40 results about "Riccati equation" patented technology

The invention relates to the technical field of geological prospecting, in particular to a geological prospecting drilling rig drilling real-time depth measuring system, which comprises a vibration acquisition module, a data processing module and a data processing module, the original vibration signals are divided into 256 time sequence segments, sampling interval values are set, the amplitude value and the phase value of each time sequence segment are calculated, and drilling dynamic characteristics are generated. According to the method, vibration sensors are arranged on the surface of a drilling tool to collect original vibration signals, the signals are segmented to calculate amplitude values and phase values, drilling dynamic characteristics are obtained, difference values of adjacent time sequence segments are calculated and compressed, Fourier transform is carried out to generate frequency spectrum parameters, and the frequency spectrum parameters are projected to Grassmann space to calculate characteristic vectors and characteristic values. And constructing a tangent plane projection function to calculate a geodesic distance to obtain a correction coordinate, and iteratively calculating a depth value based on a Riccati equation. And meanwhile, time sequence segmented compression is adopted, so that data redundancy is reduced, and the utilization efficiency of computing resources is optimized.

The invention relates to the field of fractional calculus and partial differential equation solving, and discloses a novel fractional sub-equation neural network analysis solver, which comprises the following steps: constructing a full-connection neural network model, and substituting the formula into a fractional partial differential equation to obtain a fractional sub-equation neural network analysis solver; and a Riemann-Liouville fractional derivative is corrected by utilizing Jumarie which meets a chain rule, so that the problem is converted into an algebraic equation form. Unknown variables and coefficients of the integer order partial differential equation are extracted by using a symbol calculation tool, an overdetermined nonlinear algebraic equation set is generated, and the weight and bias of the neural network are obtained by solving the equation set. And finally, optimizing the preliminary analytical solution in combination with a special solution of a fractional order Riccati equation, and generating a final analytical solution comprising a generalized fractional order hyperbolic function, a generalized fractional order trigonometric function and a Mittag-Leffler function form. According to the method, the analytic solution efficiency is improved, the expression range of understanding is expanded, and meanwhile, the mathematical characteristics of fractional derivatives are reserved.

The invention discloses an online Gaussian process regression-based model prediction control method and device for a precise air-drop system, and the method comprises the steps: processing a dynamic model of a parafoil system into a transverse lateral motion linear time-invariant nominal model through the aerodynamic analysis of the parafoil system, and considering the flexibility of the parafoil and a residual part of the nominal model of wind disturbance; designing an online Gaussian process regression algorithm to learn a (potential) time-varying nominal model residual part; a new performance functional is designed for tracking error weighting based on posterior prediction variance of a Gaussian process regression algorithm, a random optimal control problem is constructed and solved in two parts, one part is used for controlling a nominal model, a suboptimal strategy of an off-line solving Riccati equation is adopted, and the optimal control problem is solved. And the other part is used for controlling the residual error and disturbance of the model obtained by online Gaussian process regression learning, and solving to obtain an optimal control sequence so as to obtain an optimal control law. The invention provides a novel and efficient solution for accurate homing control of the air-drop system.

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.

The invention provides a crane anti-swing control method and device based on an LQR algorithm, and belongs to the technical field of crane control. The crane anti-swing control method based on the LQR algorithm comprises the following steps: S1, obtaining a state-space equation of a crane system; s2, an LQR controller u =-Kx based on the LQR algorithm is designed based on the state-space equation, K is a feedback matrix, a K value of an optimal solution is obtained through calculation based on the Riccati equation and a cost function, and the K value is substituted into the LQR controller to obtain an optimal control variable u through calculation; and S3, substituting the optimal control variable u into the state-space equation so as to carry out anti-swing control on the crane system. According to the method, a state-space equation of a crane system is established, an LQR controller based on an LQR algorithm is applied to calculate and obtain a K value of an optimal solution, an optimal control variable u is calculated and obtained according to the K value of the optimal solution, the optimal control variable u is substituted into the state-space equation, output of the crane system is influenced, and rapid anti-swing and anti-swing are achieved.

