monitor vibration displacement, an electromagnetic actuator is installed on a bearing seat to control bearing rigidity and damping through current, and a data acquisition and analysis system is configured to be connected with the sensor and the actuator and complete parameter setting; the method comprises the following steps: designing a vibration control algorithm, establishing a kinetic model and a noise model, solving an optimal feedback gain matrix and a state estimation value to calculate a control force, optimizing linear quadratic Gaussian (LQG) control parameters by using particle swarm optimization (PSO), defining a target, initializing, calculating fitness, updating particle parameters until conditions are met, and outputting an optimal solution; debugging and optimizing the system according to an actual operation condition, adjusting control parameters, and optimizing an algorithm and a structure to improve control performance and robustness; and finally, system testing and evaluation are carried out, performance and robustness tests such as vibration attenuation rate, control precision, response speed and stability under interference and noise are covered, and efficient and stable vibration control is realized.

The invention provides an automobile suspension pre-control method and system based on an LQR and an artificial neural network, and the method comprises the steps: obtaining the road surface undulation degree information in advance through a binocular camera, building a dynamic model of a suspension system through the combination of vehicle attitude parameters and motion parameters obtained by a vehicle-mounted sensor, and carrying out the expression through a linear space equation. Thirdly, designing an artificial neural network, and calculating a weighting coefficient matrix according to the vehicle parameters and the road surface height; and optimal suspension parameters are solved based on a linear quadratic regulator (LQR), and pre-control of the suspension is achieved. And calculating the posture of the suspension after deformation through a precise discretization method, and carrying out online supervision training on the artificial neural network. According to the method, the suspension deformation can be calculated in advance, the road impact is effectively absorbed, and the driving safety and comfort are improved. Compared with traditional real-time feedback control, the method has higher environmental adaptability and reliability.

A dynamic wireless charging system optimal control method based on accurate output regulation comprises the following steps: constructing an external system capable of generating an interference signal and a reference signal by using an accurate output regulation theory and aiming at the interference and constant reference voltage suffered by an energy receiving end Boost type DC-DC converter in an electric vehicle dynamic wireless charging system; therefore, a system control problem is converted into an output adjustment problem; and designing a state feedback controller based on the constructed external system and the mathematical model of the DC-DC converter, optimizing a state feedback gain parameter by means of a linear quadratic regulator (LQR), and finally obtaining an optimal state feedback controller. According to the method, the influence of large-range energy disturbance caused by movement of the electric vehicle on the system can be effectively restrained, optimal adjustment of system output can be achieved, and the method has practical engineering application prospects.

This application provides an automatic parking trajectory tracking control method, storage medium, and electronic device. The method includes: determining the current nearest point and shift point closest to the origin of the vehicle coordinate system from a sequence of discrete trajectory coordinate points; determining at least one preview point based on the current nearest point, and obtaining the vehicle's lateral deviation and heading angle deviation based on the preview point; establishing kinematic equations with lateral deviation and heading angle deviation as state variables, and obtaining discretized state-space equations; constructing an offline parameter table of different vehicle speeds and corresponding state feedback values ​​based on the discretized state-space equations and a preset linear quadratic regulator; obtaining the corresponding vehicle rotation value according to the real-time vehicle speed and the offline parameter table; and performing automatic parking control based on the gear information of the current nearest point and the current vehicle rotation value. This application effectively solves the technical problem of difficulty in simultaneously achieving accuracy, real-time performance, and robustness in existing automatic parking trajectory tracking control methods.

The invention discloses a rigid-flexible coupling system modeling and self-adaptive control method based on data driving, and belongs to the field of space tethered robots. The method comprises the following steps: constructing a physical simulation model of the space tethered robot, and collecting state variables and control input data of system operation to construct a training data set; on the basis of the Kupman operator theory, constructing a mapping model from a nonlinear system to a dimension raising linear system with a deep neural network as an embedded function, and training the mapping model on the basis of the training data set; designing a linear quadratic optimal controller; under a reinforcement learning framework, a strategy optimization target taking controller parameters and model parameters as optimization objects is constructed, and collaborative adaptive adjustment of the model parameters and the controller parameters is realized through iterative updating. According to the invention, the optimal control of the space tethered robot is realized.

