An effective but yet simple approach is introduced to automatically attain a dynamic feedforward control law for non-linear dynamic systems represented by discrete-time local model networks (LMN). In this context, feedback linearization is applied to the generic model structure of LMN and the resulting input transformation is used as model inverse.
COBISS.SI-ID: 11294804
The paper proposes a framework, which involves using an interval model for describing the uncertain or variable dynamics of the process. The framework employs a particle swarm optimization algorithm for obtaining the best performing PID controller with regard to several possible criteria, but at the same time taking into account the complementary sensitivity function constraints, which ensure robustness within the bounds of the uncertain parameters’ intervals.
COBISS.SI-ID: 11237972
In this paper a new approach called evolving principal component clustering is applied to a data stream. Regions of the data described by linear models are identified. The method recursively estimates the data variance and the linear model parameters for each cluster of data.
COBISS.SI-ID: 11069780
This paper presents a new approach to design of experiments (DoE), based on an evolving fuzzy model structure and a supervised hierarchical clustering algorithm. DoE is the field that deals with the problem of how to design the most optimal and economic experimentation. The goal is to identify a highly nonlinear and possibly high-dimensional system, together with the minimal experimental effort required.
COBISS.SI-ID: 10639700
In this paper, the idea of using an evolving method as a base for the fault-detection/monitoring system is tested. The system is based on the evolving fuzzy model method. This method allows us to model the nonlinear relations between the variables with the Takagi-Sugeno fuzzy model. The method uses basic evolving mechanisms to add and remove clusters and the adaptation mechanism to adapt the clusters' and local models' parameters.
COBISS.SI-ID: 10889556