Some elements of model engineering for optimal control

Abstract¶

This survey presentation will cover three topics relevant to motion control and state observation: (1) Model reduction: how we can compute forward and reverse mappings between whole-body and reduced dynamic models, carrying both the equations of motion and system constraints. (2) Divergent components of motion: how the analysis of nonlinear systems can turn predictive control into linear feedback of virtual states, and how we can leverage this for hierarchical behaviors. (3) Trajectory optimization: a major item in the control toolbox, whose current limitations include feasibility, recursive feasibility, and real-time performance. We review new features brought by an upcoming generation of quadratic-programming and nonlinear solvers, including differentiable QP layers and constrained trajectory optimization.

Content¶

Slides

Trajectory optimization¶

	Aligator
	PROXDDP: Proximal Constrained Trajectory Optimization

Differentiable quadratic programming¶

	ProxSuite (includes PROXQP and QPLayer)
	PROXQP: an Efficient and Versatile Quadratic Programming Solver for Real-Time Robotics Applications and Beyond
	QPLayer: efficient differentiation of convex quadratic optimization