17results about How to "Improve trajectory tracking accuracy" patented technology

The embodiment of the invention relates to the field of servo control, in particular to a servo system, a motor control method based on the servo system, equipment and a medium, and the system comprises a position loop control module, a speed loop control module and a motor module which are connected in sequence; the position loop control module comprises a position adjusting module, a speed feed-forward compensator and a speed error device; the position adjusting module is used for generating a speed instruction according to an obtained position instruction sent by the upper computer and a feedback position of the motor module; the speed feed-forward compensator is used for determining the compensation amount of the speed instruction according to the position instruction and a compensation formula; the speed error device is used for superposing the compensation amount and the speed instruction as a target speed instruction; the speed loop control module is used for receiving the target speed instruction, obtaining the feedback speed of a servo motor in the motor module, and outputting an electromagnetic torque instruction according to the target speed instruction and the feedback speed to drive the servo motor to work. By controlling the servo motor, the position dynamic following error can be reduced.

The invention discloses an improved model prediction control method for trajectory tracking of an unmanned vehicle, and belongs to the technical field of trajectory tracking control of unmanned vehicles, and the method comprises the steps: S1, building a three-degree-of-freedom nonlinear dynamic model of a vehicle; s2, making a linear dynamic model of the tire in combination with a magic tire formula; s3, formulating an objective function and constraint conditions of the model prediction control method; and S4, formulating a rule of a fuzzy controller, and enabling the unmanned vehicle to adaptively adjust a time domain coefficient according to the fuzzy rule and complete trajectory tracking. According to the method, the three-degree-of-freedom nonlinear dynamic model of the vehicle and the tire model based on the magic formula are established, the fuzzy control rule is introduced, and the improved MPC algorithm is designed, so that the unmanned vehicle can be adaptively adjusted according to the road, the preset tracking trajectory can be effectively tracked under the low-speed tracking working condition, the tracking error is reduced, and the tracking efficiency is improved. And the trajectory tracking precision is improved.

This invention discloses a model predictive trajectory tracking control method for unmanned surface vessels (USVs) based on adaptive line-of-sight guidance, belonging to the field of ship motion control technology. Addressing issues such as curve cutting, target point jumps, abrupt changes in control input, and circling near the endpoint during trajectory tracking, this invention combines adaptive line-of-sight guidance with model predictive control: First, a three-degree-of-freedom kinematic and dynamic model of the USV is established, and a path progress index and local search window are constructed based on the reference trajectory to avoid virtual target point jumps caused by global nearest point search; second, an adaptive forward sight distance adjustment mechanism based on path curvature, lateral error, and speed is designed to dynamically determine the LOS virtual target point and the desired heading angle, improving trajectory regression capability in curves and under large error conditions; finally, lateral error, heading error, velocity error, bow angular velocity, control input, and control increment are introduced into the model predictive control cost function, and combined with terminal guidance, velocity decay, and closed trajectory completion criteria, to achieve roll optimization control under constraints. Through experiments tracking ordinary smooth curves and inverted figure-eight shaped trajectories, this invention demonstrates that it can improve the trajectory tracking accuracy, control smoothness, and mission completion reliability of unmanned surface vessels (USVs) while satisfying the constraints of propulsion, yaw moment, and control increment. It is suitable for high-precision trajectory tracking control of USVs under complex curved paths and closed trajectory conditions.

The present invention discloses a collaborative robot trajectory tracking control method and system, the system comprising: establishing a joint space dynamics model of an n-degree-of-freedom collaborative robot and establishing an expression of joint tracking error; establishing a sliding surface of a sliding mode controller; deriving a robust control rate of the collaborative robot based on sliding mode control; establishing an expression of the required updated parameter vector error; deriving an adaptive fuzzy control rate based on a sliding mode controller; establishing an expression of an external load observation error; and deriving a nonlinear state observer based on an external load observation error. The present invention combines a sliding mode controller and a nonlinear state observer to establish an adaptive fuzzy controller with an external disturbance observer, and improves the trajectory tracking control accuracy of the collaborative robot without the need to know the dynamic model parameters of the collaborative robot and the external load measurement is inaccurate. And because the method of the present invention is combined with a sliding mode controller, robustness to external disturbances can be achieved.

