Model Predictive Control for a Tendon-Driven Surgical Robot with Safety Constraints in Kinematics and Dynamics

In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety and accuracy of the surgical task. Mechanical designs and actuation schemes have inevitable limitations such as backlash and joint limits. Moreover, surgical robots need to operate in narrow pathways, which may give rise to additional environmental constraints. Therefore, the control strategies must be capable of satisfying the desired motion trajectories and the imposed constraints. Model Predictive Control (MPC) has proven effective for this purpose, allowing to solve an optimal problem by taking into consideration the evolution of the system states, cost function, and constraints over time. The high nonlinearities in tendon-driven systems, adopted in many surgical robots, are difficult to be modelled analytically. In this work, we use a model learning approach for the dynamics of tendon-driven robots. The dynamic model is then employed to impose constraints on the torques of the robot under consideration and solve an optimal constrained control problem for trajectory tracking by using MPC. To assess the capabilities of the proposed framework, both simulated and real world experiments have been conducted.