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Automatic Control
Model-based analysis and design allow for the successful control of complex dynamical systems
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The institute is focused on both the development of methods and their practical application.
For an efficient control of technical processes, new techniques are devised in nonlinear control,
energy-based modeling and design, the control of distributed parameter systems, model order
reduction, as well as adaptive and predictive control and methods of optimization and computa-
tional intelligence. Moreover, a Collaborative Research Centre puts a spotlight on the modeling and
analysis of nontechnical systems. New cooperations within a DFG Priority Program and within a joint
international ANR-DFG funding initiative were established.
Concerning application, highly challenging problems include the treatment of vibrations in mechan-
ical systems and in automobiles, the robust control of multicopters, the control of unstable robots,
and the feedback control of technical and nontechnical industrial processes.
Active and Semi-Active
Suspension Control
Active suspension systems
can significantly contribute
to the comfort and safety
of passenger cars by
minimizing vibrations acting
on passengers and by
reducing dynamic wheel
load. Recent developments
of our research include
nonlinear disturbance compensators and optimal proac-
tive preview control. Thereby, different design objectives
can be achieved transparently. To gather and manage the
required preview data, we look into approaches including
vehicle-based road profile estimation (see figure below)
combined with vehicle-to-infrastructure (V2I) communica-
tion. The so-called hybrid suspension system, developed
at our institute, is shown in the figure left; it includes a
low-bandwidth actuator and a high-bandwidth variable
damper, together with a sophisticated control system.
Experiments are performed at a quarter-car test stand available in the
Institute of Automatic Control.
High-Performance Control of Constrained Fast Systems
There is huge interest in modern robots for transportation
and service capable of performing fast maneuvers while
maintaining stability at all times. As these systems are
often inherently nonlinear, unstable and fast, control
requirements are increasing rapidly. Our groups pursue
two main approaches to high-performance control of such
systems: model predictive control (MPC) and Lyapunov-
based nonlinear control methods. The main advantage
of the MPC paradigm is the possibility of systematically
considering constraints on input and state variables. How-
ever, the real-world application of nonlinear MPC remains
challenging, as non-convex optimization problems have
to be solved online with a sufficient accuracy in order to
obtain desirable closed-loop dynamics. Currently, a novel
control concept for multirotor systems using adaptive and
predictive components is developed in cooperation with
the Institute of Flight System Dynamics.
Lyapunov-based techniques can use a linear representa-
tion of the nonlinear system to design a controller that
locally stabilizes an operating point. Particularly, low- and
high-gain methods require the solution of either a Riccati
or a parametric Lyapunov equation providing an optimal
control law together with a contractive Lyapunov function
which, for systems under input saturation, can be used as