43 results about "Linear-quadratic regulator" patented technology

The invention discloses a world model driving data generation method based on a controllable diffusion mechanism, and the method comprises the following steps: S1, importing initial scene information, and selecting a background vehicle as a confrontation target; s2, generating a danger track of the confrontation target based on a diffusion model; s3, carrying out physical simulation verification on each generated dangerous track, evaluating the feasibility of the track by using a linear quadratic regulator (LQR) and a kinematic bicycle model, and carrying out comprehensive scoring on the track; s4, performing iterative optimization on the scored trajectory, and performing controllable variation processing by adjusting noise parameters of a diffusion model so as to generate a challenging confrontation trajectory conforming to the physical law; s5, starting a self-vehicle trajectory planning module, and generating a self-vehicle risk avoiding trajectory through a diffusion model by taking the confrontation trajectory obtained in the step S4 as input; and S6, combining the confrontation track and the self-vehicle risk avoiding track with the original scene information, inputting the combined information into a video generation model, and generating a multi-view driving scene video.

The invention discloses a flexible joint mechanical arm AISMC compensation control method under singular perturbation decomposition, and belongs to the field of flexible joint mechanical arm control, and the method comprises the following steps: S1, building a flexible joint mechanical arm dynamic model containing uncertain composite disturbance, and decoupling the model into a slow variable subsystem and a fast variable subsystem; s2, estimating lumped matching disturbance in the slow varying subsystem by using a compensation function observer; s3, constructing an integral sliding mode surface and a self-adaptive reaching law with dead zone correction for the slow-varying subsystem; designing a linear quadratic regulator for the quick-change subsystem, defining a performance index, and solving a Riccati equation to obtain an optimal control law; s4, superposing the slow-varying subsystem control law and the fast-varying subsystem control law to obtain integral control input; and S5, performing simulation verification. By adopting the flexible joint mechanical arm AISMC compensation control method under singular perturbation decomposition, trajectory tracking precision, robustness and stability are considered, and the method has a wide application prospect.

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.

The invention relates to the technical field of robot control, and discloses a robot grabbing control method and device, a robot and a storage medium, and the method comprises the steps: firstly obtaining point cloud data of a target object, and generating a plurality of candidate grabbing postures; then calculating a physical consistency index based on the point cloud data of the contact area, and reordering the candidate postures to screen out an optimal grabbing posture; then, a contact area depth image corresponding to the optimal grabbing posture, physical condition parameters of an object and a current touch observation image serve as input, and an expected touch image under the force optimal stable grabbing condition is predicted through a conditional diffusion model; and finally, according to the real-time tactile image, a clamping jaw torque value is iteratively calculated in the tactile submerged space by using a linear quadratic regulator, and the clamping jaw is controlled to realize stable grabbing with the minimum necessary force. The problem that in a traditional method, due to disjunction of attitude planning and force control, grabbing stability and efficiency are difficult to consider at the same time is effectively solved.

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 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.

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 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 embodiment of the invention provides an alternating current servo system control method, system and device based on an internal model principle and a medium, and belongs to the technical field of motor control. The method comprises the following steps: performing mechanism analysis, system identification and model order reduction processing on the alternating current servo system to obtain a four-order model corresponding to the alternating current servo system; on the basis of the fourth-order model, designing the corresponding optimal state feedback of the AC servo system under a linear quadratic regulator; determining a state observer of the inner ring object model, and determining an optimal observer parameter by using a first LQ criterion function according to the state observer; determining a first state space expression corresponding to the AC servo system according to the optimal state feedback and the optimal observer parameter; and determining a target control parameter corresponding to an optimal controller of the AC servo system according to the first state space expression, so that the AC servo system realizes optimal control according to the target control parameter.

The invention discloses a near-surface X-rudder autonomous underwater vehicle control method based on sea wave interference observation, and the method comprises the steps: collecting a measurement signal of X-rudder autonomous underwater vehicle measurement equipment, and obtaining the attitude data of an underwater vehicle; each rudder blade of the X-rudder autonomous underwater vehicle is numbered, and an underwater vehicle motion model is established; according to the state-space equation of the underwater vehicle motion model and the attitude data of the underwater vehicle, establishing a linear quadratic regulator control model to obtain a first control rate; establishing a sea wave interference observer, observing and estimating the interference of the sea current on the aircraft, establishing a nonlinear passive estimator model based on an observation result, and obtaining a second control rate; and establishing a rudder force distribution model of the underwater vehicle, obtaining an expected rudder angle of each rudder blade of the X-rudder autonomous underwater vehicle based on the first control rate and the second control rate, and realizing control of the vehicle. According to the method, the problem of tracking errors of the autonomous underwater vehicle caused by ocean current disturbance can be solved.

