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