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Radial basis function neural networks are introduced to approximate the system uncertainty and only one parameter needs to be updated online. The output constraints are satisfied by converting the constrained system to an unconstrained one. Moreover, an auxiliary dynamic system is applied in the transformed unconstrained system to handle the input saturation nonlinearity and the Moore\u2013Penrose pseudo-inverse term is introduced for controller design. All signals in the closed-loop system are proved to be bounded by using the Lyapunov method. To show the good performance of the given scheme, comparative simulation is conducted on a two-joint manipulator, which is controlled by the proposed adaptive neural networks force\/position tracking controller and a conventional force\/position controller without considering input saturation and output constraints. Simulation results show that the adaptive neural network force\/position controller has better control performance than the traditional method.<\/jats:p>","DOI":"10.1177\/09596518221080249","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T05:08:25Z","timestamp":1646629705000},"page":"1197-1210","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive neural networks force\/position control of uncertain manipulators subject to input saturation and output constraints"],"prefix":"10.1177","volume":"236","author":[{"given":"Yuxiang","family":"Wu","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, 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