This invention provides an adaptive admittance method and system based on the optimal control LQR theory, including constructing an environmental dynamics model and a robotic arm admittance control model, and deriving their interactive dynamics model; defining state variables and control inputs, and establishing a standard form state-space equation; constructing a quadratic performance index function, transforming admittance parameter optimization into an LQR problem; substituting the linear quadratic regulator framework into the quadratic performance index function, transforming the LQR problem into solving the Riccati matrix differential equation, and deriving analytical expressions for virtual optimal stiffness and virtual optimal damping; within the robotic arm control sampling frequency, inputting real-time collected environmental contact forces and the actual pose of the robotic arm end effector, along with set weighting coefficients, into the analytical expressions to calculate optimal virtual stiffness and damping; and then calculating pose corrections based on the admittance control model to correct the robotic arm's motion trajectory.

The invention discloses a trajectory tracking and stability cooperative control method for a multi-axis distributed driving vehicle, and provides a hierarchical control architecture. The upper-layer trajectory tracking controller comprises a self-adaptive preview linear quadratic regulator based on fuzzy logic reasoning, and the self-adaptive preview linear quadratic regulator dynamically adjusts the preview distance according to the vehicle speed and the acceleration and outputs a steering angle instruction; and the seven-section S curve speed planner generates a smooth speed and acceleration curve. The lower-layer stability and torque distribution controller comprises an equivalent sliding mode controller for calculating an additional yaw moment based on the yaw velocity and the side slip angle error; and based on a torque distributor of quadratic programming, introducing an inter-axle yawing moment coefficient as a geometric constraint, tracking a required torque, and minimizing a tire attachment utilization rate and a slip rate as a target distribution torque. Through cooperation of the upper layer and the lower layer, high-precision trajectory tracking, high robust stability and longitudinal smoothness are considered, and the comprehensive performance of the multi-axle vehicle under the limiting working condition is remarkably improved.

The application discloses a satellite sailboard deployment joint simulation method based on MATLAB and ADAMS, first, a satellite sailboard deployment dynamics simulation system is constructed based on ADAMS software, second, a multi-board deployment simulation model of the satellite sailboard is established by using a second Lagrange method, and a dynamics model of the system is obtained; next, Runge-Kutta with four-order high precision is used to solve the dynamics differential equation by using MATLAB software, and a deployment process of the satellite sailboard and a response result of the satellite sailboard subjected to impact are given; then, ADAMS dynamics simulation results (S1) are compared with theoretical model results (S3), and the accuracy of the dynamics theoretical model is verified within a certain error; finally, an optimal control law of state linear feedback of the satellite sailboard deployment system is derived based on a linear quadratic regulator (LQR) control algorithm; the application solves the problem that the satellite sailboard cannot be quickly and stably deployed, and can provide certain theoretical guidance for the structural design and on-orbit deployment of the space satellite sailboard.

The invention relates to the field of robot balance control, and discloses a control parameter optimization method of a second-order balance robot. According to the method, for a second-order balance system formed by connecting a vehicle body and a swing rod in series, a dynamic model and a linearization discrete state space model are established, and a linear quadratic regulator control framework is constructed; taking diagonal elements of the state weight matrix as to-be-optimized parameters, mapping the to-be-optimized parameters to a logarithmic scale search space after left-right symmetry constraint dimensionality reduction processing, and performing optimization by adopting a particle swarm optimization algorithm; and a composite cost function including a core error term, a control saturation term and a control stability term is constructed based on vehicle body inclination angle response, swing rod inclination angle response, control input and closed-loop poles, and an optimal state weight matrix and a corresponding control gain are output to serve as optimal control parameters of the second-order balance robot. The second-order balance robot control parameter setting method can improve pertinence and stability of second-order balance robot control parameter setting.

