Optimal Controller Identification for multivariable non-minimum phase systems.

This work deals with data-driven control for non-minimum phase (NMP) systems, where the goal is to design a controller for a plant whose model is unknown by using a batch of input-output data collected from it. The approach is based on the Model Reference paradigm, where a transfer function matrix - the reference model - is used to specify the desired closed-loop performance. The NMP systems issue in Model Reference approaches is a well-known problem in control literature and it is no different in data-driven methods. This work explains how to adapt the formulation of the Optimal Controller Identification (OCI) method to cope with this class of systems. Considering a convenient parametrization of the reference model and a flexible performance criterion, it is possible to identify the NMP transmission zeros of the plant along with the optimal controller parameters, as it will be shown. Both diagonal and block-triangular reference model structures are treated in detail. Simulation examples show the effectiveness of the proposed approach.

Cycling of organic and inorganic sulphur in a chestnut oak forest.

Sulfur (S) cycling in a chestnut oak forest on Walker Branch Watershed, Tennessee, was dominated by geochemical processes involving sulfate. Even though available SO 42- was present far in excess of forest nutritional requirements, the ecosystem as a whole accumulated ∼60% of incoming SO4-S. Most (90%) of this accumulation occurred by SO 42- adsorption in sesquioxide-rich subsurface soils, with a relatively minor amount accumulating and cycling as SO 42- within vegetative components. Organic sulfates are thought to constitute a large proportion of total S in surface soils, also, and to provide a pool of readily mineralized available S within the ecosystem.