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To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer\u2010loop force impedance control and the inner\u2010loop position tracking control. In the outer\u2010loop, an improved impedance controller, which combines the traditional impedance relationship with the PID\u2010like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner\u2010loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end\u2010effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer\u2010loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed\u2010loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two\u2010link robotic manipulator.<\/jats:p>","DOI":"10.1155\/2019\/1406534","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T23:30:39Z","timestamp":1546558239000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Position\/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2823-6571","authenticated-orcid":false,"given":"Jinzhu","family":"Peng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeqi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2414-8926","authenticated-orcid":false,"given":"Tianlei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"141","article-title":"Trajectory tracking control of an industrial robot manipulator using fuzzy SMC with RBFNN","volume":"28","author":"Ak A. 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