Force/position control of constrained reconfigurable manipulators with sliding mode control based on adaptive neural network

A reconfigurable manipulator can achieve proficient end effector and elongate workspace. However, deformable link causes frequent changes in shape and therefore bring difficulties to model and control the manipulator. In view of distinctive behaviour because of bending operation, a sliding mode based mechanism with no prior dynamic information is introduced for validated control operation. The nonlinear term included in the sliding mode is to improve the convergence rate. Moreover, we show that fast terminal sliders reinforce parametric uncertainty as compared to conventional sliders. The neural network system is adopted for the estimation of nonlinear components whereas the friction term and constraint force of each joint are compensated with the help of adaptive control. The Lyapunov theory proves the stability of a closed-loop system. Finally, simulations are performed in a comparative manner with two different configuration controls that will provide the benefit of the design method.