Real-Time Trajectory Optimization Under Disturbance Constraints
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Solution Overview
Problem
Current methods for real-time optimization of dynamical systems in manufacturing and industrial processes are complex due to constraints and disturbances, and existing solutions are primarily offline, making real-time trajectory optimization infeasible and ineffective in handling dynamic changes.
Innovation Solution
A system comprising a data receiver, storage, and transmitter unit, along with hardware processors and memory units that preprocess and classify data, forecast disturbances, select actuation profiles, monitor processes, adjust models, and re-estimate actuation profiles to optimize trajectories in real-time, enabling online adaptation and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline optimization methods are used for trajectory optimization, then computational accuracy can be improved, but real-time adaptability deteriorates due to inability to handle disturbances and dynamic changes
Solution Approach 1:
The patent transforms the static offline optimization approach into a dynamic online optimization system that continuously adapts to changing conditions. The system dynamically updates the trajectory optimization based on real-time disturbance measurements and system state changes, enabling both high accuracy and real-time adaptability through continuous receding horizon optimization.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors actual system behavior, compares it with predicted trajectories, and uses this information to adjust future optimization decisions. The feedback loop incorporates disturbance measurements and constraint violations to improve subsequent trajectory predictions and control actions.
2Adaptability or versatility
If real-time monitoring and adjustment mechanisms are implemented, then adaptability to disturbances is improved, but system complexity increases due to multiple processing units and continuous optimization
Solution Approach 1:
The patent merges multiple functions into integrated processing units. The disturbance detection, trajectory prediction, constraint checking, and optimization calculation are combined into a unified online optimization system that operates through coordinated interaction of integrated components rather than separate independent units.
Solution Approach 2:
The patent segments the optimization problem into manageable computational tasks that can be executed in real-time. The overall trajectory optimization is divided into disturbance detection, state prediction, constraint verification, and control calculation stages, allowing efficient processing through modular computational steps.
3Measurement precision
If extensive data processing and classification are performed for online optimization, then optimization quality is improved, but computational time increases which may affect real-time performance
Solution Approach 1:
The patent applies partial optimization actions by focusing computational resources on the most critical aspects of trajectory optimization at each time step. Rather than performing complete exhaustive optimization, the system performs sufficient optimization to achieve acceptable performance within real-time constraints, using receding horizon approach where full optimization is performed over a finite future window.
Data Source
AI summary
Trajectory optimization is process of designing a trajectory of operating variables that optimizes measure of performance while satisfying a set of constraints, when the system moves from one state to another. It is very necessary to achieve optimization in real time. A system and method for real-time trajectory optimization has been provided. The trajectory optimization of a process can be performed in any dynamical automated system. The system is configured to optimize the trajectory in both online and offline mode. In the online mode, the system optimizes the trajectory of the process in real-time. The system has the ability to handle both machine learning and deep learning based time series models along with first principles based models represented by ordinary/partial differential equation or differential algebraic equation based dynamic models of the process to estimate process variables given the disturbance profile and the actuation profile of manipulated variables.