Motion Profile Sequence Prediction for Multi-Target Optimization
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Solution Overview
Problem
Conventional methods for predicting and optimizing motion profile sequences in industrial systems are subjective, time-consuming, and unreliable, often failing to consider data collected during operation, and do not account for correlations among multiple targets or long-term optimality.
Innovation Solution
A system and method using predictive models trained on motion profile sequences and sensor data to optimize target values, incorporating unsupervised learning and anomaly detection to determine optimal motion profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion profiles are designed manually based on mathematical formulae and domain knowledge, then the design process follows conventional methods, but the process becomes subjective, time-consuming, and unreliable
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated machine learning system. The system uses trained models to generate motion profiles automatically based on sensor data and performance targets, eliminating subjective manual design while reducing development time through automated optimization algorithms.
Solution Approach 2:
The system transforms the design approach by changing from fixed mathematical formulae to adaptive parameter optimization. The machine learning model learns optimal motion parameters (position, velocity, acceleration profiles) from operational data, allowing continuous improvement and adaptation without manual reconfiguration.
2Productivity
If conventional methods focus on optimizing individual motion profiles, then each sub-motion can be optimized independently, but the system fails to consider optimization at the sequence level
Solution Approach 1:
The patent merges individual motion profile optimization into a unified sequence-level optimization framework. The system evaluates and optimizes the entire motion profile sequence as an integrated system, considering interactions between consecutive motions and overall task completion efficiency, rather than treating each motion independently.
3Device complexity
If a single target is used during motion profile design, then the design process is simpler, but it cannot cover all performance aspects or incorporate correlations among multiple targets
Solution Approach 1:
The system implements multi-functionality by handling multiple performance targets simultaneously. The machine learning model is trained to optimize multiple objectives (cycle time, energy consumption, position accuracy, vibration) concurrently, allowing the system to address diverse performance requirements through a unified optimization framework.
4Measurement precision
If accurate target data is collected for supervised learning, then the model can be trained effectively, but accurate target data may not be available due to collection infeasibility, inaccuracies, or incompleteness
Solution Approach 1:
The system enables self-service learning by automatically deriving training targets from operational sensor data without requiring manual labeling or external target data collection. The model learns from the physical system's own operational data, using unsupervised and self-supervised learning techniques to identify patterns and optimize performance directly from measured system behavior.
5Speed
If optimization is based on target value at current time, then immediate adjustments can be made, but it does not provide long-term optimization or opportunity for remediation
Solution Approach 1:
The system performs preliminary action by using the predictive model to forecast future target values before they occur. This allows the optimization system to proactively adjust motion profiles in advance to achieve desired future states, rather than merely reacting to current conditions, thereby enabling both rapid response and long-term optimization.
Data Source
AI summary
Traditionally. motion profile sequences are designed manually, as there are numerous obstacles to automated design of motion profile sequences. Disclosed embodiments may utilize unsupervised learning and other techniques to automatically derive targets from sensor data, to train a predictive model that may concurrently predict target values for one or a plurality of targets for a motion profile sequence for each of one or a plurality of future time windows. The predictive model may be incorporated into an optimization process that identifies an optimal motion profile sequence, comprising one or more motion profiles. The optimal motion profile sequence may be deployed to a physical asset, to thereby control the physical asset to perform a task according to the motion profile sequence.