The invention provides a mechanical arm trajectory tracking control method fusing model prediction and sliding mode control. The mechanical arm trajectory tracking control method comprises the steps that a discrete sliding mode controller is constructed, and a discrete sliding mode surface based on trajectory tracking errors is designed; calculating a sliding mode control law; constructing a dynamics prediction model based on an echo state network, and performing online learning and updating by taking historical state data and a sliding mode control law of the mechanical arm as input so as to predict a state track of the mechanical arm in a future time domain; constructing a model prediction controller embedded with sliding mode control, and introducing a prediction state and a sliding mode control law into an optimization objective function of the model prediction controller; and under the model prediction controller, an optimization problem is converted into a quadratic programming problem to be solved, a smooth optimization control quantity meeting physical constraints is obtained, and the control quantity acts on the mechanical arm system. The high-frequency buffeting problem of sliding mode control is effectively solved, and meanwhile high-precision trajectory tracking of the mechanical arm under strong nonlinear interference is guaranteed.

The invention discloses a self-adaptive vehicle trajectory tracking control method and system suitable for a curved surface terrain. The method comprises the following steps: S1, obtaining a point cloud map and a reference trajectory of a current environment; s2, selecting key points on the reference trajectory, and fitting a local ground equation around the key points; and S3, establishing a manifold model prediction controller by taking the position, the speed and the yaw angle of the vehicle as state variables and taking the longitudinal acceleration and the front wheel rotation angle of the vehicle as control variables. And S4, adjusting the target function weight of the manifold model prediction controller in combination with the curvature of the current position of the reference trajectory. And S5, in combination with the current speed of the vehicle and the transverse error between the vehicle and the reference trajectory, the PID controller is used for compensating the acceleration output by the controller and the front wheel turning angle. Compared with an existing trajectory tracking method, the method provided by the invention has the advantage that the trajectory tracking precision and control robustness of the vehicle on the complex curved surface terrain can be remarkably improved.

The invention discloses a modular mechanical arm non-zero sum game optimal tracking control method based on a control-input-free performance index function, and belongs to the field of robot control algorithms. The method comprises the steps that a dynamic model of the modular mechanical arm system is established; constructing a performance index function and a Hamiltonian function which only contain a state error item and do not contain a control input item; based on a non-zero sum game theory, all joint modules are regarded as game participants, and an optimal control law is exported; a neural network approximation performance index function is used for online learning and network weight updating, and optimal tracking control without an accurate model is realized. According to the method, the problem that in traditional adaptive dynamic planning, due to the fact that a performance index function contains control input, the tracking problem is infinite and cannot be optimized is solved, the precision and stability of trajectory tracking of the modular mechanical arm are improved, the control torque is continuous and smooth, and the joint energy loss is reduced.

The application discloses a kind of based on improved model predictive control unmanned ship trajectory tracking control method and device.The method includes, H-PINN method is introduced into unmanned ship hydrodynamics model, obtains the simplified hydrodynamics model of unmanned ship;Based on the prediction model of the trajectory tracking of unmanned ship obtained in simplified hydrodynamics model, the expected trajectory and control quantity of model predictive control MPC are designed using differential flatness theory, and stability analysis is designed according to Lyapunov function.The method improves model predictive control using differential flatness theory, and Lyapunov function optimization analysis is combined, improves trajectory tracking precision, reduces the change amplitude of control quantity, so that the speed change in navigation process is more smooth.

