The　increasing　energy　demand　and　the　changing　of　energy　structure　have　imposed　higher　requirements　on　the　conventional　large-scale　power　plants　control.　Complexity　of　the　power　plant　processes　and　the　frequent　change　of　operation　condition　make　the　accurate　physical　models　hard　to　obtain　for　control　design.　To　this　end,　a　data-driven　control　strategy,　the　active　disturbance　rejection　control　(ADRC)　has　received　much　attention　for　the　estimation　and　mitigation　of　uncertain　dynamics　beyond　the　canonical　form　of　cascaded　integrators.　However,　the　robustness　of　ADRC　is　seldom　discussed　in　a　quantitative　manner.　In　this　study,　the　maximum　sensitivity　is　used　to　evaluate　and　then　constrain　the　robustness　of　ADRC　applied　to　high-order　processes.　Firstly,　by　using　the　new　idea　of　the　vertical　asymptote　of　the　Nyquist　curve,　a　preliminary　one-parameter-tuning　method　is　developed.　Secondly,　a　quantitative　relationship　between　the　maximum　sensitivity　and　the　tuning　parameter　is　established　using　optimization　methods.　Then,　the　feasibility　and　effectiveness　of　the　proposed　method　is　initially　verified　in　the　total　air　flow　control　of　a　power　plant　simulator.　Finally,　field　tests　on　the　secondary　airflow　control　in　a　330　MWe　circulating　fluidized　bed　confirm　the　merit　of　the　proposed　maximum　sensitivity-constrained　ADRC　tuning.