The invention discloses a fault compensation and H-infinity fault-tolerant tracking control method for an autonomous underwater vehicle, and belongs to the technical field of engineering control methods. Comprising the following steps: S1, aiming at actuator faults, sensor faults and external disturbance problems in vertical plane motion of an autonomous underwater vehicle, introducing a depth variable and establishing a high-order all-drive fault model; s2, designing an intermediate estimator based on a high-order all-drive model, and realizing estimation of a system state and a fault; and S3, designing a controller based on the high-order all-drive model, solving a game Riccati equation by using a Minmax method, obtaining an optimal control strategy, and realizing fault compensation and external disturbance suppression. The effectiveness of the method and the excellent tracking control effect of the designed controller are verified through a depth control simulation experiment of the autonomous underwater vehicle.

The invention relates to the technical field of automatic driving, in particular to an omnidirectional motion chassis device and a trajectory tracking control method thereof. The control method comprises the following steps: constructing a two-degree-of-freedom chassis dynamical model of the chassis, converting the chassis dynamical model to obtain a chassis trajectory tracking error model, establishing a Riccati equation according to the chassis trajectory tracking error model, and solving the Riccati equation according to the current motion state information of the chassis. Obtaining a tracking error according to the current motion state information of the chassis and the target path, and obtaining an initial rotation angle control quantity of a driving wheel of the chassis according to a coefficient obtained by solving a Riccati equation and the tracking error; the current course error of the chassis is obtained, PID adjustment is conducted on the initial rotation angle control quantity of the driving wheels of the chassis according to the current course error of the chassis, and the adjusted rotation angle control quantity is obtained; and trajectory tracking control is carried out on the chassis according to the adjusted rotation angle control quantity, so that the control precision of the chassis is improved.

The application discloses a spacecraft attitude maneuver path real-time planning method based on a flywheel under multiple constraints, which comprises the following steps: state-dependent decomposition is performed on integrated spacecraft kinematics and dynamics equations containing a flywheel working principle; a performance-power optimization index is designed, and a spacecraft attitude path planning problem of performance-power comprehensive optimization under multiple constraints is constructed; a state-dependent penalty function and a continuous saturation function are designed to process spacecraft angular velocity constraints, flywheel speed saturation constraints and flywheel output torque constraints, the constructed spacecraft attitude path planning problem of performance-power comprehensive optimization under multiple constraints is converted into an augmented state-dependent optimization problem; and a solution of the converted augmented state-dependent optimization problem is obtained by solving a state Riccati equation in real time. The method avoids numerical reconstruction and solvability judgment of attitude dynamics state-dependent decomposition, and realizes real-time planning of a spacecraft attitude maneuver path based on a flywheel under multiple constraints.

The invention provides a non-zero and dynamic Stackelberg pursuit game control method for a spacecraft system, and aims to solve the pursuit game problem of a spacecraft in the fields of space exploration, military reconnaissance and the like. According to the method, a traditional zero-sum game model is broken through, a non-zero-sum dynamic game framework is adopted, and the cooperation and competition relation between purchasers and escapers is reflected more practically. Effective control of a spacecraft pursuit game is realized through five steps of establishing a mathematical model, designing a control strategy, converting a cost function, solving a matrix Riccati equation and solving an optimal control gain.

The present invention belongs to the technical field of unmanned underwater vehicles, and particularly relates to a robust motion control method and system based on a state-dependent Riccati equation. The robust motion control method based on the state-dependent Riccati equation is applied to an unmanned underwater vehicle with a manipulator arm. The unmanned underwater vehicle with a manipulator arm includes a hull and an underwater manipulator arm arranged on the hull. The method includes establishing a dynamic model of the unmanned underwater vehicle with a manipulator arm based on the Lagrangian energy method; designing a fuzzy disturbance observer based on the dynamic model, and using a fuzzy logic system to compensate for the external disturbance forces received by the hull and the underwater manipulator arm of the unmanned underwater vehicle with a manipulator arm during the motion process; designing a robust controller based on the state-dependent Riccati equation based on the fuzzy disturbance estimator, and compensating for errors by adjusting the ranges of the state weight matrix and the input weight matrix and restricting the upper bounds of parameter uncertainties. The present invention realizes the robust control of the unmanned underwater vehicle with a manipulator arm.

