Fuzzy model predictive control for 2-DOF robotic arms

Purpose Robotic arm control is challenging due to the intrinsic nonlinearity. Proportional-integral-derivative (PID) controllers prevail in many robotic arm applications. However, it is usually nontrivial to tune the parameters in a PID controller. This paper aims to propose a model-based control strategy of robotic arms. Design/methodology/approach A Takagi-Sugeno (T-S) fuzzy model, which is capable of approximating nonlinear systems, is used to describe the dynamics of a robotic arm. Model predictive control (MPC) based on the T-S fuzzy model is considered, which optimizes system performance with respect to a user-defined cost function. Findings The control gains are optimized online according to the real-time system state. Furthermore, the proposed method takes into account the input constraints. Simulations demonstrate the effectiveness of the fuzzy MPC approach. It is shown that asymptotic stability is achieved for the closed-loop control system. Originality/value The T-S fuzzy model is discussed in the modeling of robotic arm dynamics. Fuzzy MPC is used for robotic arm control, which can optimize the transient performance with respect to a user-defined criteria.