A new semidefinite relaxation jointly optimizes trajectories, control inputs, and time scaling for linear and piecewise-affine systems, and extends to mode-sequence optimization via graphs of convex sets.
Feedback Control of the Pusher-Slider System: A Story of Hybrid and Underactuated Contact Dynamics
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abstract
This paper investigates real-time control strategies for dynamical systems that involve frictional contact interactions. Hybridness and underactuation are key characteristics of these systems that complicate the design of feedback controllers. In this research, we examine and test a novel feedback controller design on a planar pushing system, where the purpose is to control the motion of a sliding object on a flat surface using a point robotic pusher. The pusher-slider is a simple dynamical system that retains many of the challenges that are typical of robotic manipulation tasks. Our results show that a model predictive control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of the contact. In order to achieve real-time control, simplifications are proposed to speed up the integer program. The concept of Family of Modes (FOM) is introduced to solve an online convex optimization problem by selecting a set of contact mode schedules that spans a large set of dynamic behaviors that can occur during the prediction horizon. The controller design is applied to stabilize the motion of a sliding object about a nominal trajectory, and to re-plan its trajectory in real-time to follow a moving target. We validate the controller design through numerical simulations and experimental results on an industrial ABB IRB 120 robotic arm.
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2025 1verdicts
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A New Semidefinite Relaxation for Linear and Piecewise-Affine Optimal Control with Time Scaling
A new semidefinite relaxation jointly optimizes trajectories, control inputs, and time scaling for linear and piecewise-affine systems, and extends to mode-sequence optimization via graphs of convex sets.