On Using a Support Vector Machine in Learning Feed-Forward Control

For mechatronic motion systems, the performance increases significantly if, besides feedback control, also feed-forward control is used. This feed-forward part should contain the (stable part of the) inverse of the plant. This inverse is difficult to obtain if non-linear dynamics are present. To overcome this problem, learning feed-forward control can be applied. The properties of the learning mechanism are of importance in this setting. In the paper, a support vector machine is proposed as the learning mechanism. It is shown that this mechanism has several advantages over other learning techniques when applied to learning feed-forward control. The method is tested with simulation

Phase correction for Learning Feedforward Control

Intelligent mechatronics makes it possible to compensate for effects that are difficult to compensate for by construction or by linear control, by including some intelligence into the system. The compensation of state dependent effects, e.g. friction, cogging and mass deviation, can be realised by learning feedforward control. This method identifies these disturbing effects as function of their states and compensates for these, before they introduce an error. Because the effects are learnt as function of their states, this method can be used for non-repetitive motions. The learning of state dependent effects relies on the update signal that is used. In previous work, the feedback control signal was used as an error measure between the approximation and the true state dependent effect. If the effects introduce a signal that contains frequencies near the bandwidth, the phase shift between this signal and the feedback signal might seriously degenerate the performance of the approximation. The use of phase correction overcomes this problem. This is validated by a set of simulations and experiments that show the necessity of the phase corrected scheme

Pruning Error Minimization in Least Squares Support Vector Machines

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsparse solution is obtained. The sparseness is imposed by pruning, i.e., recursively solving the approximation problem and subsequently omitting data that has a small error in the previous pass. However, omitting data with a small approximation error in the previous pass does not reliably predict what the error will be after the sample has been omitted. In this paper, a procedure is introduced that selects from a data set the training sample that will introduce the smallest approximation error when it will be omitted. It is shown that this pruning scheme outperforms the standard one

On-line nonparametric regression to learn state-dependent disturbances

A combination of recursive least squares and weighted least squares is made which can adapt its structure such that a relation between in- and output can he approximated, even when the structure of this relation is unknown beforehand.\ud This method can adapt its structure on-line while it preserves information offered by previous samples, making it applicable in a control setting. This method has been tested with compntergenerated data, and it b used in a simulation to learn the non-linear state-dependent effects, both with good success

Hard sphere colloidal dispersions: Mechanical relaxation pertaining to thermodynamic forces

The complex viscosity of sterically stabilized (hard) silica spheres in cyclohexane has been measured between 80 Hz and 170 kHz with torsion pendulums and a nickel tube resonator. The observed relaxation behaviour can be attributed to the interplay of hydrodynamic and thermodynamic forces. The validity of the Cox-Merz rule is checked

Gevoeligheid van belgische Mycoplasma Hyopneumoniae isolaten voor antimicrobiële middelen

To control Mycoplasma hyopneumoniae infections, the use of antibiotics in feed or water is a common practice. Unfortunately, information concerning the susceptibility of M. hyopneumoniae to different antimicrobials is very scarce. The in vitro susceptibility of 21 M hyopneumoniae field isolates was determined in this study using a broth microdilution technique. Acquired resistance to spectinomycin, oxytetracycline, doxycycline, gentamicin, florfenicol and tiamulin was not observed. One isolate showed acquired resistance to lincomycin, tilmicosin and tylosin, but was susceptible to all other antibiotics tested. For 5 isolates, the MIC-values of flumequine were > 16 mu g/ml, while the MIC50-value was 2 mu g/ml. The MIC-values of enrofloxacin for these 5 isolates were >= 0.5 mu g/ml, the MIC50 being 0.06 mu g/ml. This is the first report showing acquired resistance against macrolides, lincosamides and fluoroquinolones in M hyopneumoniae field isolates