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

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

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.

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 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 controllable asymmetric foldable fixed wing aircraft and an attitude control algorithm thereof. The aircraft is provided with left and right independent foldable main wing structures, and the attitude control freedom degree and maneuverability are improved through asymmetric folding. According to the matched control method, the advantages of a linear quadratic regulator (LQR) and model predictive control (MPC) are fused, an inner and outer ring cooperative control framework is constructed, and high-response and high-robustness control over the roll angle, the pitch angle, the yaw angle and the flight speed is achieved. The system has control input constraint processing capability and disturbance adaptability, is suitable for a complex flight environment and a high-dynamic task scene, and has a good engineering application prospect.

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 present invention belongs to the field of trajectory control optimization, and specifically relates to an optimization method for a gliding missile controller based on an improved Gray Wolf algorithm. The method comprises the following steps: Step (1): Based on the dynamic characteristics analysis of the gliding missile, a disturbance motion equation of the missile body is established and simplified; Step (2): Based on the simplified disturbance motion equation of the missile body, under the conditions of zero interference and zero initialization, a suitable input control quantity and state vector are selected to construct its longitudinal state equation; Step (3): Based on the system state equation, an LQR linear quadratic regulator is constructed: the parameters of the weighting matrix Q matrix and the R matrix in the LQR linear quadratic regulator are optimized using the Gray Wolf algorithm with a nonlinear convergence factor to obtain the feedback gain matrix K, thereby improving the system response time and the stability of the new control system. The present invention optimizes the trajectory and improves the system response by selecting some parameters in the longitudinal disturbance equation as state variables and optimizing the feedback gain, thereby improving the stability margin of the system.

The present application provides an autonomous driving control method, electronic device, vehicle, and storage medium. The method includes: obtaining the vehicle's current state data, which includes at least the vehicle's driving mode, current speed, and current position; when the driving mode is a specified type of autonomous driving mode, using a linear quadratic regulator optimization module based on the current speed and current position, as well as planning parameters corresponding to the autonomous driving mode, to obtain optimized control parameters; and controlling the vehicle's motion state using a PID controller based on the control parameters. In this way, autonomous driving planning control can be adaptively adjusted as the vehicle's operating state changes, which is beneficial for improving the effectiveness of autonomous driving planning control.

This application discloses a trajectory planning method, apparatus, device, and storage medium based on an incremental iterative linear quadratic regulator solution, relating to the field of intelligent driving technology. The method comprises: constructing an initial model based on a Cartesian coordinate system; performing objective function design and multi-circle model-based constraint processing on the initial model to obtain a target model; and performing incremental iterative linear quadratic regulator solution and trajectory post-processing on the target model to obtain a target trajectory. Based on the concept of spatiotemporal joint planning, the application improves the accuracy and applicability of trajectory planning and reduces the impact of curvature fluctuations by constructing a vehicle model and performing incremental iLQR solution.

The invention relates to a trajectory optimization method and device, electronic equipment and a storage medium. The trajectory optimization method comprises the steps of determining whether a sudden change trajectory point with constraint information sudden change exists on a reference trajectory or not according to detection data of a vehicle-mounted sensor; if the abrupt change track point with the constraint information abrupt change exists, determining target information of a non-abrupt change track point according to the constraint information of the abrupt change track point and the constraint information of the non-abrupt change track point located in front of the abrupt change track point on the reference track; according to the target information of the non-abrupt-change track point, optimizing the non-abrupt-change track point before the abrupt-change track point through an iterative linear quadratic regulator (ILQR) to obtain an optimized track point; and determining an optimized track corresponding to the reference track according to the optimized track point. The problem of solution convergence caused by abrupt change of constraint information of trajectory points in the trajectory optimization process is solved, and the solution performance of an optimizer in a constraint information abrupt change scene is ensured.

This invention provides a dual-motor steer-by-wire system and its active fault-tolerant control method. The method includes: a front wheel steering angle tracking controller, a vehicle stability controller, and a nonlinear model control method based on a variable parameter (LPV) model. The front wheel steering angle tracking controller and the stability controller are designed based on a linear quadratic regulator (LQR) and an optimal control algorithm, including: solving for the ideal front wheel steering angle; establishing a dynamic model of the dual-motor steering actuation system; and designing the steering angle tracking controller and the stability controller based on the ideal front wheel steering angle and the steering system dynamic model. The dual-motor active fault-tolerant control includes: comparing the control torque output by the dual-motor torque sensors with that output by the front wheel steering angle tracking controller, calculating the fault coefficient, and optimizing the steering actuator model into a variable parameter (LPV) nonlinear structure containing motor fault parameters. This invention can ensure vehicle safety after a dual-motor failure.

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.