Virtual Machine Operator Model for Reducing Implement Jerk
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Solution Overview
Problem
Conventional operator models for virtual machine simulations are brittle and require expertise to tune, making it difficult to achieve robust, human-like trajectories, especially when design parameters or boundary conditions change, and they often struggle with chaotic system dynamics.
Innovation Solution
A virtual machine operator model using a neural network-based control system where a virtual operator agent executes control actions, assigns rewards based on new states, and updates the model using a learning algorithm to optimize behavior and reduce high implement jerk movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based logic or PID controllers are used for operator models, then the control structure is simple, but the models become brittle and unsatisfactory when design parameters or boundary conditions change
Solution Approach 1:
The patent applies dynamics by transitioning from static rule-based logic to a dynamic reinforcement learning system where the operator model continuously adapts its control policies based on real-time feedback from simulation outcomes, enabling the model to remain robust when design parameters or boundary conditions change
Solution Approach 2:
The patent implements parameter changes by using reinforcement learning to automatically adjust control parameters and strategies based on simulation performance feedback, allowing the operator model to adapt to different design parameters and boundary conditions without manual retuning
2Reliability
If advanced control methods are used to handle complicated behavior, then the control capability improves, but the design and tuning require careful control theory expertise and complete knowledge of system dynamics
Solution Approach 1:
The patent applies self-service by implementing a reinforcement learning system where the operator model automatically learns and tunes its own control strategies through interaction with the simulation environment, eliminating the need for external expertise in control theory or complete knowledge of system dynamics
Solution Approach 2:
The patent implements feedback by using reinforcement learning where the operator model receives continuous feedback from simulation outcomes and automatically adjusts its control policies, enabling sophisticated control behavior without requiring manual tuning or expert knowledge
3Ease of manufacture
If conventional operator models are used, then the implementation is straightforward, but achieving robust human-like trajectories is difficult, especially with chaotic system dynamics
Solution Approach 1:
The patent applies dynamics by replacing static conventional operator models with a dynamic reinforcement learning system that continuously adapts to chaotic system dynamics and learns robust human-like trajectories through iterative simulation and feedback
Data Source
AI summary
A method of using a virtual machine operator model includes providing a virtual machine operating environment having a current state and including a virtual machine and a virtual operator agent acting within the virtual machine operating environment. The method also includes executing a control action, by the virtual operator agent, relative to the virtual machine based on the virtual machine operator model. The method also includes analyzing a new state of the virtual machine operating environment resulting from execution of the control action, assigning a positive reward or a negative reward to the control action based on the new state, assigning the negative reward to the control action resulting in a high implement jerk movement of the virtual machine, and executing a learning algorithm to update the virtual machine operator model based on the positive reward or the negative reward.