Joris Gillis

TL;DR: This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.

TL;DR: This work has developed a computationally efficient optimal control framework to predict human gaits based on optimization of a performance criterion without relying on experimental data, and identified a multi-objective performance criterion combining energy and effort considerations that produces physiologically realistic walking gaits.

TL;DR: Combining AD with direct collocation and implicit differential equations decreases the computational burden of trajectory optimization of human movement, which will facilitate their use for biomechanical applications requiring the use of detailed models of the musculoskeletal system.

TL;DR: A computational framework for direct trajectory optimization of general manipulation systems with unspecified contact sequences, exploiting environmental constraints as a key tool to accomplish a task.

TL;DR: In this paper, a regularized nonlinear least square identification approach for linear parameter-varying (LPV) systems is presented, where the objective is to obtain an LPV model of which the response fits the system measurements as accurately as possible and, on the other hand, to favor models with an as simple as possible dependency on the scheduling parameter.