Sequential Parameter Adjustment for Complex Control Tuning
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Solution Overview
Problem
In complex control systems, such as servo control systems, the adjustment of numerous interdependent parameters is time-consuming and difficult, especially as the systems become more complicated, making it challenging for designers to optimize parameters effectively.
Innovation Solution
An information processing apparatus and method that utilize machine learning to automatically optimize parameters by sequentially adjusting parameter sets through a network of parameter adjusting units, where each unit adjusts its set based on the previous stage's adjustments, using actual measured or predicted values to minimize target evaluation values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of control parameters is increased to achieve high performance, then the control system performance is improved, but the adjustment time and difficulty increase significantly
Solution Approach 1:
The system enables self-adjustment of control parameters through automatic optimization algorithms. The parameter adjustment unit automatically searches for optimal parameter values based on evaluation functions, eliminating the need for manual step-by-step adjustment by designers. This self-service mechanism resolves the contradiction by maintaining high performance while dramatically reducing adjustment time.
Solution Approach 2:
The invention systematically changes and optimizes multiple control parameters simultaneously using automated search algorithms. Instead of manual step-by-step parameter tuning, the system evaluates multiple parameter combinations based on objective functions and automatically identifies optimal parameter sets, thereby reducing adjustment time while maintaining performance.
2Adaptability or versatility
If the control system becomes more complicated to handle advanced control targets, then the control capability is enhanced, but the ease of adjustment deteriorates
Solution Approach 1:
The invention replaces manual mechanical adjustment processes with automated computational systems. The parameter adjustment unit uses algorithms and evaluation functions to automatically determine optimal parameters, substituting the designer's manual tuning process. This resolves the contradiction by maintaining enhanced control capability while significantly improving ease of adjustment through automation.
Solution Approach 2:
The system implements automated feedback mechanisms where evaluation functions continuously assess control performance and guide parameter optimization. The parameter adjustment unit receives feedback from simulation results and actual system performance, automatically adjusting parameters to achieve optimal control. This feedback loop enables complex control systems to be easily adjusted without manual intervention.
3Manufacturing precision
If manual parameter adjustment is performed in a stepped manner based on knowledge and experience, then some optimization is achieved, but the process becomes time-consuming and difficult for complex systems
Solution Approach 1:
The system performs self-optimization of parameters through automated algorithms rather than relying on manual stepped adjustment. The parameter adjustment unit independently evaluates parameter combinations using objective functions and automatically identifies optimal settings, maintaining high optimization quality while dramatically improving adjustment efficiency and productivity.
Solution Approach 2:
The system performs preliminary automated evaluation of multiple parameter combinations before final implementation. The parameter adjustment unit pre-calculates optimal parameter sets based on evaluation functions and simulation results, allowing for quick deployment without time-consuming manual stepped adjustment, thereby improving productivity while maintaining optimization quality.
Data Source
AI summary
An information processing apparatus includes an n-th parameter adjuster and an (n+1)-th parameter adjuster. The n-th parameter adjuster adjusts an n-th parameter set so that an n-th evaluation value set based on the n-th parameter set approaches an n-th target value set. The (n+1)-th parameter adjuster adjusts an (n+1)-th parameter set so that an (n+1)-th evaluation value set based on the (n+1)-th parameter set approaches an (n+1)-th target value set. In addition, the n-th parameter adjuster acquires, based on initial value set or search value set of the n-th parameter set, an n-th actual measured value set or an n-th predicted value set, acquires an (n+1)-th target value set based on the initial value set or the search value set of the n-th parameter set, and searches for the n-th parameter set that optimizes the (n+1)-th target value set under a restriction that the n-th evaluation value set approaches the n-th target value set using the acquired n-th actual measured value set or the n-th predicted value set and the acquired (n+1)-th target value set.