Robotic Arm Trajectory Tracking Without a Kinematic Model
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Solution Overview
Problem
Existing robotic arm control techniques rely on kinematic models, which are often unknown or difficult to obtain accurately due to parameter uncertainty, leading to lower accuracy in trajectory tracking.
Innovation Solution
A kinematics model-free trajectory tracking method using an applied gradient neural network to obtain the joint state vector and Jacobian matrix from sensor measurements and target trajectory information, allowing for accurate tracking without knowing the robotic arm's model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If kinematic model-based control techniques are used, then control structure is established, but trajectory tracking accuracy deteriorates due to parameter uncertainty and model inaccuracy
Solution Approach 1:
The patent replaces the traditional model-based control mechanism with a sensor-feedback-driven control mechanism. Instead of relying on the Jacobian matrix and kinematic models to compute control inputs, the system uses sensor measurements of actual joint positions and velocities directly in the control law, eliminating the need for accurate model parameters while maintaining control structure.
Solution Approach 2:
The system uses its own sensor measurements of actual state to correct and adapt the control process in real-time. The feedback from sensors about actual joint positions and velocities allows the system to self-correct trajectory deviations without requiring external model information, making the control accurate despite parameter uncertainties.
2Manufacturing precision
If accurate kinematic model parameters are obtained, then trajectory tracking accuracy is improved, but system complexity and measurement requirements increase
Solution Approach 1:
The patent extracts and eliminates the requirement for accurate kinematic model parameters from the control system. By removing the dependency on the Jacobian matrix and model-based computations, the system simplifies the overall structure while maintaining or improving trajectory tracking accuracy through direct sensor feedback.
Solution Approach 2:
The patent introduces sensor feedback as an intermediary between the actual system state and the control input. Instead of using complex model computations as intermediaries, the sensor measurements directly mediate the control process, simplifying the system while improving accuracy through real-time feedback.
3Ease of operation
If model parameters are uncertain or unknown, then ease of system setup is improved, but trajectory tracking accuracy deteriorates
Solution Approach 1:
The patent implements continuous feedback from sensors that measure actual joint positions and velocities. This feedback loop allows the system to automatically compensate for parameter uncertainties and model inaccuracies by constantly comparing actual state with desired state and adjusting control inputs accordingly, maintaining high accuracy without requiring precise model parameters.
Data Source
AI summary
A kinematics model-free trajectory tracking method for a robotic arm includes the following steps. Obtain an actual trajectory equation ra(t) of the robotic arm at time t according to a sensor, and combines the actual trajectory equation ra(t) with a predetermined target trajectory equation rd(t) to obtain a first error function e(t). Obtain a differential equation (I) of a state change rate of a driver of the robotic arm. Obtain a second error function ε(t). Pass the second error function c(t) through the applied gradient neural network to obtain equation (IV). Jointly solve equation (I) and equation (IV) to obtain an joint state vector θ(t) of the robotic arm. Drive a motion of the robotic arm by a controller according to the joint state vector θ(t) of the robotic arm to complete trajectory tracking.