This invention relates to the field of power grid inspection technology, and more particularly to a spatiotemporal dynamic path planning method for power grids integrating GNN-UTVD-CBF. This method first constructs a hybrid power grid environment model and calculates the safe time intervals for potential path edges based on equipment dynamics constraints. Then, it uses a graph neural network to predict edge expansion priorities and combines a unified temporal visibility deformation criterion to construct a dynamically connected visibility map, generating a candidate path set with multiple topological features. In path search, a safety verification mechanism based on a linear quadratic regulator and a high-order control barrier function is integrated to achieve efficient safety verification of the quadratic programming solution. Finally, a smooth trajectory is generated through spatial-temporal corridor expansion and B-spline curve optimization. When the environment changes dynamically, the system quickly adjusts the path through local pruning and incremental repair algorithms. This invention significantly improves the planning efficiency, safety, and robustness of power grid inspection equipment in complex dynamic environments.

The invention discloses a self-adaptive vibration control method, system and device applied to a wheel-legged vehicle, and relates to the technical field of wheel-legged vehicles, and the method comprises the steps: constructing a spring-damping model; determining reference data of the wheel-legged vehicle by adopting a nonlinear model predictive control method; obtaining the stiffness of the spring-damping model and the stiffness of the hip joint by adopting a self-adaptive stiffness control law; a linear quadratic regulator and an inverse kinematics and inverse dynamics method are adopted to split the reference data, and the actual knee joint torque and expected control information of each leg of the wheel-legged vehicle are obtained; based on the expected control information, the hip joint rigidity and the motion state, the actual hip joint moment and the actual wheel moment of each leg of the wheel-legged vehicle are calculated; and controlling the wheel-legged vehicle based on the actual knee joint torque, the actual hip joint torque and the actual wheel torque. According to the invention, the stability, comfort, flexibility and operation efficiency of the wheel-legged vehicle in a complex application scene can be improved.

The application provides a micro-grid group emergency cooperative control method and system, and relates to the field of micro-grid group operation control. The micro-grid group emergency cooperative control method firstly establishes a system steady-state convergence formula according to a cooperative control target; combines local known information, estimates average values of each state quantity and calculates neighbor node interaction information based on a dynamic convergence algorithm, establishes group-to-group and group-to-group distributed cooperative control, fault splitting, compensation quantity satisfying mobile energy storage active power sharing and SOC balance is obtained based on the dynamic convergence algorithm and a linear quadratic regulator; an event triggering condition is constructed through an imbalance of the estimated average value and information transmission error; mobile energy storage seamless exit after fault recovery is realized through supply and demand power transfer. The method can realize micro-grid group frequency / key bus voltage recovery, power sharing and mobile energy storage SOC balance, has the advantages of convergence performance and communication cost, and improves flexibility and elasticity of the micro-grid group under fault conditions.

The embodiment of the invention discloses an emergency obstacle avoidance trajectory planning method, equipment and a medium. The method comprises the steps that the current state of a to-be-planned vehicle and a final driving target lane are obtained, and the vehicle state comprises the transverse and longitudinal position, the course angle and the speed; planning the trajectory of the vehicle in a future period of time according to the current state and the target lane by using an iterative linear quadratic regulator to obtain a first trajectory in a barrier-free scene; with an obstacle avoidance effect as an optimization target, optimizing the first trajectory by using a particle swarm optimization algorithm to obtain a second trajectory in an obstacle scene; and performing smooth optimization on the second track by using an iterative linear quadratic regulator to obtain a third track in the obstacle scene. According to the embodiment of the invention, the problem of efficient, safe and stable trajectory planning in an emergency obstacle avoidance scene is solved.

A robot control method includes: building a two-wheeled inverted pendulum model based on a wheel-legged robot; constructing initial state-space equations based on the two-wheeled inverted pendulum model; linearizing the initial state-space equations to obtain the state-space equations for a linear time-invariant system; obtaining a quadratic performance objective function according to the state-space equations for the linear time-invariant system; and solving the quadratic performance objective function by a linear quadratic regulator to obtain wheel torques of the wheel-legged robot, and controlling the wheel-legged robot according to the wheel torques.

The application specifically relates to a spacecraft attitude weakening-takeover control method based on deep reinforcement learning, which comprises the following steps: in the weakening phase, an interference strategy is learned by a double-delay deep deterministic policy gradient algorithm agent to quickly consume the fuel of a target spacecraft; in the takeover phase, a takeover torque output by a service spacecraft is determined by a linear quadratic regulator; when the takeover torque output by the service spacecraft makes the attitude and angular velocity of a combination converge to a target attitude angle and a target angular velocity respectively, it is determined that the service spacecraft completes the attitude takeover of the target spacecraft; and the method has high robustness and can effectively cope with control challenges in a complex space environment by combining the adaptability of the double-delay deep deterministic policy gradient algorithm with the stability of the linear quadratic regulator.

