Optimization Control System for Accurate Behavior Search
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Solution Overview
Problem
Existing optimization control systems face challenges in accurately determining optimal behavior for control subjects, such as robots, due to limited sampling periods, which restrict the search for and application of optimal solutions.
Innovation Solution
An optimization control system comprising a state estimating element, a plan storing element, and a behavior searching element that iteratively updates probability distributions and conditional probability distributions to approach the shape characteristics of an evaluation function, enabling the determination of optimal behavior over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the solution is searched in a limited sampling period, then the control system can operate in real-time, but the searching accuracy of the optimal solution deteriorates
Solution Approach 1:
The patent applies dynamics by making the sampling period variable rather than fixed. The sampling period is dynamically adjusted based on the iteration count and convergence criteria of the optimization algorithm. Initially, a larger sampling period allows broader search, and as iterations progress, the sampling period decreases to refine the optimal solution, thus balancing real-time operation with searching accuracy.
Solution Approach 2:
The patent introduces a temporal dimension to the optimization process by using iterative updates across multiple sampling periods. Instead of searching for the optimal solution in a single fixed period, the system performs sequential searches where each sampling period contributes to progressively refining the solution, transforming a single-dimension time constraint into a multi-dimensional search space.
2Measurement precision
If iterative updates of probability distributions are performed, then the searching accuracy of the optimal solution is improved, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the probability distribution models and their update rules before the optimization process begins. The state estimating element and plan storing element are prepared in advance with initial distributions, and the update mechanisms are predetermined based on expected state transitions, reducing the computational burden during real-time iterative updates.
Solution Approach 2:
The patent implements feedback mechanisms where the state estimating element continuously monitors the actual state of the control subject and feeds this information back to update the probability distributions. The plan storing element uses this feedback to refine the conditional probability distributions, creating a closed-loop system that improves accuracy while managing complexity through structured information flow.
3Measurement precision
If the conditional probability distribution is updated to approach the shape characteristics of the evaluation function, then the optimal behavior determination accuracy is improved, but the calculation time increases
Solution Approach 1:
The patent applies periodic action by structuring the probability distribution updates to occur at regular sampling intervals rather than continuously. The state estimating element updates the state probability distribution p(x) at each sampling period, and the plan storing element updates the conditional probability distribution p(u|x) based on these periodic state updates, balancing accuracy improvement with calculation time management.
Solution Approach 2:
The patent applies partial action by updating only the necessary components of the probability distributions at each sampling period rather than performing complete re-optimization. The state estimating element focuses on updating p(x) based on current state observations, and the plan storing element selectively updates p(u|x) using the updated state distribution and previous joint distribution information, reducing unnecessary calculations.
Data Source
AI summary
Provided is an optimization control system in an attempt to improve searching accuracy of an optimal solution defining a behavior mode for a control subject. A plan storing element 120 is configured to obtain a current update result of a joint probability distribution p(u, x) on the basis of a current update result of a probability distribution p(x) from a state estimating element 110 and a current update result of the conditional probability distribution p(u|x) from a behavior searching element 200. The behavior searching element 200 is configured to determine the conditional probability distribution p(u|x) as a current basis for obtaining the current update result of the conditional probability distribution p(u|x) on the basis of the current update result of the probability distribution p(x) from the state estimating element 110 and a previous update result of the joint probability distribution p(u, x) from the plan storing element 120.