The invention discloses an efficient numerical solution method for a steady-state water head field of a dam containing a complex foundation, and relates to the technical field of geotechnical engineering. The method comprises the following steps: establishing a seepage mathematical model in a Cartesian coordinate system by acquiring foundation parameters and boundary conditions, introducing similar lines parallel to a far-field boundary to construct an improved proportional boundary finite element coordinate system mapping relation, and discretizing and deducing a variable coefficient control equation by adopting a one-dimensional high-order spectrum unit; an equation is simplified into a Riccati equation by analyzing the characteristics of a coefficient matrix, an infinite seepage matrix is solved in combination with Schur decomposition, a seepage matrix at a boundary is obtained through iteration of a fourth-order Runge-Kutta method with the infinite seepage matrix as an initial value, and finally steady-state water head field distribution is obtained. According to the method, the continuity of the interface water head and the flowing boundary condition is guaranteed, the precision and efficiency of complex foundation seepage solving are greatly improved, and reliable technical support is provided for engineering seepage-proofing design and stability evaluation.

The invention discloses a vehicle interaction decision-making method based on an unprotected intersection and a related device, and the method comprises the steps: obtaining a system state matrix, an own vehicle control matrix and an other vehicle control matrix in linear system state equations of an own vehicle and an other vehicle, first to fourth positive semi-definite matrixes and first and second positive definite matrixes are arranged in the optimization target of the linear quadratic differential game problem of the own vehicle and the other vehicle; taking the first positive semidefinite matrix and the third positive semidefinite matrix as a first intermediate matrix and a second intermediate matrix at the Nth moment respectively; according to the first intermediate matrix, the second intermediate matrix, the first positive definite matrix, the second positive definite matrix, the system state matrix, the self-vehicle control matrix, the other-vehicle control matrix, the second positive definite matrix and the fourth positive definite matrix at the Nth moment, reverse recursion is carried out on an optimal control gain matrix equation set of the two vehicles and a coupling Riccati equation of the first intermediate matrix and the second intermediate matrix at the kth moment; and obtaining an optimal control gain matrix sequence of the vehicle, and controlling the vehicle according to a control quantity sequence determined based on the optimal control gain matrix sequence.

This invention discloses a robust control method for spacecraft pursuit and escape game based on incomplete information in a non-zero-sum game, mainly comprising the following steps: Step 1: Establishing a non-zero-sum game control model for spacecraft containing uncertainties based on dynamics; Step 2: Designing an adaptive estimator for the control gain of the target spacecraft under incomplete information; Step 3: Establishing an improved Riccati equation for the non-zero-sum game control of spacecraft based on the adaptive estimator; Step 4: Designing a value iteration algorithm for the spacecraft control strategy based on the improved Riccati equation. This control method overcomes the adverse effects of uncertainty in the pursuit and escape game system and solves the problem of unknown cost function and control strategy of the target spacecraft, ensuring the effectiveness and control accuracy of the spacecraft pursuit strategy.

The application discloses a spacecraft magnetic moment attitude control method based on a non-inverse iteration algorithm, and the method establishes a linear periodic model for a spacecraft attitude control system which only relies on a magnetic moment; on the basis, according to a linear quadratic optimal control theory, an optimal controller design problem is converted into a solution problem of a discrete periodic Riccati equation associated with the optimal controller design problem; on the basis of a gradient descent principle, an error function is constructed, and a non-inverse iteration algorithm based on the gradient descent principle is proposed to solve the discrete periodic Riccati equation, so that a matrix inversion operation in a traditional solution method is avoided, and the calculation efficiency and the numerical stability are effectively improved; the obtained Riccati equation solution is used to obtain an optimal state feedback control law, so that the spacecraft attitude optimal control only using the magnetic moment is realized, and a new solution scheme is provided for an attitude control problem of a limited actuator in a space task.

The present application relates to a kind of main satellite of star companion under the spacecraft main satellite cooperative evasion control method, it is applied to the non-cooperative target such as space debris, malfunction spacecraft when facing unknown intention approach, task spacecraft is cooperated with star companion in the vicinity of orbit and carries out evasion in one pursuit one escape one guard scene.First, the dynamic model of relative motion state including non-cooperative target, guard satellite and task spacecraft is established;Then, the cost function of both sides of differential game is constructed using the idea of differential game;After that, the Hamilton-Jacobi method is used to solve Riccati equation, and the full-state linear feedback control solution of task spacecraft and satellite is obtained.The present application can avoid non-cooperative target approaching important on-orbit task spacecraft as much as possible, and assist task spacecraft to evade the potential threat of direct collision from the target through the active shielding of satellite, which is consistent with the engineering practice.

