Robot MPC Torque-Rate Control for Jerk and Vibration Limits
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Solution Overview
Problem
Existing robot motion control techniques, such as classical PID control, struggle to accurately manage dynamic response changes due to configuration variations and payload dynamics, often leading to instability and violation of mechanical constraints like jerk limits.
Innovation Solution
A model predictive control (MPC) system that incorporates a robot dynamics model with torque rate control and inequality constraints to manage jerk and end tooling oscillation, ensuring accurate control of robot joints while adhering to mechanical constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If classical PID control is used, then the control system is simple to implement, but it becomes unstable when large input steps are defined and cannot adapt to changes in dynamic response due to configuration changes
Solution Approach 1:
The patent implements model predictive control (MPC) which dynamically adapts the control strategy based on the current robot configuration and predicted future states. The controller continuously updates the optimization model to reflect changing inertia properties and natural frequencies as the robot transitions between compact and extended configurations, resolving the contradiction between simple control implementation and control stability under varying conditions.
Solution Approach 2:
The patent changes the control parameters from fixed PID gains to time-varying torque commands optimized through predictive control. The MPC controller adjusts torque references and rate limits based on real-time configuration parameters, allowing the system to adapt to dynamic response changes without requiring complex retuning of control parameters.
2Productivity
If the robot is moved as quickly as possible from start point to destination point, then productivity increases, but high accelerations cause oscillation dynamics that affect robot dynamic response and require accurate modeling
Solution Approach 1:
The patent uses model predictive control to perform preliminary calculations of optimal torque trajectories that account for oscillation dynamics before executing the motion. The controller predicts future robot states and pre-computes torque commands that will achieve high-speed motion while anticipating and compensating for oscillation effects, allowing fast motion without sacrificing dynamic response accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the actual robot state is continuously compared with the predicted state from the optimization model. The controller uses this feedback to adjust subsequent torque commands, ensuring that high-speed motion maintains accuracy despite oscillation dynamics. The feedback loop compensates for deviations caused by rapid accelerations and payload variations.
3Device complexity
If torque rate limits are not controlled, then the control calculations are simpler, but joints may exceed mechanical constraints such as permissible ranges of joint acceleration and jerk
Solution Approach 1:
The patent merges the torque control and torque rate control into a unified model predictive control framework. By combining these constraints in the optimization problem, the controller simultaneously determines optimal torque commands that satisfy both torque limits and torque rate limits, ensuring joint constraints are met without requiring separate control loops or complex post-processing.
Solution Approach 2:
The patent dynamically adjusts torque rate limits based on the current robot configuration and motion requirements. The MPC controller adapts the rate constraints in real-time, allowing simpler control calculations when constraints are not active while maintaining precision when constraints become relevant during high-dynamic maneuvers or configuration changes.
Data Source
AI summary
A method and system for robot motion control using a model predictive control (MPC) technique including torque rate control and suppression of end tooling oscillation. An MPC module includes a robot dynamics model which inherently reflects response nonlinearities associated with changes in robot configuration, and an optimization solver having an objective function with a torque rate term and inequality constraints defining bounds on both torque and torque rate. The torque rate control in the MPC module provides an effective means of controlling jerk in robot joints, while accurately modeling robot dynamics as the robot changes configuration during a motion program. End tooling oscillation dynamics may also be included in the MPC objective function and constraints in order to automatically control end tooling vibration in the calculations of the MPC module.