Motor Trajectory Estimation With Dynamic Constraint Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional control strategies for precision motion systems face challenges in balancing precision with computational efficiency, leading to either excessive computational intensity or conservative safety margins that reduce productivity and speed.
Innovation Solution
A motion control system that generates dynamically feasible trajectories by using a hybrid approach, incorporating a motion planner, tracking controller, and amplifier, which considers motor dynamics and constraints to optimize torque, velocity, acceleration, and voltage, and employs a dynamic parameter estimator to adaptively adjust motor parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optimization-based control strategies are used to generate optimal motion trajectories, then manufacturing precision is improved, but device complexity increases due to computational intensity
Solution Approach 1:
The system pre-computes and stores optimal trajectories offline using optimization-based methods. During real-time operation, the pre-computed trajectories are retrieved and executed without requiring intensive online computation, thus achieving high precision while reducing real-time computational complexity
Solution Approach 2:
The system dynamically adjusts trajectory parameters based on real-time motor state feedback. By combining offline optimization with online adaptive adjustment, the system maintains high trajectory accuracy while keeping real-time computational requirements manageable through dynamic parameter tuning rather than full re-optimization
2Device complexity
If parameterized trajectory control strategies are used to reduce computational complexity, then device complexity is reduced, but manufacturing precision deteriorates due to conservative safety margins
Solution Approach 1:
Optimal trajectory parameters are pre-computed offline and stored in a database. During real-time operation, the system retrieves these pre-optimized parameters and applies them directly, eliminating the need for conservative safety margins while maintaining computational efficiency
Solution Approach 2:
The system incorporates real-time feedback on motor state (current, voltage, temperature) to dynamically adjust trajectory execution. This feedback mechanism allows the system to safely operate at the boundaries of motor capabilities without requiring conservative margins, thus improving precision while maintaining computational efficiency
3Reliability
If large safety margins are introduced to ensure dynamic feasibility, then reliability is improved, but productivity decreases due to reduced speed
Solution Approach 1:
Real-time feedback on motor state (current, voltage, temperature) enables the system to dynamically verify dynamic feasibility during operation. This continuous monitoring allows the system to operate with minimal safety margins while ensuring reliability through immediate detection and correction of feasibility violations
Solution Approach 2:
The system dynamically adjusts trajectory parameters based on real-time motor capabilities. By continuously adapting to the actual motor state rather than using fixed conservative margins, the system achieves both high reliability and maximum productivity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves faster and more accurate motor control by generating trajectories that fully utilize motor capabilities while ensuring dynamic feasibility, reducing conservatism and enhancing productivity.
Implementation Method 1
The amplifier converts the control signal to voltage supplied to motor
Implementation Method 2
the motor drives the load with electromagnetic torque
Data Source
AI summary
A controller for controlling a motor comprises a processor and a memory having instructions stored thereon that, when executed by the processor, cause the controller to collect a feedback signal indicative of the current state of an operation of the motor and determine current constraints on parameters of the operation of the motor using model of dynamics of the motor connecting the current state of the motor with the current constraints on the operation. The processor is further configured to determine a control trajectory of the operation of the motor based on the current constraints on parameters of the operation of the motor using a parametrized estimation agnostic to the dynamics of the motor and control the operation of the motor according to the control trajectory.