The present invention discloses a design method for a high-speed motor rigid rotor system, which includes establishing a state space model of the high-speed motor rigid rotor system; establishing a quadratic cost function according to the state and control input of the high-speed motor rigid rotor system; solving the Riccati equation according to the state space model and performance index of the high-speed motor rigid rotor system to obtain an optimal state feedback matrix K; calculating an optimal control input signal u by formula u=‑KZ; inputting the calculated optimal control input signal u into the system to implement an LQR controller. The present invention adopts minimization of a predefined cost function to achieve comprehensive optimization of system performance. The present invention can not only ensure the stability of the closed-loop system, but also resist changes in system parameters and external disturbances to a certain extent. The present invention can greatly reduce the noise of the entire high-speed motor rigid rotor system based on electromagnetic bearings by adopting an effective control or signal processing method.

The present invention discloses an inertial navigation modulation axis control method based on an LQR regulator, which relates to the technical field of inertial navigation, and includes: sampling the current angular velocity and the angular position of the motor and respectively taking the differences from the expected values of the inertial navigation system, and taking the two differences as the angular velocity error and the angular position error respectively and inputting them into the LQR regulator; taking the state equation of the motor as the objective function in the LQR regulator, generating and solving the Riccati equation, inputting the expected acceleration of the motor into the PID controller and tracking the expected acceleration, taking the difference between the expected acceleration and the currently sampled acceleration of the motor, and calculating the control quantity as the output of the PID controller, and the control quantity is the Q-axis control voltage of the motor in the rotating coordinate system; the inertial navigation system calculates and processes the control quantity through the SVPWM algorithm to control the modulation axis of the inertial navigation device, and the modulation axis issues an instruction to the motor to correct the operating state of the motor.

The invention provides a time-lag industrial process tracking control method based on inner and outer ring singular perturbation decomposition, and the method comprises the steps: firstly building a cascade singular perturbation time-lag system model for an industrial process containing fast and slow dynamic states and time lag, and decomposing a tracking problem into an optimal tracking problem of an outer ring slow subsystem and an inner ring fast subsystem based on a singular perturbation theory; linear quadratic tracking is converted into an adjustment problem by constructing an auxiliary system, and an optimal controller theoretical form is deduced. The condition that model parameters are difficult to obtain is considered, a data-driven reinforcement learning algorithm is designed for inner and outer ring operation layers, a compact equation least square solution converted by a Riccati equation is solved through input and output data, and an optimal control strategy is learned. And finally, the learned controller is utilized to realize the optimal tracking of the given signal by the system. The method does not depend on system prior information, solves the problem of traditional control in a multi-scale and time-delay coupling system, improves the control precision and response speed of a complex industrial process, and has important application value in the field of production automation.

The invention relates to the field of multi-agent systems, in particular to a decomposition compensation mechanism differential privacy protection method for a sampling control multi-agent system. Comprising the following steps: establishing a multi-agent system under non-uniform sampling control; obtaining a hybrid distributed controller and a differential privacy noise generator of the multi-agent system under a decomposition compensation mechanism; discretization is used to obtain a new closed-loop system, and a multi-agent system is obtained to realize progressive unbiased output average consensus; designing a time-varying control gain, and quantifying the sensitivity and privacy level of the multi-agent system in a limited time domain; and determining sampling step length and scale parameters, and enabling the multi-agent system to realize a progressive unbiased output average consensus meeting predefined precision and an expected privacy upper bound on an infinite time domain by utilizing a Riccati equation and a pole assignment method. According to the method, when random noise is added, the communication burden is reduced, the multi-agent progressive unbiased output average consensus is realized, and the initial information of the multi-agent is better protected from being stolen.

The invention discloses an SPICE model generation method considering passive network thermal noise, and the method comprises the steps: carrying out the modeling of a multi-port passive network S parameter through employing a passive macro-modeling algorithm, obtaining a passive state space model, constructing a Riccati equation, solving the Riccati equation, obtaining a state space model of a lossless network, employing a state space-based SPICE model generation method, carrying out the solving of a Riccati equation, and carrying out the calculation of the Riccati equation. And obtaining an equivalent SPICE circuit model, and carrying out joint simulation by utilizing the SPICE circuit model and other modules in the circuit to obtain an analysis result of field-circuit co-simulation. According to the method, a state space model for representing an original system is decomposed into a lossless network and a pure resistance network, wherein the lossless network only considers the transmission characteristics of a passive network and can be synthesized by using a noiseless model device; and the pure resistance network is used for equivalent thermal noise generated by the passive network, so that the accuracy of a result in simulation related to noise analysis is ensured to be kept.

