Deterministic OBC Control Trajectory Search for Fast Dynamics
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Solution Overview
Problem
Optimization-based control (OBC) systems for complex dynamic control systems are computationally demanding due to the need for solving constrained optimization problems like quadratic programming, which can take seconds or minutes, making them unsuitable for real-time control with fast dynamics.
Innovation Solution
Implementing a deterministic optimization-based control method that uses a combination of infeasible and feasible search algorithms within predetermined sample periods to find and stabilize a feasible control trajectory, utilizing a stabilization function to ensure a stable solution within a predetermined time window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optimization-based control (OBC) is used to handle multivariable constraints and operate closer to constraints, then control performance is improved, but computational time increases significantly making it unsuitable for fast dynamics
Solution Approach 1:
The patent segments the control system into multiple parallel processors, each handling a portion of the optimization calculation. This parallelization divides the computationally intensive OBC task into smaller, simultaneously executable units, reducing overall computational time while maintaining the ability to handle multivariable constraints and operate near constraints effectively.
2Reliability
If general solvers are used to solve constrained optimization problems, then feasible solutions are found, but solution time becomes unpredictable and often exceeds real-time control requirements
Solution Approach 1:
The patent performs preliminary actions by pre-processing and initializing optimization data structures before the actual optimization run. This includes setting up parallel processor configurations, pre-calculating constraint boundaries, and preparing initial feasible solutions. These preliminary steps reduce the computational burden during real-time execution, making solution time more predictable and ensuring feasibility within real-time constraints.
3Productivity
If computational complexity is reduced to enable real-time control, then processing speed improves, but the ability to handle complex multivariable constraints may be compromised
Solution Approach 1:
The patent transitions from sequential single-processor optimization to a parallel multi-processor architecture, adding the dimension of concurrent computation. This dimensional change allows the system to maintain high processing speed by distributing calculations across multiple processors while simultaneously handling complex multivariable constraints through coordinated parallel operations, effectively resolving the trade-off between speed and constraint handling capability.
Data Source
AI summary
The embodiments described herein include one embodiment that a control method including executing an infeasible search algorithm during a first portion of a predetermined sample period to search for a feasible control trajectory of a plurality of variables of a controlled process, executing a feasible search algorithm during a second portion of the predetermined sample period to determine the feasible control trajectory if the infeasible search algorithm does not determine a feasible control trajectory, and controlling the controlled process by application of the feasible control trajectory.