Kernel methods and Model predictive approaches for Learning and Control
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Model Predictive Control has been implemented on a large drainage canal system in the Netherlands. This water system can be represented as a reservoir with uncertain inflow due to rainfall runoff and a water level that has to be kept within a certain range ...
Model predictive control (MPC) is a very effective approach to control nonlinear systems, especially when the systems are high dimensional and/or constrained. MPC formulates the problem of input trajectory generation as an optimization problem. However, du ...
To improve the resolution of the Very Large Telescope Interferometer (VLTI) a two-stage mechanical system, a so called Differential Delay Line (DDL), is developed jointly by the EPFL and the Observatory of Geneva. The system is designed to reach nanometer ...
A standard model predictive controller (MPC) can be written as a parametric optimization problem whose solution is a piecewise affine (PWA) map from the measured state to the optimal control input. The primary limitation of this optimal `explicit solution ...
In this paper we propose an application of distributed model predictive control techniques to the problem of driving a group of autonomous agents towards a consensus point, i.e. a negotiated position in their state space. Agents are assumed to be governed ...
This presentation show the successful usage of LabVIEW on the Mac with a specific focus on control solutions. First, 15 years of control solutions with LabVIEW on the Mac are reviewed. Then it presents current DAQmx base solutions, using as an example a Sp ...
The control of cranes and nonholonomic robots has gained increased interest mainly because of the civilian and military industrial need to achieve fast and accurate transport of goods and equipment. Old and new harbors are now venturing into fully automate ...
A predictive optimal control system for micro-cogeneration in domestic applications has been developed. This system aims at integrating stochastic inhabitant behavior and meteorological conditions as well as modeling imprecisions, while defining operation s ...
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In this paper a combination of brain emotional learning based intelligent controllers (BELBICs) is employed to control an unidentified practical overhead crane. The proposed controller is a model free controller and has the capability to deal with multi-ob ...
Limits on the storage space or the computation time restrict the applicability of model predictive controllers (MPC) in many real problems. Currently available methods either compute the optimal controller online or derive an explicit control law. In this ...
