47 results about "Linear quadratic" patented technology

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.

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 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 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 invention discloses an amphibious vehicle cluster autonomous cooperation method based on LQR control, and the method comprises the steps: (1) a master vehicle plans an expected trajectory from a starting position to a task position, takes the expected trajectory as a global reference trajectory, and transmits the global reference trajectory to each slave vehicle; (2) each slave vehicle determines an expected trajectory based on the relative pose of the slave vehicle and the master vehicle and the global reference path; and (3) the master vehicle and the slave vehicle control the vehicle to track an expected trajectory through a linear quadratic regulation control algorithm. According to the method, a control strategy of centralized planning and distributed execution is adopted, when a vehicle operation medium changes, the structure of a linear quadratic regulation (LQR) control algorithm is not changed, and corresponding model parameters and an LQR weight matrix are switched to adapt to different environments; and meanwhile, the smoothness of switching from underwater to land or from land to underwater is greatly improved.

The invention relates to the technical field of multi-agent driving control, in particular to an uncertain nonlinear agent driving control method based on linear quadratic adjustment, and the method comprises the steps: deducing a standard differential equation mathematical model according to an uncertain nonlinear kinematics model of a guider, thereby independently listing unmeasurable nonlinear items; a non-measurable nonlinear item is estimated through an observer, a dynamic tracking controller is designed based on the observer, and the input state stability of the system is ensured in combination with a multi-loop nonlinear small gain condition, so that a guider can track a time-varying reference signal. Then, a reference signal generating system which generates a variable reference signal at the moment is used as a new guider, a drive control controller is designed through linear quadratic adjustment, so that optimal input is solved through state estimation and error optimization, an uncertain nonlinear drive control problem is simplified to be a linear quadratic adjustment problem to be solved, and the drive control precision is improved. The integrated collaborative design of'tracking the reference signal by the guider 'and'arriving the target position by the rover' is realized.

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 invention discloses a model identification and damping control method and system for a network-forming converter, and belongs to the technical field of power systems. The method comprises the following steps: injecting a pre-generated test signal sequence into an active power reference instruction of a network-forming converter to excite a system to generate a dynamic response; response data of the system under excitation of the test signal sequence is collected, and the response data and the test signal sequence form an evolution type time sequence data set; based on the evolution type time sequence data set, an equivalent linear model for describing system dynamics is calculated through a Koopman operator identification method; calculating to obtain a state feedback gain matrix by solving a linear quadratic optimal control problem; generating a damping control signal based on the state feedback gain matrix and system state data acquired in real time, and superposing the damping control signal to a control instruction of the network-forming converter; according to the method, the risk of performance reduction and even instability caused by model mismatch in a traditional method is overcome.

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 application discloses a spacecraft escort problem solving method based on LQR and differential game, constructs a game model of the escort problem based on a CW equation, and comprises a cost function, optimization variables, dynamic constraints, acceleration capacity constraints and the like; the cost function adopts a linear quadratic index, the optimization variables are continuous control force accelerations of both attack and defense sides, the dynamic constraints are described by using the CW equation, and the acceleration capacity constraints are modeled by using maximum acceleration limitation; the method has the advantages of simple model, clear physical meaning and convenient solving; in addition, the application converts a complex non-zero-sum game problem into a phased zero-sum game problem and an optimal control problem, can effectively solve the spacecraft escort problem under the action of continuous thrust, is an effective expansion of a continuous thrust escort problem model and method in the prior art, and significantly improves the solving efficiency of the problem. Through the method, an effective cooperative defense threat solution can be provided for on-orbit spacecraft.

The application discloses a strip plate shape intelligent regulation and control method based on an adaptive fusion model, which comprises the following steps: step 1, collecting historical rolling production process data and performing pretreatment; step 2, constructing a rolling production process data set based on the pretreated data; step 3, classifying plate shape quality according to the ratio of strip crown and target thickness and setting a category label; step 4, establishing an adaptive strip outlet plate shape diagnosis model containing multiple classifiers based on a DS theory, and training the diagnosis model through the rolling production process data set; step 5, inputting rolling production process data under a new rolling schedule into the classifier of the trained adaptive strip outlet plate shape diagnosis model to obtain a real-time classification prediction result of the strip, and if the prediction classification is under-crown or over-crown, step 6 is executed; otherwise, the process parameters are not adjusted; and step 6, dynamically adjusting and optimizing the process parameters based on an inverse linear quadratic control (ILQ) according to the prediction result of step 5.

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.

The application discloses a variable reference trajectory iterative learning linear quadratic control method for an injection molding process, which firstly utilizes the tracking error of the last period in the injection molding process to construct a variable reference trajectory learning strategy; secondly, based on the variable reference trajectory learning strategy, a state space model with prediction ability between the controlled variables and the manipulated variables of the injection molding process is established; and finally, based on the state space model with prediction ability, a variable reference trajectory iterative learning linear quadratic control law is designed. The application avoids the burden of designing a state observer and enhances the control performance of the controlled variables of the injection molding process control system when facing time lag and reference trajectory change.

