This article introduces a computationally efficient robust model predictive control (MPC) scheme for controlling nonlinear systems affected by parametric uncertainties in their models. The approach leverages the recent notion ofclosed-loop state sensitivityand the associated ellipsoidal tubes of perturbed trajectories for taking into account online time-varying restrictions on state and input constraints. This makes the MPC controller “aware” of potential additional requirements needed to cope with parametric uncertainty, thus significantly improving the tracking performance and success rates during navigation in constrained environments. One key contribution lies in the introduction of a computationally efficient robust MPC formulation with acomparable computational complexityto a standard MPC (i.e., an MPC not explicitly dealing with parametric uncertainty). An extensive simulation campaign is presented to demonstrate the effectiveness of the proposed approach in handling parametric uncertainties and enhancing task performance, safety, and overall robustness. Furthermore, we also provide an experimental validation that shows the feasibility of the approach in real-world conditions and corroborates the statistical findings of the simulation campaign. The versatility and efficiency of the proposed method make it therefore a valuable tool for real-time control of robots subject to nonnegligible uncertainty in their models.

This paper presents a novel method to maintain the dynamic balance of a Mobile Manipulator (MM) during the pick-up of heavy objects. The approach entails the generation of a preliminary reach-to-grasp trajectory, which is subsequently refined by an Optimization-Based Controller (OBC) formulated as a Quadratic Program (QP). The trajectory is modified in a minimal fashion to ensure that the robot maintains balance during the reaching phase and remains balanced when the payload is grasped. This is accomplished by incorporating a balance constraint into the OBC that predicts the Zero Moment Point (ZMP) position of the robot at the beginning of the pick-up phase. This accounts for the gravitational and inertial effects that the object has on the robot. The method is validated through simulations conducted with the TIAGo robot in Gazebo. The results demonstrate that the proposed approach effectively prevents the robot from tipping over when the payload is considered.

We propose an effective whole-body MPC controller for locomotion of humanoid robots. Our method generates motions using the full kinematics, allowing it to account for joint limits and to exploit upper-body motions to reject disturbances. Each MPC iteration solves a single QP that considers the interplay between dynamic and kinematic features of the robot. Thanks to our special formulation, we are able to perform a feasibility analysis, which opens the door to future enhancements of functionality and performance, e.g., step adaptation in complex environments. We demonstrate its effectiveness through a campaign of dynamic simulations aimed at highlighting how the joint limits and the use of the angular momentum through upper-body motions are fundamental for maximizing performance, robustness, and ultimately make the robot able to execute more challenging gaits.

We present a whole-body control architecture for the generation of stable task-oriented motions in Wheeled Inverted Pendulum (WIP) robots. Controlling WIP systems is challenging because the successful execution of tasks is subordinate to the ability to maintain balance. Our feedback control approach relies both on partial feedback linearization and Model Predictive Control (MPC). The partial feedback linearization reshapes the system into a convenient form, while the MPC computes inputs to execute the desired task by solving a constrained optimization problem. Input constraints account for actuation limits and a stability constraint is in charge of stabilizing the unstable body pitch angle dynamics. The proposed approach is validated by simulations on an ALTER-EGO robot performing navigation and loco-manipulation tasks.