This invention describes a system that uses machine learning to fine-tune how a servo motor operates, especially when its movements or commands are restricted. If the motor's commands go beyond safe limits, the system tells the machine learning part, which then uses this information to learn and find better ways to control the motor without exceeding those limits, specifically for machines like robots or machine tools.
Why it matters: Since 2019, reinforcement learning algorithms have significantly improved in handling real-time system constraints and feedback, making this adaptive control approach more robust and efficient for industrial applications. The increased availability of powerful edge computing also supports its real-time deployment.
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