This invention belongs to the technical field of robotic arm motion planning and discloses a robotic arm motion planning method and system based on an error-driven adaptive gradient model. The method includes: S1 establishing the joint velocity at two adjacent moments with respect to the adaptive coefficient λ based on a Lyapunov function. n The gradient relationship is obtained; S2. The joint velocity at time n+1 is calculated using the gradient relationship, and the error between the calculated joint velocity at time n+1 and the adaptive coefficient is updated using the central finite difference method; S3. n = n+1; return to step S2 until the joint velocities at all times are obtained; S4. The joint position is calculated using the relationship between the joint velocity and position; the velocity of the end effector is calculated using the robotic arm motion planning model and the joint velocities at each time, thereby realizing the motion planning of the robotic arm. This invention solves the problem of low computational efficiency in robotic arm motion planning.

Disclosed are a robust iterative learning model predictive controller for a magnetic levitation planar motor and its application. For the repeated motion of the X-axis of the magnetic levitation planar motor, a system dynamics model and a reference trajectory model with bounded disturbances that satisfy the Lipschitz continuity condition are established, and a tracking error model is obtained based on the system dynamics model and the reference trajectory model; according to the model predictive control principle, a tracking error model combined with the disturbance of the last iteration cycle is used as a learning prediction model, the upper bound of the second norm of the tracking error based on the Lipschitz continuity characteristic of the disturbance is used as a cost function, and a robust positive invariant set based on the state feedback control rate and bounded disturbance is used as a constraint condition to construct a magnetic levitation planar motor learning optimization control problem. The present invention solves the problem that model predictive control is easily affected by disturbances in actual magnetic levitation systems, and the control method provided by the present invention is used to improve the tracking performance of the magnetic levitation planar motor in the presence of external disturbances.

The invention discloses a feedforward compensation method and system based on acceleration feedback, and the method comprises the steps: obtaining and analyzing a motion instruction of a controller, determining expected speed information and expected acceleration information of a controlled object, determining an expected feedforward resultant force based on the expected speed information and the expected acceleration information, and determining a feedforward control signal based on the expected feedforward resultant force; determining the acceleration feedback force of the controlled object based on the real-time acceleration information of the controlled object; determining an acceleration feedback compensation force based on the acceleration feedback force, and determining an acceleration feedback compensation signal of the controlled object based on the acceleration feedback compensation force; and synthesizing the feedforward control signal and the acceleration feedback compensation signal to obtain a motion control instruction of the controlled object, and controlling the controlled object to move according to the motion control instruction. According to the method, open-loop feedforward prediction and closed-loop acceleration observation feedback are deeply fused, the robustness of external interference and internal reference change is enhanced, mechanical resonance is effectively inhibited, and the trajectory tracking precision, the anti-interference capability and the motion stability are improved.

This invention discloses a model prediction trajectory tracking control method for a multi-joint robotic arm based on Q-learning, belonging to the field of robotic arm control technology. The method includes the following steps: S1, linearizing the nonlinear dynamic model of the multi-joint robotic arm to construct a discrete-time state-space model; S2, using the cost function of the linear MPC as an approximator for the action value function in a reinforcement learning framework; S3, introducing an adaptive weighted perturbation exploration strategy based on state error to replace traditional random exploration, generating control actions that satisfy the joint position and torque constraints of the robotic arm; S4, under the constraint of system stability, using the discrete model from S1 as the dynamic basis and the cost function from S2 as the basis for evaluating the value of candidate actions, combined with the candidate action set generated by the adaptive weighted perturbation exploration strategy in S3, adjusting the parameters of the MPC based on the Q-learning algorithm. This invention improves the trajectory tracking performance of multi-joint robotic arms under model uncertainty conditions.

The application discloses a kind of photographic unmanned aerial vehicle stabilizing gimbals adaptive robust constraint following control method, belong to unmanned aerial vehicle control technical field, comprising: first based on establishing gimbals system kinematics model, through Lagrange equation construction includes all working condition dynamics model with uncertainty and disturbance;Photographic lens desired trajectory is converted into second-order constraint equation, and a constraint tracking error index is constructed;Design includes nominal control item and adaptive robust compensation item Compound controller, solve servo constraint force, while embedding fixed threshold anti-shake strategy;Unknown uncertainty of system is estimated in real time by adaptive law with leakage term, and control compensation strength is updated;Finally, the system stability is verified by Lyapunov theory, and simulation and physical testing are completed.The application improves the gimbals trajectory tracking accuracy and motion stability, solves the problem of weak control buffeting, weak anti-interference ability and parameter drift in the prior art, and has strong engineering practicability.

