A-C-I Fuzzy Logic System Structure-Based Reconfigurable Robot Manipulators Finite-Time Optimal Backstepping Force/Position Control

To address the force/position control challenges under varying external constraints, this letter proposes an actor-critic-identify (A-C-I) fuzzy logic system (FLS) structure-based reconfigurable robot manipulators (RRM) finite-time optimal backstepping force/position control. Unlike traditional force/position control methods, the proposed controller adopts a backstepping framework to construct performance indices and optimal controllers associated with the exponential form of tracking error dynamics. The FLS is employed in place of conventional neural networks due to its ability to avoid predefined initial weights and support fast convergence. This replacement enhances the identification of system uncertainties, the approximation of performance indices, and the derivation of the finite-time optimal controller. As a result, the strategy ensures rapid state error convergence and control optimality in the presence of external constraints. Lyapunov-based analysis confirms that the closed-loop system achieves semi-global practical finite-time stability (SGPFS), and experimental results demonstrate the effectiveness of the proposed approach.