On the Control of Distributed Parameter Systems using a Multidimensional Systems Setting

The unique characteristic of a repetitive process is a series of sweeps, termed passes, through a set of dynamics defined over a finite duration with resetting before the start of the each new one. On each pass an output, termed the pass profile is produced which acts as a forcing function on, and hence contributes to, the dynamics of the next pass profile. This leads to the possibility that the output, i.e. the sequence of pass profiles, will contain oscillations which increase in amplitude in the pass-to-pass direction. Such behavior cannot be controlled by standard linear systems approach and instead they must be treated as a multidimensional system, i.e. information propagation in more than one independent direction. Physical examples of such processes include long-wall coal cutting and metal rolling. In this paper, stability analysis and control systems design algorithms are developed for a model where a plane, or rectangle, of information is propagated in the passto- pass direction. The possible use of these in the control of distributed parameter systems is then described using a fourthorder wavefront equation

Robust Control of a Laboratory Servomechanism.

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Modular Shift of a Polynomial Matrix.

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Robust Control of Distributed Parameter Systems using a Multidimensional Systems Setting

In this paper we extend the previous results of [1] on the control of distributed parameter systems to the case when there is uncertainty associated with the process dynamics. The result is algorithms for control law design

Robust control of distributed parameter mechanical systems using a multidimensional systems approach

The unique characteristic of a repetitive processes is a series of sweeps, termed passes, through a set of dynamics defined over a finite duration. On each pass an output, termed the pass profile is produced which acts as on forcing function, and hence contributes to, the dynamics of the next pass profile. This leads to the possibility that the output, i.e. the sequence of pass profiles, will contain oscillations that increase in amplitude in the pass-to-pass direction. Such behavior cannot be controlled by application of standard linear systems control laws and instead they must be treated as two-dimensional (2D) systems where information propagation in two independent directions, termed passto-pass and along the pass respectively, is the defining feature. Physical examples of such processes include long-wall coal cutting and metal rolling. In this paper, stability analysis and control law design algorithms are developed for discrete linear repetitive processes where a plane, or rectangle, of information is propagated in the pass-to-pass direction. The possible use of such a model in the control of distributed parameter systems has been investigated in previous work and this paper considers an extension to allow for uncertainty in the model description

Modular Shift of a Polynomial Matrix.

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Robust Control of a Laboratory Servomechanism.

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Symbolic regression driven by training data and prior knowledge

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior. If these properties were considered already during the search process, more accurate and relevant models could be produced. We propose a multi-objective symbolic regression approach that is driven by both the training data and the prior knowledge of the properties the desired model should manifest. The properties given in the form of formal constraints are internally represented by a set of discrete data samples on which candidate models are exactly checked. The proposed approach was experimentally evaluated on three test problems with results clearly demonstrating its capability to evolve realistic models that fit the training data well while complying with the prior knowledge of the desired model characteristics at the same time. It outperforms standard symbolic regression by several orders of magnitude in terms of the mean squared deviation from a reference model.Learning & Autonomous Contro