The invention discloses an ROV trajectory tracking optimization control method for a complex environment, and relates to the technical field of underwater robot control, and the method comprises the steps: building a north-east coordinate system as a fixed coordinate system, building an underwater robot body coordinate system as a motion coordinate system, defining six-degree-of-freedom motion parameters, and building a complete dynamic model; according to the actual operation condition and the structural characteristics of the underwater robot, the complete dynamic model is simplified into a four-degree-of-freedom control model, and thrust distribution is carried out according to a direct logic distribution method; designing a double-closed-loop sliding mode surface containing an integral term and an improved index reaching law, and using a hyperbolic tangent function as a switching term to complete the improved design of an outer-loop position controller and an inner-loop speed controller; and designing a multi-objective fitness function, and carrying out iterative optimization on parameters of the sliding mode controller based on an improved crow search algorithm to obtain an optimal parameter combination adaptive to thrust limitation and complex trajectories. According to the invention, the tracking precision of the ROV in a complex environment is effectively improved.

The application discloses a high-precision PID trajectory tracking control method for a space manipulator, and relates to the technical field of spacecraft control. The method first establishes a dynamic model of the rotating motion of the manipulator containing system uncertainty; a nonlinear disturbance observer is constructed to estimate the unmodeled dynamics and external disturbances of the system in real time; a PID compound controller is designed in combination with the uncertainty estimation value; a PID parameter optimization problem is established with the minimum trajectory tracking error as a target and the joint angle, angular velocity and control torque as physical constraints; the physical constraints are converted into finite-dimensional constraints by using a constraint transcription method, and the gradient of the objective function and the constraints with respect to the PID parameters is calculated, and the optimal PID parameters are solved by an iterative optimization algorithm. The application realizes high-precision trajectory tracking of the free-flying space manipulator by using the compound control architecture and parameter optimization, effectively suppresses system uncertainty, and ensures strict satisfaction of multiple physical constraints, and has good engineering generality.

This invention relates to the field of intelligent sensing and control equipment technology, and discloses an autonomous mobile control method and system for an IT maintenance robot, including: synchronously acquiring the environmental ranging sequence of the movement direction, the local temperature field gradient, the airflow distribution vector, and the real-time infrared thermal image; determining the longitudinal pose phase deviation by comparing the periodic characteristics of the ranging sequence with the phase of the width reference; determining the thermodynamic equilibrium axis offset rate using the spatial change rate of the temperature field gradient and mapping it to the heading offset compensation amount; adjusting the coupling weight coefficient of the temperature field gradient and the airflow distribution vector according to the temperature rise rate; and resetting the longitudinal inspection origin according to the image grayscale centroid when the pose phase deviation exceeds the limit. This invention breaks the geometric symmetry constraint through the physical field gradient feedforward correction mechanism, eliminates the positioning aliasing and heading control oscillation generated by the inspection robot in a homogeneous space, and ensures smooth driving in an environment without external beacons.

The application discloses a kind of considering the motion control method of paddle drive unmanned vehicle of actuator dynamics, belong to unmanned vehicle formation control technical field.The existing paddle drive unmanned vehicle is difficult to effectively control in multi-modal switching and high dynamic operation.The geometric relationship between the follower and the follower is established to establish a tracking error model;Construct a finite time preset performance function embedded in overshoot and stable time and other classical control indicators, and perform error transformation through tangent function;Based on the backstepping method, an adaptive control law is designed, a first-order command filter is introduced to suppress calculation expansion, and RBF neural network is used to compensate model uncertainty and external disturbance online.While considering the physical limitations of the actuator, high-precision and strong-robustness motion tracking control of the paddle-driven unmanned vehicle in complex environments is achieved.