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 an intelligent inspection robot trajectory tracking method integrating trajectory optimization and LQR control, and the method comprises the following steps: firstly, generating an initial path point sequence through employing a global path planning algorithm based on a rasterized environment map, and completing the initialization of a global path; and optimizing the path structure. And a Reeds-Shepp curve is used for fitting a path. And then setting a high-feasibility track obtained by fitting as a target path of the inspection robot for subsequent control and tracking. The robot obtains pose information in real time through a sensor of the robot. And finally, a linear quadratic adjustment control algorithm (LQR) is adopted to transversely control the robot, and the speed is longitudinally adjusted according to path speed planning in combination with a speed closed-loop controller. According to the method, trajectory optimization and LQR control are combined, the accuracy and stability of trajectory tracking of the intelligent inspection robot are improved, complex environments can be effectively handled, the inspection efficiency and quality are improved, and the method has wide application prospects in the fields of industrial inspection, security monitoring and the like.

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.

This invention provides a predictive control method for laser processing mirrors based on an extended-dimensional mapping dynamic model, belonging to the field of laser processing mirror motion control. The method includes the following steps: S1, proposing a dynamic model based on an extended-dimensional mapping of a neural network, combining neural networks and linear models to characterize the dynamic characteristics of the dual-axis laser processing mirror; S2, identifying the parameters in the extended-dimensional mapping dynamic model by solving an optimization problem based on input-output data; S3, designing a linear quadratic programming problem based on the extended-dimensional mapping dynamic model and the control objective; S4, solving the quadratic programming problem to obtain a predictive controller with nonlinear feedforward compensation capability. This invention mainly addresses the trajectory tracking problem of dual-axis laser processing mirrors under high-frequency sampling, solving the problems of insufficient tracking accuracy caused by the nonlinearity of the mirror driver itself and the difficulty in solving nonlinear optimization problems under rapid sampling.

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 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 kind of dual wind wheel wind turbine operation control methods, comprising: obtaining the speed and pitch angle of the front and rear wind wheels of dual wind wheel wind turbine under current wind speed;With the maximum overall output power of dual wind wheel wind turbine as the target, the target speed and pitch angle of the front and rear wind wheels of dual wind wheel wind turbine under current wind speed are determined;Linearization model based on state space of the transmission chain of the front and rear wind wheels is established respectively;Based on the linearization model, a state observer, a disturbance adjustment controller and a quadratic regulator are introduced, and the torque controller and the pitch controller of the front and rear wind wheels are designed to control the front and rear wind wheels.The method can take the maximum power output of the whole machine as the target, realize a control strategy based on linear quadratic regulation and disturbance adjustment technology, to solve the mutual influence problem of the front and rear wind wheels of dual wind wheel wind turbine during operation, improve the robustness of the system, improve the overall wind energy absorption rate of dual wind wheel, and fully exert the high-efficiency wind energy conversion capacity of dual wind wheel wind turbine.

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 control method for a large tail-seat electric vertical takeoff and landing (EVTOL) aircraft, employing a hierarchical control architecture. First, a time-series trajectory from the starting state to the ending state is optimized by the planner based on mission requirements. Leveraging the differential flatness property of tail-seat UAVs, the kinematic trajectory is mapped to the corresponding dynamic states, namely the aircraft's desired attitude and angular velocity, which, along with the desired position and velocity, serve as inputs to the MPC controller. The MPC controller constructs a constrained linear quadratic programming problem based on state errors, calculating the desired acceleration and angular velocity control commands by solving this optimization problem. These commands are sent to the flight control autopilot to complete the underlying angular velocity control and power distribution, ultimately calculating the control signals for each actuator to achieve flight control. The aerodynamic and propeller characteristics of the aircraft are identified through CFD simulation and wind tunnel testing, and the corresponding data is imported into the controller for precise control compensation.

The invention provides a local demagnetization fault diagnosis and fault-tolerant control method for a double-permanent-magnet synchronous motor. The method comprises the following steps: establishing a dynamic model of a double-permanent-magnet synchronous motor system under a rotating speed synchronous control target; the method comprises the following steps: constructing an augmented system according to a dynamic system of a dual permanent magnet synchronous motor system, designing an improved sliding mode function, and constructing a fault diagnosis sliding mode observer by using the designed sliding mode function; the rotating speed output estimated by the fault diagnosis sliding-mode observer is compared with a motor rotating speed actual measurement value to generate a residual signal, and a fault estimation algorithm is designed; designing an active fault-tolerant control strategy in inner ring controllers of the fault motor and the healthy motor according to fault information obtained by a fault estimation algorithm; on the basis of active fault-tolerant control, a linear quadratic regulation algorithm is introduced to optimize the speed synchronization error of the double permanent magnet synchronous motors. The fused fault-tolerant control reduces the influence of the fault on the system, and solves the problem that the performance of the whole motor system is reduced due to the fact that the fault is transmitted through a synchronous control loop.

This invention specifically relates to a non-paper-based real-time time physical signal generation system and method based on an optical clock. The system includes: an optical clock, a microwave clock, an optical frequency comb, a counter, a phase fine-tuning instrument, and a PC. The method includes: recording and storing the frequency difference data of the optical clock and the microwave clock in real time through the counter; establishing communication with the storage device on the PC to read the frequency difference data in real time and filtering the frequency difference data to remove outliers; employing a linear quadratic Gaussian optimal driving algorithm combined with Kalman filtering to calculate the driving adjustment amount for the microwave clock in real time based on the filtered frequency difference data stream; establishing communication with the phase fine-tuning instrument, inputting the driving adjustment amount to the phase fine-tuning instrument, and performing real-time driving adjustment of the microwave signal; the phase fine-tuning instrument outputs a frequency domain or time domain signal, which is the real-time time physical signal after optical clock driving. This invention generates a real-time, non-paper-based, high-precision time physical signal, which can be widely used in the field of optical frequency standards.