Automated H-infinity Optimization for Multivariable Control Systems
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Solution Overview
Problem
Classical H∞ control techniques are complex and opaque, making it challenging for designers to recast design requirements into a format suitable for H∞ optimization, especially in multi-loop, multivariable control systems.
Innovation Solution
The use of a Technical Computing Environment (TCE) automates H∞ optimization techniques by providing interfaces to facilitate the application of H∞ methods to control system models, allowing for the formulation of multivariable control problems into a standard form and tuning of controllers with relatively little guidance from the designer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical H∞ control techniques are used to synthesize controllers for multivariable control systems, then robust performance and stabilization are achieved, but the controller becomes opaque and complex, and recasting design requirements into H∞ optimization format is challenging
Solution Approach 1:
The patent introduces an intermediary computational framework that translates design requirements into H∞ optimization format automatically. This intermediary layer handles the complex mathematical transformations and computations, allowing designers to work with intuitive specifications while the system manages the complexity of converting these into H∞ controller parameters and transfer functions.
Solution Approach 2:
The patent extracts and separates the complex H∞ optimization computations from the designer's workflow. By isolating the mathematical formulation, transfer function calculations, and optimization algorithms into a dedicated computational environment, the system maintains robust performance capabilities while removing the complexity burden from the design process.
2Reliability
If classical H∞ control techniques are used to synthesize controllers, then robust stabilization is achieved, but the design process becomes challenging and requires significant designer expertise
Solution Approach 1:
The patent implements a self-service design system where the computational environment automatically performs the complex tasks of formulating design requirements, calculating transfer functions, and optimizing controller parameters. The system serves itself by handling the mathematical computations and transformations that would otherwise require significant designer expertise, allowing users with minimal H∞ knowledge to achieve robust stabilization.
Solution Approach 2:
The patent performs preliminary actions by automatically preparing the optimization problem formulation, scaling the signals, and setting up the H∞ optimization framework before the designer begins the actual design work. This preliminary computational setup eliminates the need for designers to manually perform complex mathematical preparations, significantly easing the design process while maintaining robust stabilization capabilities.
3Adaptability or versatility
If H∞ optimization is applied to multivariable control systems, then cross-coupling between channels is handled, but the mathematical formulation and optimization process become increasingly complex
Solution Approach 1:
The patent creates a universal computational framework that handles multivariable control problems with cross-coupling through a unified H∞ optimization approach. This universal system can accommodate multiple inputs and outputs, manage cross-coupling effects, and optimize all channels simultaneously through a single mathematical formulation, rather than requiring separate treatments for each variable or coupling effect.
Solution Approach 2:
The patent merges the handling of multiple control channels and cross-coupling effects into a single integrated H∞ optimization problem. By combining all the multivariable considerations, transfer functions, and coupling effects into one unified mathematical framework, the system manages complexity through consolidation rather than through separate complex formulations for each aspect.
Data Source
AI summary
H-infinity optimization techniques may be automated for multiple input multiple output (MIMO) control problems. In one implementation, a model may be received, where the model includes a plant portion that models elements that are to be controlled and a controller portion that models elements used to control the plant portion, the plant and controller portions of the model interacting with each other in a MIMO feedback configuration. Identification of tunable elements of the controller portion of the model may also be received. Requirements, relating to constraints of open or closed loop transfer functions of the model may also be received. Values for the adjustable parameters of the tunable elements may be calculated, where the calculation may be performed using non-smooth H-infinity optimization techniques and the calculation may be based on the model, the identification of the tunable elements, and the one or more requirements.