A dynamic target tracking method based on cascaded robust optimal hybrid filtering, comprising: establishing a continuous kinematic model for the motion of a mobile robot in a warehouse; considering the hybrid disturbances in the complex environment of the warehouse and the inaccuracy of the model, establishing a system state equation and an observation equation of the sensor; designing a corresponding cascaded robust optimal hybrid filter according to the observed values of the sensor; giving an autonomous error model of the system, designing and solving the Riccati equation through an iterative algorithm to obtain the gain of each filter; substituting the gain of the above filter to obtain a real-time estimated value, and realizing real-time position tracking of the mobile robot. The present invention can perform real-time high-precision estimation of the position of the mobile robot.

The invention discloses an unmanned tracked vehicle trajectory tracking method based on H infinity model prediction hybrid control, which comprises the following steps: firstly, establishing a tracked vehicle dynamics model, and discretizing through a forward Euler method; an H infinity and MPC double-controller parallel computing architecture is adopted, and an MPC controller combines a reference trajectory and a prediction state to generate an optimal control quantity of an adaptive constraint through rolling optimization to minimize a tracking error and a control cost; multi-source interference is introduced into the H infinite controller, and disturbance compensation control quantity is generated through interference modeling, linear processing and Riccati equation solving; and the optimal control quantity and the disturbance compensation control quantity are fused, a crawler executing mechanism is driven, a new state is fed back, and closed-loop feedback control is achieved. The method not only has the dynamic optimization capability of the MPC controller, but also has the strong anti-interference capability of the H infinite controller, realizes synchronous consideration of optimization and robustness, and ensures that the system always keeps trajectory tracking precision and stability under complex working conditions through closed-loop feedback control.

The present disclosure comprises: on the basis of establishing a dynamic model of a steer-by-wire actuator and converting an equation of the dynamic model into a state-space equation form, obtaining an angle difference and an angular velocity difference, and calculating a control quantity of an LQR controller; using the angle difference and the angular velocity difference as inputs, and constructing a system error equation on the basis of the above state-space equation; solving a Riccati equation using an iterative method to obtain a gain matrix, and, on the basis of the gain matrix, calculating an LQR torque in the design of the LQR controller; by designing an observer, implementing a feedforward compensation strategy and calculating a feedforward torque; and, on the basis of the feedforward torque and the LQR torque, obtaining a total torque input to the system. The disclosure uses the LQR solution to achieve the optimal gain, avoiding repeated parameter trial and error by engineers, while taking into account system convergence speed and system energy consumption. The feedforward is calculated in real time using the observer, enhancing the anti-interference capability of the system and improving system response speed.

The invention relates to the technical field of intelligent compensation control, in particular to a linear motor driving system control method based on robust baseline control and adaptive compensation. The method comprises the following steps: establishing a linear motor state space model; a dynamic output feedback baseline controller is obtained by iteratively solving a Riccati equation set based on a disturbance suppression level index, a closed-loop matrix is extracted, and a baseline control signal is output; calculating a steady-state gain and filtering the reference input to generate a steady-state feed-forward control signal; estimating the total uncertainty on line through a state predictor, constructing a projection operator to decompose the total uncertainty into a matched component and an unmatched component, performing output equivalent mapping on the unmatched component, and performing band-limiting processing through a low-pass filter to synthesize a self-adaptive compensation signal; and after the three paths of signals are superposed, the linear motor is driven through amplitude saturation and change rate limitation. According to the method, the robustness and the online compensation capability are fused, and the tracking precision and the anti-interference robustness of the linear motor are remarkably improved.

The invention discloses a vehicle control method and device, equipment, a medium and a vehicle, and the method comprises the steps: obtaining vehicle state data; estimating a system state by adopting Kalman filtering based on the vehicle state data to obtain an optimal state estimation value; obtaining a feed-forward gain according to the REPS system state-space equation and the motor torque; obtaining a feedback gain according to the REPS system state space equation and the Riccati equation; and determining an optimal control gain according to the feedforward gain and the feedback gain, and obtaining an optimal control law according to the optimal control gain and the optimal state estimation value, so as to control a motor to drive a steering wheel to rotate through the optimal control law. The method can reduce the external interference influence on the system, accelerates the response speed of the system, enables the actual steering wheel rotation angle and rotation speed to closely track the target steering wheel rotation angle and rotation speed, reduces the tracking error, and achieves the stable control.

