Nonlinear Control

To date, most approaches to the control of nonlinear systems such as humanoid robots are highly dependent on handcrafted high gains and/or precise rigid body dynamics models. However, in order to ever leave laboratory floors, humanoid robots will require low-gain control so that they cannot damage their environment and due to the large extent of unmodeled nonlinearities the learning of the dynamics model will become essential. Over the last decades, we have developed several new approaches to nonlinear control and will illustrate some of these in more detail at this point.

Learning and Adaptive Control

Many difficult robot systems as well as other plants defy any attempt of modeling from a physical understanding. If high-gain control is impossible due to the application, compliance requirements or the usage of light-weight low-torque motors, then learning is often the only choice. In our lab, we have developed a variety of learning and adaptive control methods. Most of these techniques learn extremely fast and outperform human modeling by far in tested robot applications.

Gauss-Udwadia Control

Inspired by results from analytical dynamics, we have introduced a novel control architecture together with our collaborator Firdaus Udwadia (Department of Aerospace and Mechanical Engineering). This architecture allows the derivation both of novel as well as established control laws (e.g., operational space control laws) from a unique immediate cost optimal control perspective. We are currently working on a generalization which will allow the framework to become a learning framework.

Operational Space Control

We use nonlinear control techniques to address the issue of task achievement in operational space while maintaining coordination among redundant degrees of freedom: a particularly challenging problem for highly redundant robots, like humanoids. In addition to examining both traditional and novel redundancy resolution schemes on our 7-DOF manipulator, we are investigating operational space control techniques as a means of center-of-gravity placement for balancing legged platforms.

Contact persons: Jun Nakanishi, Michael Mistry, Jan Peters, Stefan Schaal

Publications

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