Quasi-Bayes algorithm, combined with stabilized forgetting, provides a tool for efficient recursive estimation of dynamic probabilistic mixture models. They can be interpreted either as models of closedloop with switching modes and controllers or as a universal approximation of a wide class of non-linear control loops.Fully probabilistic control design extended to mixture models makes basis of a powerful class of adaptive controllers based on the receding-horizon certainty equivalence strategy.Paper summarizes the basic elements mentioned above, classifies possible types of control problems and provides solution of the key one referred to as 'simultaneous' design. Results are illustrated on mixtures with components formed by normal auto-regression models with external variable (ARX).

Real-world, multidimensional, dynamic, non-linear processes typically exhibit many distinct modes of operation. Mixtures of dynamic models improve greatly on traditional one-component linear models in this context. Improved prediction then points the way to effective adaptive control design. This paper presents the experience gained under the EU Project, ProDaCTool, in designing and implementing advisory systems, based on dynamic mixtures, in diverse domains: urban traffic regulation, therapy recommendations in nuclear medicine, and operator support for metal-strip rolling mills. Efficient, recursive estimation of the dynamic mixtures from archive data is accomplished using the quasi-Bayes (QB) algorithm, implemented with dedicated software developed within ProDaCTool. The advisory systems are designed using the probabilistic control design technique presented in the previous paper. Highly encouraging prediction and performance enhancements are reported for the applications considered.

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An exploitation of prior knowledge in parameter estimation becomes vital whenever measured data is not informative enough. Elicitation of quantified prior knowledge is a well-elaborated art in societal and medical applications but not in the engineering ones. Frequently required involvement of a facilitator is mostly unrealistic due to either facilitator's high costs or complexity of modelled relationships that cannot be grasped by humans. This paper provides a facilitator-free approach based on an advanced knowledgesharing methodology. It presents the approach on commonly available types of knowledge and applies the methodology to a normal controlled autoregressive model.

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