The invention belongs to the technical field of unmanned underwater vehicles, and particularly relates to a robust motion control method and system based on a state-related Riccati equation, and the robust motion control method based on the state-related Riccati equation is applied to a carrier arm unmanned underwater vehicle. The arm-carrying unmanned underwater vehicle comprises a boat body and an underwater mechanical arm arranged on the boat body, and the method comprises the following steps: establishing a dynamic model of the arm-carrying unmanned underwater vehicle based on a Lagrange energy method; a fuzzy disturbance observer is designed based on the kinetic model, and external disturbance force borne by a boat body and an underwater mechanical arm of the arm-carrying unmanned underwater vehicle in the moving process is compensated through a fuzzy logic system; and designing a robust controller based on a state correlation Riccati equation based on a fuzzy disturbance estimator, and compensating errors by adjusting a state weight matrix, inputting the range of the weight matrix and limiting the upper bound of parameter uncertainty. According to the invention, robust control of the arm-carrying unmanned underwater vehicle is realized.

The application discloses a kind of factory control method based on Internet of Things, the present application relates to industrial control system technical field, the method according to the time delay, packet loss and quantization error generated by network transmission determines the Markov mode where controlled object is located, gain scheduling interpolation is carried out by solving algebra riccati equation on line, obtain network adaptive gain matrix, bypass detection is carried out to industrial internet control message, and abnormal confidence score is obtained, according to this, normal mode and security degradation mode gain matrix are smoothly fused, and security fusion gain matrix is obtained, two kinds of gain matrix are weighted and summed to obtain comprehensive feedback gain matrix and generate control instruction, the present application can realize the dynamic trade-off of control real-time and security protection redundancy when network uncertainty and security risk dynamic change, guarantee controlled object stable operation, applicable to industrial internet factory closed-loop control scene.

The application relates to a kind of multi-channel networked control system asymptotic stability control law design method and system, comprising: establishing the first system model of networked control system with delay and packet loss simultaneously and equivalent expansion to the first system model to obtain the second system model;Establishing intermediate state and equivalent constraint condition to build the third system model;According to the expression of the control law designed by the third system model;Construct Riccati equation and obtain positive definite solution, substitute the positive definite solution into the expression of the control law, obtain the third system control law;Delete the channel information not transmitting information in the third system control law to obtain the first system control law;According to the first system control law, multi-channel information transmission can be carried out, so as to solve the problem of multiple transmission delay and random packet loss coexistence, improve the integrity of transmission information during channel transmission.

The invention discloses a spacecraft magnetic moment attitude control method based on a non-inverse iterative algorithm, and the method comprises the steps: building a linearized periodic model for a spacecraft attitude control system which only depends on magnetic moment; on the basis, according to a linear quadratic form optimal control theory, an optimal controller design problem is converted into a solving problem of a discrete period Riccati equation connected with the optimal controller design problem; on the basis of the gradient descent principle, by constructing an error function, a non-inverse iterative algorithm based on the gradient descent principle is provided to solve a discrete period Riccati equation, matrix inversion operation in a traditional solving method is avoided, and calculation efficiency and numerical stability are effectively improved; and an optimal state feedback control law is obtained by using the obtained Riccati equation solution, so that spacecraft attitude optimal control only using the magnetic moment is realized, and a new solution is provided for the attitude control problem of a limited actuator in a space task.

The application discloses a wheat crown layer geometric parameter extraction method, system, equipment and a storage medium, and comprises the following steps: constructing a multi-modal sensor integrated acquisition system; adopting an ESO-LQR-based pitch axis disturbance control strategy for the acquisition system; collecting point cloud data and image data of the wheat by using the acquisition system, extracting two-dimensional traits based on color indexes by using the image data, and extracting three-dimensional crown layer structure distribution traits by using the point cloud data and the image data. The ESO-LQR-based pitch axis disturbance control strategy comprises the following steps: establishing a pitch axis dynamics model and performing disturbance analysis; expanding the lumped disturbance into a system state, designing a third-order extended state observer (ESO), and generating a disturbance estimation value; constructing a state vector for an optimal controller (LQR), designing a cost function, solving an optimal feedback matrix through a Riccati equation, and generating a control amount; and compensating the disturbance estimation value into the control amount to generate a final control input.