10results about How to "Improve trajectory tracking accuracy" patented technology

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 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.

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 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.