Iterative Refinement Procedure for Solutions to Algebraic Riccati Equations

The current article presents the design, implementation, validation, and use of a Computer-Aided Control System Design (CACSD) toolbox for nonlinear and hybrid system uncertainty modeling, simulation, and control using μ synthesis. Remarkable features include generalization of classical system interconnection operations to nonlinear and hybrid systems, automatic computation of equilibrium points for nonlinear systems, and optimization of least conservative uncertainty bounds, with direct applicability for μ synthesis. A unified approach is presented for the step-down (buck), step-up (boost), and single-ended primary-inductor (SEPIC) converters to showcase the use and flexibility of the toolbox. Robust controllers were computed by minimization of the H∞ norm of the augmented performance systems, encompassing a wide range of uncertainty types, and have been designed using the well-known mixed-sensitivity closed loop shaping μ synthesis method.

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Iterative Refinement Procedure for Solutions to Algebraic Riccati Equations

Cited by 3 publications

References 9 publications

An improved iterative algorithm for solving optimal tracking control problems of stochastic systems

An improved iterative algorithm for solving optimal tracking control problems of stochastic systems

Linear Quadratic Optimal Control of Stochastic Discrete-Time Systems with λ-Policy Iteration

Unified CACSD Toolbox for Hybrid Simulation and Robust Controller Synthesis with Applications in DC-to-DC Power Converter Control

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