The invention relates to a compressed air energy storage control analysis platform design method, and the method comprises the steps: carrying out the modular modeling of a core part of a compressed air energy storage system in a dynamic model database layer, and building a three-stage model library which comprises a basic assembly library, a subsystem library and a whole system library; in the control algorithm library layer, a three-level architecture comprising a basic control layer, a collaborative optimization layer and a strategy scheduling layer is adopted to package a proportion-integral-differential controller and a linear secondary regulator; and in the platform operation and data integration layer, at least one service including model management, algorithm calling and task scheduling is provided through a micro-service architecture, and a unified data integration framework, a visual operation interface and a hardware-in-the-loop test interface are constructed according to the at least one service. Therefore, the problems that an existing CAES platform model is poor in reusability, low in algorithm compatibility, data splitting and the like are solved, and the control strategy optimization efficiency and the system cooperative operation capacity are remarkably improved.

This invention relates to an underactuated bridge crane trajectory planning method based on a two-point boundary value problem, belonging to the field of automation control technology. The method first constructs a complete nonlinear dynamic model considering the trolley displacement, hook swing angle, and load swing angle, and sets engineering constraints such as the trolley's maximum displacement, velocity, and acceleration. Then, the trajectory planning problem is transformed into a two-point boundary value problem, solved using numerical analysis methods, generating a unified reference trajectory for trolley positioning, hook swing, and load swing cancellation. This is then combined with an extended-state linear quadratic regulator to form a feedforward-feedback composite controller, achieving precise tracking of the planned trajectory. This invention eliminates the need for linearization of the swing angle, fully considers the actual positional constraints of the trolley, and effectively solves the strong coupling problem between trolley positioning and double-swing swing cancellation through precise nonlinear trajectory planning, improving the positioning accuracy, operational stability, and work efficiency of the bridge crane system.

The invention provides an automatic driving decision planning method, electronic equipment, a storage medium and a program product. The method comprises the following steps: defining a state space and an action space in a space-time three-dimensional space; on the basis of the state space and the action space, adopting Monte Carlo tree search, continuously evaluating values of different actions through repeated iteration of a selection stage, an expansion stage, a simulation stage and a backtracking stage, and obtaining a movement track corresponding to an optimal action sequence; taking the motion trail as an initial nominal trail of an iterative linear quadratic regulator, creating a cost function for optimizing the initial nominal trail based on a multi-factor limitation constraint condition, and continuously optimizing the trail of the vehicle based on multiple iterations of forward propagation and backward propagation of the control quantity in the process of solving the cost function, so as to achieve the purpose of solving the initial nominal trail. The vehicle driving track meets the multi-factor limitation constraint condition, and the cost function optimal solution is obtained. According to the invention, transverse and longitudinal combined flexible decision planning is realized, and the decision planning capability of the vehicle in a complex traffic environment is improved.

The disclosed embodiments include systems and methods to perform automated drilling for both surface and downhole control. The method includes receiving a curvature reference set point, and calculating a curvature error with respect to the curvature reference set point. The method also includes providing the curvature error to a linear matrix inequalities based robust linear quadratic regulator controller. The method further includes determining one or more steering commands based on an output of the linear matrix inequalities based robust linear quadratic regulator controller. The method further includes providing the one or more steering commands to be implemented by a drilling tool during a drilling operation.

The invention discloses a PLC variable pitch control method and system fusing digital twin data, which introduces an active disturbance injection mechanism on the basis of the existing control architecture of a PLC, and enhances the observability of the dynamic characteristics of the system by superposing a weak excitation signal in a reference PID (Proportion Integration Differentiation) instruction. The filtered generator rotating speed and pitch angle feedback data are used for online identifying an ARX model reflecting the real-time pneumatic and mechanical characteristics of a unit, and the ARX model is mapped into a continuous state space model. On this basis, a multi-objective optimization framework based on a linear quadratic regulator is constructed, the power generation efficiency and the mechanical load are balanced through a quantized weight coefficient, and an optimal PID parameter is analyzed and calculated and is smoothly updated. According to the mechanism, online sensing and self-adaptive compensation of slow varying characteristics such as fan component aging and blade icing are achieved in a low-computing-power PLC, and the technical problem that a traditional static control strategy cannot give consideration to performance optimization and load balance in the whole life cycle is solved.

