101 results about "Linear-quadratic regulator" patented technology

The invention provides a vehicle trajectory tracking control method and system, an unmanned vehicle and a readable medium, and belongs to the field of automatic driving. The vehicle trajectory tracking control method comprises the steps that the longitudinal speed, the steering state and the reference trajectory of a vehicle are obtained; constructing an error model of the vehicle relative to the reference trajectory, wherein the error model at least represents a transverse error and a course error of the vehicle relative to the reference trajectory; performing nonlinear optimization on the error model by using an iterative linear quadratic regulator algorithm, and generating a control input sequence for vehicle transverse control in a prediction time domain; and mapping the control input sequence into a steering control signal of the vehicle, so that the vehicle adjusts a driving track based on the steering control signal. According to the vehicle trajectory tracking control method and system, the unmanned vehicle and the readable medium provided by the invention, the dependence on kinetic parameters difficult to identify can be weakened, the nonlinear precision and the vehicle-mounted real-time performance are considered, and the trajectory tracking precision and convergence are improved.

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

The invention relates to an underwater cleaning robot positioning and tracking method. Positioning comprises the steps that data of multiple sensors on an underwater cleaning robot are fused through an extended Kalman filter, and coordinates of the underwater cleaning robot on the surface of a ship body are obtained after sensor measurement noise is filtered through a first-order low-pass filtering algorithm. The tracking method comprises the step of realizing the path tracking of the underwater cleaning robot based on the positioning method in combination with an iterative linear quadratic regulator (ILQR). According to the invention, a multi-sensor fusion scheme of the encoder, the IMU and the depth meter is innovatively provided, and the positioning problem of the underwater cleaning robot in a wall attaching state is solved. And double-plane differential treatment is adopted, so that the working state of the robot on the ship wall or the ship bottom is better fitted, and positioning is more accurate. A target point updating mechanism of geometric distance constraint is provided, the nearest neighbor point of the reference trajectory is adjusted in real time, and the problem of tracking lag caused by updating rate mismatch in a traditional method is solved.

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

The invention belongs to the technical field of unmanned aerial vehicle attitude control. According to the fire-fighting unmanned aerial vehicle attitude control method and system, the medium, the product and the equipment, the process noise covariance and the measurement noise covariance of a self-adaptive Kalman filter are dynamically adjusted in combination with weight factors, and the self-adaptive Kalman filter determines the real state of the fire-fighting unmanned aerial vehicle based on sensing data and control input. A linear quadratic regulator is adopted to determine an optimal control gain according to the real state of the fire-fighting unmanned aerial vehicle, and based on an adaptive gain and the optimal control gain, final control input is determined to control a motor of the fire-fighting unmanned aerial vehicle to work; according to the method, the control robustness in a complex environment is remarkably improved, high-precision tracking and quick response are realized, the dependence on a precise model is reduced, the sensor interference resistance is enhanced, the expansibility is high, and the method is suitable for multiple scenes.

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

The invention relates to the technical field of low-altitude aircraft attitude control, in particular to a low-altitude aircraft attitude control system based on intelligent information data analysis. The method comprises the following steps: a data acquisition and storage unit acquires aircraft data and environmental parameter data; the intelligent information analysis unit analyzes the real-time attitude of the aircraft by using a nonlinear dynamics technology and a singular perturbation theory technology, and judges whether the aircraft is in a safe state or not by using a singular perturbation analysis technology; the single aircraft control unit generates an optimal control strategy by using a linear quadratic regulator in combination with an advanced attitude control algorithm; the multi-aircraft control unit adopts a distributed control strategy to control coordinated flight among the aircrafts; and the control display terminal unit is used for providing an integrated human-computer interaction interface. According to the low-altitude aircraft attitude control system based on intelligent information data analysis, nonlinear dynamics and singular perturbation theories are combined, and cooperative flight control of a single aircraft or multiple aircrafts is realized by adopting a distributed control architecture.

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

The invention relates to a PID (Proportion Integration Differentiation) control inertial servo system and a control method. The PID control inertial servo system comprises a controller, a control circuit, a driving circuit, a motor, a potentiometer, a system clock, a timer, an analog-to-digital converter, a plurality of serial ports, a communication bus, main control equipment, a steering engine, a fuzzy PID control module, a sampling module, a current sensor, a linear quadratic regulator, a PWM (Pulse-Width Modulation) duty ratio module and an integration module, the control circuit comprises a control chip; the driving circuit comprises a first driving circuit and a second driving circuit; the first driving circuit comprises a driving chip; and the second driving circuit is electrically connected with the motor and is provided with a plurality of switches. On the basis of position closed-loop feedback, with the fuzzy PID control algorithm as the core, optimal system parameters are obtained through a linear quadratic regulator, and compared with traditional PID control, fuzzy PID control has higher control precision, stability and response speed.