This invention discloses a fault-tolerant and vibration suppression control method for rigid-flexible coupled robots based on synchronous disturbance observation and dynamic event triggering. The method includes: establishing a large-deformation dynamic model of the rigid-flexible coupled robot mechanism and constructing rigid coordinates, flexible modal coordinates, and control targets; establishing an actuator effectiveness decay fault model and transforming the original underactuated system into an equivalent fully actuated model oriented towards trajectory tracking; designing a fixed-time synchronous disturbance observer based on error dynamics to estimate external disturbances and composite uncertainties online; constructing a dynamic event triggering mechanism and a time-synchronous fault-tolerant sliding mode control law to achieve synchronous convergence of the system state to the sliding surface within a fixed time; introducing a hybrid reference trajectory and virtual input to establish an augmented state model and designing a robust linear quadratic state feedback controller. This invention achieves coordinated control of trajectory tracking and flexible vibration suppression under conditions of external disturbances, actuator failures, and limited communication resources.

The invention discloses a posture control method and system of a discrete gravity center double-wheel-foot robot and a storage medium, and relates to the technical field of gravity center control. Discrete configuration and rapid switching of the gravity center position are achieved by actively adjusting hip joints and knee joints, and a robot model is established based on multi-rigid-body dynamics; simplifying the model into an inverted pendulum linear model family with parameters changing along with the gravity center in the front-back direction; a plurality of discrete gravity center working conditions are preset, each working condition corresponds to a specific joint target angle and model parameters, and the optimal working condition is selected for switching according to the real-time posture and the motion state; and designing a cooperative control law by adopting a linear quadratic regulator, and finally, realizing stable attitude control of the robot under complex terrains and disturbances through multi-joint cooperative execution. According to the method, the disturbance rejection capability and the attitude stability margin are remarkably improved through discrete gravity center configuration, meanwhile, the model complexity and the real-time calculation burden of continuous gravity center adjustment are avoided, and control performance and engineering realizability are achieved at the same time.

The invention discloses a method for optimizing the thickness of an electrolyte and a cathode and improving the energy efficiency of SOECs. The method specifically comprises the steps of electrochemical modeling of SOEC considering current leakage, prediction of working potential needed by the SOEC during operation, building of an electrolytic cell energy efficiency model, building of a residual stress model based on the SOEC and building of a thermal-electric-chemical-mechanical coupling stress model. Two optimization problems are formulated by considering the volatility of renewable energy sources, the optimization problems are combined with a nonlinear quadratic programming SQP algorithm and a particle swarm optimization PSO algorithm to be converted into a wanted standard optimization problem, the optimal electrolyte thickness and cathode thickness are obtained, the optimal electrolytic cell energy efficiency is obtained, and the optimal energy efficiency of the SOEC is achieved. According to the method, hydrogen production and energy efficiency improvement can be reasonably balanced, stress safety is guaranteed, and valuable guidance is provided for design and development of SOEC.

The application provides a privacy protection method of a linear quadratic control system based on model disturbance, and comprises the following steps: intentionally informing a cloud of an incorrect system model according to the problem setting that system knowledge between the cloud and a client is asymmetric; introducing a system observation matrix error, and widening the coverage of the privacy protection method design under the condition that the system knowledge between the cloud and the client is asymmetric according to the existing state estimation inherent error boundary and the existing privacy protection method; the client sends processed data to the cloud instead of the original information sequence of the local system; and according to a linear quadratic Gaussian problem, the influence of the collaborative design of the linear transformation and the model parameter disturbance on the privacy protection and control performance trade-off is obtained.

This invention provides a control method for a lower limb exoskeleton rehabilitation robot based on the sliding mode optimization method, belonging to the field of medical rehabilitation robot control technology. The method includes: establishing a two-degree-of-freedom dynamic model of the lower limb exoskeleton rehabilitation robot, and transforming it into a linear state-space model using feedback linearization technology; designing a linear quadratic regulator (LQR) for the linearized model to obtain the optimal feedback gain matrix; constructing an integral sliding surface containing the optimal feedback gain matrix, so that the equivalent control law of the system in the sliding mode possesses the globally optimal dynamic performance of the LQR; and based on adaptive preset time theory, constructing a sliding mode reaching law and an adaptive gain update law containing novel nonlinear functions to generate the final control torque.