This invention discloses a method and system for trajectory planning of autonomous vehicles based on dynamic safety buffers. The method first acquires the state information of the autonomous vehicle and surrounding obstacles; then, it establishes state equations and multi-objective cost functions based on the vehicle's kinematic model. The core of the method lies in dynamically calculating the safety buffer distance based on the collision time between the autonomous vehicle and obstacles, and constructing adaptive collision constraints accordingly. Constraints are integrated into the cost function through an exponential obstacle function, and an efficient iterative solution is achieved using a constraint-iterative linear quadratic regulator algorithm, ultimately outputting the optimal trajectory and control sequence. The system includes corresponding modules for environmental perception, state modeling and optimization objectives, dynamic safety buffer calculation, constraint processing and optimization solution, and trajectory output. This invention achieves adaptive adjustment of the safety distance according to the risk level, improving driving efficiency and comfort while ensuring safety, and meeting the real-time requirements of autonomous driving through an efficient optimization framework.

The application provides an industrial Internet of Things adaptive weight design and power optimization method and system, and relates to the technical field of industrial Internet of Things communication and control collaborative design. The method comprises the following steps: constructing an industrial Internet of Things control system model containing a limited retransmission mechanism, analyzing the block error rate and the average transmission number based on the limited code length information theory, and then deducing the effective average information age affected by the retransmission; establishing a linear quadratic regulator control cost model based on the information age, and deducing the linear upper bound relationship between the control cost and the information age; and constructing a joint optimization problem with the communication energy consumption and the control cost as the targets. A three-layer adaptive weight adjustment mechanism is designed according to the real-time channel state, and the multi-objective optimization weight is dynamically adjusted; further, by utilizing the quasi-convex characteristics of the joint cost function, the golden section search algorithm with a hot start is adopted to solve the optimal transmission power, so that the influence of the industrial wireless channel fluctuation on the control stability is effectively relieved.

The invention discloses an anti-interference autonomous navigation method for an unmanned aerial vehicle in a weak communication environment and a related device. According to the method, a VINS-Fusion algorithm is adopted to fuse odometer IMU data and camera visual data, and the attitude and the motion state of the unmanned aerial vehicle are obtained; constructing a raster map based on camera visual data, and generating an initial path by using a jump point search algorithm; converting the initial path into a B spline curve, and dynamically adjusting a control point in the B spline curve to obtain an optimized path; and inputting the pose and the motion state as real-time states, and outputting a control instruction by using a linear quadratic regulator based on the optimized path to realize the attitude control and trajectory tracking of the aircraft in the disturbance environment. According to the method, LQR control, JPS path planning, B spline trajectory optimization and a VINS-Fusion algorithm are integrated, flight stability can be guaranteed, and reliable navigation is supported.

The application provides a four-wheel independent driving full-rotation profiling chassis and a pose stability control method, which comprises a chassis frame, four steering and walking components, four profiling adjusting components, an inertial measurement unit and a control system; the control system controls the walking and full-rotation steering movement of the chassis, and actively controls the pose of the chassis frame based on the measurement data of the inertial measurement unit through the profiling adjusting components. The steering and walking components of the chassis adopt an independent differential wheel train structure with two inputs and two outputs, and can realize multiple walking and steering movement modes; the series active suspension system can realize ground profiling and pose stability control. Based on the vertical dynamics model of the chassis degrees of freedom, a linear quadratic regulator is designed, the feedback information of the inertial measurement unit and the displacement sensor is fused, active adjustment of the suspension is realized, and the stability of the chassis in complex terrain is improved.

The application discloses an aircraft online trajectory planning method based on an iterative linear quadratic regulator and belongs to the field of aircraft trajectory optimization.The application realizes the following method: a high-fidelity aircraft dynamics model constraint considering the earth rotation and the flattening is constructed to realize the accurate characterization of the aircraft dynamic characteristics; the speed, the speed inclination angle, the speed deflection angle, the geocentric distance, the longitude and the latitude of the aircraft are taken as the state variables, and the attack angle and the sideslip angle are taken as the control variables, so that an optimal control model of the aircraft trajectory planning problem is constructed, and the constraint conditions include the dynamics constraint, the path constraint, the initial and terminal constraints and the control constraint; the iterative linear quadratic regulator is used for solving to obtain the optimal control variable U of the trajectory planning and the optimal trajectory of the aircraft, so that the real-time performance and the accuracy of the trajectory planning are improved; and according to the control sequence U, the flight trajectory of the aircraft is obtained according to numerical integration, so that the accuracy and the efficiency of the online trajectory planning are balanced, and the trajectory optimization of the strong nonlinear system under the complex disturbance environment is realized.