Robust Control Toolbox™ provides functions, algorithms, and blocks for analyzing and tuning control systems for performance and robustness. You can create uncertain models by combining nominal dynamics with uncertain elements, such as uncertain parameters or unmodeled dynamics. You can analyze the impact of plant model uncertainty on control system performance and identify worst-case combinations of uncertain elements. H-infinity and mu-synthesis techniques let you design controllers that maximize robust stability and performance.
The toolbox automatically tunes both SISO and MIMO controllers. These can include decentralized, fixed-structure controllers with multiple tunable blocks spanning multiple feedback loops. The toolbox lets you tune one controller against a set of plant models. You can also tune gain-scheduled controllers. You can specify multiple tuning objectives, such as reference tracking, disturbance rejection, stability margins, and closed-loop pole locations.
Models of systems with uncertain parameters or unmodeled dynamics
Statistical and worst-case analysis of stability and performance
Automated tuning of control systems to meet design requirements
Frequency-domain MIMO controller design, controller design for uncertain systems
Order reduction of plant models and synthesized controllers
LMI solvers, control system analysis and design with LMIs