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

The invention provides a control method and device for a door frame type transfer robot and electronic equipment, and the control method for the door frame type transfer robot comprises the steps: building a component coordinate system according to the structure of the robot, and determining the combined mass center position of the robot when the robot does not carry out the transfer work; the robot is controlled to execute the carrying action, and feature data of the robot in the carrying work are extracted; based on the feature data, obtaining offset compensation of the combined mass center position of the robot in a load state; constructing a kinetic model of the robot, and designing a linear quadratic regulation controller and an active disturbance rejection controller based on the kinetic model; and generating a hybrid control signal for the robot in combination with the linear secondary regulation controller and the active disturbance rejection controller. According to the invention, the robot can keep stable and realize better speed tracking under the condition of load, and the self-adaptive performance of the robot is improved.

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

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

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 provides a method and a device for monitoring a near-earth asteroid, belongs to the technical field of universe navigation, and can realize full-range monitoring of the near-earth asteroid in a relatively large range. The method comprises the following steps: determining a nominal orbit of a detection spacecraft according to an earth revolution orbit, solar center gravity and a genetic algorithm; acquiring a fuel consumption index function of the detection spacecraft; and based on the nominal orbit, the fuel consumption index function and a linear quadratic regulator, determining the control acceleration required by the detection spacecraft to run on the nominal orbit.

The present invention discloses a trajectory tracking control method, system and vehicle-mounted control device for driverless vehicles based on a neural network dynamics model. In the neural network-based dynamics model, a neural network model is designed using a fully connected feedforward neural network. The model has two hidden layers, with 64 neurons in each layer, and uses the ReLU activation function. The input of the model is the vehicle state and control command at the current moment, and the output is the vehicle state at the next moment. This neural network learns the dynamic equation by minimizing the mean square error between the predicted output state and the observed output state. The ILQR controller designed based on the neural network dynamics model contains the control algorithm of the iterative linear quadratic regulator, and obtains the optimal control command by minimizing the cost function to achieve the tracking of the reference trajectory. Compared with the end-to-end control, the proposed solution of the present invention has stronger interpretability, and while ensuring the trajectory tracking accuracy, it takes into account both lateral and longitudinal stability.

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 provides an SERF inertial measurement air chamber temperature self-adaptive decoupling control method and system based on double-sampling and double-control aiming at the characteristics of large time lag and strong coupling of an SERF inertial measurement alkali metal air chamber double-sampling and double-control heating system. Phase angle lag caused by system time lag is suppressed by adopting Smith pre-estimation control, and Smith pre-estimation control deviation caused by double-loop coupling and internal model mismatch is regarded as lumped disturbance to be suppressed by utilizing linear active disturbance rejection control (LADRC). Further dynamically adjusting the bandwidth of a system controller and an observer according to the tracking error through an adaptive linear quadratic regulator (ALQR) so as to enhance the anti-interference capability and the response speed of the system; the method improves the long-term stability and disturbance suppression capability of the alkali metal gas chamber double-production and double-control heating system, has the advantages of fast dynamic parameter response, strong robustness and the like, and can be used for high-precision temperature control of an SERF inertial measurement gas chamber.

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

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

The 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 dynamic response enhanced magnetic disturbance suppression system and method. The system comprises an AD acquisition unit, a magnetic field sensor, a sliding mode observer, a self-adaptive extended linear quadratic regulation controller and a DA output unit. Wherein the adaptive extended linear quadratic regulation controller comprises an adaptive calculation module and an error extended linear quadratic regulator (eLQR); the AD acquisition unit is used for acquiring an analog signal output by the magnetic field sensor and converting the analog signal into a digital signal; the sliding mode observer is used for estimating external magnetic field disturbance and system unmodeled dynamics in real time; the self-adaptive calculation module is used for carrying out self-adaptive calculation on a cost function of an error expansion linear quadratic regulator by using a fuzzy immune regulation method, namely FIS, in combination with a disturbance observation value, and the eLQR after parameter updating generates an optimal control quantity; and the DA output unit is used for converting the control quantity into an analog voltage signal and driving a magnetic compensation coil to generate a reverse compensation magnetic field through a current source.

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 relates to the technical field of fuel cells, and discloses a temperature control method, system and equipment of a combined heat and power system and a medium. The method comprises the following steps: establishing a system model of the combined heat and power system; according to the system model, establishing a state-space equation of the cogeneration system; according to the state-space equation, different current disturbances are applied to the combined heat and power system, and under the different current disturbances, the control action quantity enabling the combined heat and power system to reach a balance working point and the state variable of the combined heat and power system at the balance working point are obtained; constructing a cost function of a linear quadratic regulator by combining a control action quantity for enabling the cogeneration system to reach a balanced working point under different current disturbances and a state variable at the balanced working point; with the minimum value of the cost function as the target, the linear quadratic regulator is adopted to carry out temperature control on the combined heat and power system, and the temperature control effect of the combined heat and power system can be effectively improved.