The invention discloses an ultra-low orbit satellite electric propeller two-stage cascade power overall planning control method, and relates to the technical field of satellite electric propellers, and the method comprises the steps: constructing a thrust-radio frequency power matching model; utilizing an extended Kalman filtering algorithm to carry out radio frequency source power dynamic estimation technology research, and constructing a state observer; based on a thrust-radio frequency power matching model and a state observer, constructing a self-adaptive predictive power control framework based on measured data and dynamic state estimation; determining a power coupling mechanism between the spiral wave radio frequency source and the ion cyclotron resonance heating radio frequency source, and constructing a front-stage and back-stage power linkage relation mathematical model; based on a front-stage and back-stage power linkage mathematical model, a dynamic power optimal distribution method with optimal efficiency, thrust and specific impulse under power constraint is established, and a feedback controller based on delay compensation linear quadratic Gaussian control is constructed. And the performance of the ultra-low orbit satellite electric propeller is optimized.

The present invention provides a train virtual reconnection operation control method and system based on an interference-compensated linear quadratic regulator, belonging to the technical field of train virtual reconnection operation optimization control. The method and system establish a longitudinal train dynamics model of the basic resistance and additional resistance of the train operation; establish a train operation safety envelope that takes into account the factors of train speed measurement positioning error and communication delay error, and establish a dynamic target interval between adjacent trains during the train virtual reconnection operation; set the controller's cost function to minimize the weighted sum of the quadratic form of the system state variable and the quadratic form of the control quantity in the infinite time domain; introduce an auxiliary matrix to transform the optimization problem into solving the algebraic Riccati equation, solve the auxiliary matrix by numerical calculation, bring it into a state feedback controller model with interference compensation, obtain the optimized control quantity, and realize the optimization control process of safe operation in the train virtual reconnection mode. The present invention can realize the optimization control of train virtual reconnection operation.

The invention discloses a data processing method and device for a vehicle active suspension, terminal equipment and a storage medium, and belongs to the field of suspension control, and the method comprises the steps: obtaining driving related data and initial grey wolf optimization algorithm parameters of a to-be-controlled vehicle; constructing a random pavement model, an active suspension model and a braking system model of the to-be-controlled vehicle according to the driving related data; determining a state space matrix according to the active suspension model; determining an initial grey wolf set based on the initial grey wolf optimization algorithm parameters, taking the initial grey wolf set as input of a first grey wolf optimization algorithm, repeatedly executing the grey wolf optimization algorithm, and outputting optimal grey wolf optimization algorithm parameters; and determining an optimal linear quadratic regulator based on the optimal grey wolf optimization algorithm parameter, and controlling the active suspension of the to-be-controlled vehicle, so that the problem of poor riding experience feeling caused by the fact that the active suspension is not matched with the driving condition in the prior art can be solved.

The present application belongs to the field of aircraft flight control, and particularly relates to a kind of finite time flight height control method of integrated flap direct force control.The finite time flight height control method of integrated flap direct force control according to height control reference trajectory, establishes the linear dynamics model of the longitudinal channel of the controlled aircraft;According to the flight control target, a finite time domain linear quadratic optimal control problem is established, and the corresponding two-point boundary value problem is derived;Using finite-time iteration algorithm, the optimal flight state and control solution of flight height control are obtained;The optimal solution is applied to height control.The finite time flight height control method of integrated flap direct force control uses finite-time iteration to obtain optimal control instructions, and through parallel computing, efficient solution of height and heading position control instructions is realized, which has engineering practical value.

The application discloses a kind of unmanned aerial vehicle flight control methods based on adaptive quasi-optimal high-order sliding mode control, including obtaining the first pose vector of unmanned aerial vehicle to be controlled;The first pose vector is used as the input of preset pose control model, and the control signal output by the preset pose control model is obtained according to the preset integral sliding surface of the preset pose control model and preset control rate, wherein the preset pose control model is the preset second-order integral chain model with pose as state variable, and the preset control rate makes the state variable of the preset second-order integral chain model reach the preset equilibrium state, and the preset linear quadratic index is minimum;The unmanned aerial vehicle to be controlled is controlled according to the control signal.The method of the application can guarantee that the tracking error of closed-loop system converges in finite time.