Martin Pottmann

Model-based control of a granulation system

Abstract Granulation, the process by which granules are made from powdered, slurried, solution or molten feed material, is an important process in many industries. The main objective in the granulation process is to produce granules with consistent product quality, as indicated by various industry standard variables which can be related to two fundamental process quantities: particle size distribution and bulk density. While it is customary to specify a desired setpoint value for bulk density, the specifications on particle size distribution typically take the form of an upper limit (dU) and a lower limit (dL) determined by the screen sizes used in product classification. The paper discusses the peculiar control problems arising from such a combination of product quality specifications and then develops a model-based control scheme which systematically addresses the problems. Simulation studies illustrate the control system implementation and performance for various situations of practical significance.

Direct adaptive control of partially known nonlinear systems

A direct adaptive control strategy for a class of single-input/single-output nonlinear systems is presented. The major advantage of the proposed method is that a detailed dynamic nonlinear model is not required for controller design. The only information required about the plant is measurements of the state variables, the relative degree, and the sign of a Lie derivative which appears in the associated input-output linearizing control law. Unknown controller functions are approximated using locally supported radial basis functions that are introduced only in regions of the state space where the closed-loop system actually evolves. Lyapunov stability analysis is used to derive parameter update laws which ensure (under certain assumptions) the state vector remains bounded and the plant output asymptotically tracks the output of a linear reference model. The technique is successfully applied to a nonlinear biochemical reactor model.

Compactly supported radial basis functions for adaptive process control

Abstract An adaptive nonlinear control strategy based on networks of compactly supported radial basis functions is proposed. The local influence of the basis functions allows efficient on-line adaptation that is performed using a gradient law, and new basis functions are added to the network only when new regions in state space are encountered and the prediction error exceeds a pre-specified tolerance. The approximate model is used to construct an input-output linearizing control law. The adaptive control strategy is applied to a nonlinear chemical reactor model.