Robust Control Toolbox

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.


References in zbMATH (referenced in 119 articles , 2 standard articles )

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  1. Agler, Jim; Lykova, Zinaida A.; Young, N. J.: Algebraic and geometric aspects of rational (\Gamma)-inner functions (2018)
  2. Chestnov, V. N.; Samshorin, N. I.: Controllers design via given oscillation index: parametric uncertainty and power-bounded external disturbances (2018)
  3. Pujol-Vazquez, Gisela; Acho, Leonardo; Mobayen, Saleh; Nápoles, Amelia; Pérez, Vega: Rotary inverted pendulum with magnetically external perturbations as a source of the pendulum’s base navigation commands (2018)
  4. Scherer, Carsten W.; Veenman, Joost: Stability analysis by dynamic dissipation inequalities: on merging frequency-domain techniques with time-domain conditions (2018)
  5. Tan, Fang; Li, Han-Xiong; Shen, Ping: Smith predictor-based multiple periodic disturbance compensation for long dead-time processes (2018)
  6. Abdulwahhab, Omar Waleed; Abbas, Nizar Hadi: A new method to tune a fractional-order PID controller for a twin rotor aerodynamic system (2017)
  7. Bhowmick, Parijat; Patra, Sourav: An observer-based control scheme using negative-imaginary theory (2017)
  8. Guglielmi, Nicola; Rehman, Mutti-Ur; Kressner, Daniel: A novel iterative method to approximate structured singular values (2017)
  9. Pereira, Renan L.; Kienitz, Karl H.; Guaracy, Fernando H. D.: Discrete-time static (H_\infty) loop shaping control via LMIs (2017)
  10. Ravanbod, Laleh; Noll, Dominikus; Apkarian, Pierre: Branch and bound algorithm with applications to robust stability (2017)
  11. Xue, Dingyü: Fractional-order control systems. Fundamentals and numerical implementations (2017)
  12. Apkarian, Pierre; Noll, Dominikus; Ravanbod, Laleh: Nonsmooth bundle trust-region algorithm with applications to robust stability (2016)
  13. Feng, Yu; Yagoubi, Mohamed: Comprehensive admissibility for descriptor systems (2016)
  14. Kim, Sung Hyun: Model predictive control algorithm based on off-line region dependency (2016)
  15. Veenman, Joost; Scherer, Carsten W.; Köroğlu, Hakan: Robust stability and performance analysis based on integral quadratic constraints (2016)
  16. Wang, Shu; Pfifer, Harald; Seiler, Peter: Robust synthesis for linear parameter varying systems using integral quadratic constraints (2016)
  17. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  18. Chestnov, V. N.: (H_\infty)-approach to controller synthesis under parametric uncertainty and polyharmonic external disturbances (2015)
  19. Chumalee, Sunan; Whidborne, James F.: Gain-scheduled (H_\infty) control via parameter-dependent Lyapunov functions (2015)
  20. Fravolini, Mario L.; Yucelen, Tansel; Campa, Giampiero: Set theoretic performance verification of low-frequency learning adaptive controllers (2015)

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