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Optimal Adaptive Control of an Ash Stabilization Batch Mixing Process using Change Detection

We present an optimal adaptive controller that is used to regulate the chemical process of wood ash stabilization (WAS). The model parameters of the time-varying process dynamics are estimated using recursive least squares (RLS). At each batch an auto-tuning sequence produced with the controller disabled is carried through in order to obtain a good estimate of the process dynamics. After the auto-

A New Formalism for Modeling of Reactive and Hybrid Systems

A new Modelica library is presented that is used to model safe hierarchical state machines in combination with any Modelica model, e.g., controllers, logical blocks, and physical systems described by differential-algebraic equations. It has been designed for simple usage, to guarantee safety properties and to harmonise with the design of the new Modelica_EmbeddedSystems library. Furthermore, new b

Doctoral education shapes the academy – what shapes doctoral education?

For universities all over the world, the strategic importance of doctoral education has increased over the last decades. Both locally and internationally we can observe growing activity in three interrelated areas of doctoral education: policy, knowledge formation and practice. Doctoral education is moving and has become a concern for the field of educational development. In a recent Swedish repor

An optimization approach to multi-dimensional time domain acoustic inverse problems

An optimization approach to a multi-dimensional acoustic inverse problem in the time domain is considered. The density and/or the velocity are reconstructed by minimizing an objective functional. By introducing dual functions and using the Gauss divergence theorem, the gradient of the objective functional is found as an explicit expression. The parameters are then reconstructed by an iterative alg