Servo Actuator Gain Self-Tuning With Minimal Motor Movement
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Solution Overview
Problem
Existing servo actuators require extensive motor movement and time for automatic gain tuning, which is inefficient and not feasible in environments where computer equipment is restricted or where controlled components have varying characteristics, leading to increased labor costs and reduced efficiency.
Innovation Solution
A fast self-tuning method for servo actuators that computes an estimated control gain using current and position feedback signals, allowing for quick gain tuning with minimal motor movement, without the need for extensive data storage or additional computer equipment, by employing a CPU with torque and acceleration estimating modules and a gain computing module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the automatic gain tuning function is implemented using computer software with user interface, then users can be guided to set parameters step-by-step, but it cannot be used in environments where computer equipment is restricted or software installation is forbidden
Solution Approach 1:
The servo actuator performs self-diagnosis and self-tuning by automatically identifying motor parameters (inertia, friction, torque constants) through built-in testing routines. The system generates test signals, measures responses, and computes optimal control parameters without external computer assistance, enabling autonomous operation in restricted environments
Solution Approach 2:
The patent replaces the computer software-based tuning system with a direct embedded control approach. The servo actuator's internal processor directly executes parameter identification algorithms and gain tuning computations, substituting the external computer-software-user interface chain with an integrated self-contained system
2Extent of automation
If the controlled component moves between two points for gain tuning, then automatic tuning can be performed, but it requires 2-3 minutes and the motor must rotate at least one to three circles
Solution Approach 1:
The system performs parameter identification using partial movement ranges rather than complete multi-circle rotations. By using identification signals that generate sufficient response data within smaller angular displacements, the system achieves adequate tuning accuracy without requiring the motor to rotate one to three full circles
Solution Approach 2:
The servo actuator performs parameter identification and gain tuning automatically during initial power-up or system startup before actual production work begins. This preliminary automated tuning prepares the system for optimal performance without interrupting subsequent operational cycles
3Extent of automation
If the controlled component moves between two points for gain tuning, then automatic tuning can be performed, but it is inefficient for machines with controlled components having different characteristics at different positions or limited movement range
Solution Approach 1:
The system adapts to different machine types and movement constraints by dynamically adjusting identification signal parameters (frequency, amplitude, duration) and tuning algorithms. The embedded processor selects appropriate identification methods based on detected system characteristics, enabling effective tuning for robotic arms, linear actuators, and machines with limited rotation ranges
4Extent of automation
If extensive motor movement is performed for gain tuning, then automatic tuning can be completed, but it reduces productivity and increases time loss
Solution Approach 1:
The system achieves sufficient parameter identification accuracy using minimal motor movement and shortened identification signal durations. By optimizing the balance between signal excitation and measurement accuracy, the system completes tuning in seconds rather than minutes, significantly reducing productivity loss during setup
Data Source
AI summary
A fast self-tuning method of gain applied to a servo actuator connected with a motor is disclosed and includes following steps: retrieving a current-feedback information of the motor to compute a torque estimated value; retrieving a position-feedback information of the motor to compute an acceleration estimated value; computing a system inertia based on the torque estimated value and the acceleration estimated value, wherein the system inertia indicates an inertia of the motor carrying a specific load; computing an estimated control gain for the servo actuator based on the system inertia; and, performing a self-tuning procedure by the servo actuator in accordance with the estimated control gain.