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With the help of universal approximation principle of radial basis function neural networks and the positive invariant set of established TB model, the expressions of control variables are designed and the stability of error model is analyzed. Thus, the adaptive control method can guarantee that the number of susceptible and infected individuals can be kept close to the corresponding control targets. Finally, the designed control variables are illustrated by numerical examples. The results indicate that the proposed adaptive controllers can effectively control the established TB model and ensure the stability of controlled model, and two control measures can protect more people from tuberculosis infection.&lt;\/p&gt;&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2023461","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T11:29:09Z","timestamp":1680866949000},"page":"10464-10478","source":"Crossref","is-referenced-by-count":2,"title":["Nonlinear neural networks adaptive control for a class of fractional-order tuberculosis model"],"prefix":"10.3934","volume":"20","author":[{"given":"Na","family":"Pang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2023461-1","unstructured":"<i>Global Tuberculosis Report 2019<\/i>, World Health Organization, (2019)."},{"key":"key-10.3934\/mbe.2023461-2","doi-asserted-by":"publisher","unstructured":"S. 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