tetracomTTP-FE_EVON_v2
- 1. TETRACOM: Technology Transfer in Computing Systems FP7 Coordination and support action to fund 50 technology transfer projects (TTP) in computing systems. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement n⁰ 609491. TETRACOM coordinator: Prof. Rainer Leupers, leupers@ice.rwth-aachen.de http://www.tetracom.eu | @TetracomProject TETRACOM: Technology Transfer in Computing Systems FP7 Coordination and Support Action to fund 50 technology transfer projects (TTP) in computing systems. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement n⁰ 609491. TETRACOM coordinator: Prof. Rainer Leupers, leupers@ice.rwth-aachen.de http://www.tetracom.eu | @TetracomProject TTP Facts TTP Impact TTP Solution TTP Problem Contact: Igor Škrjanc E-mail: igor.skrjanc@fe.uni-lj.si TETRACOM contribution: 30,131 EUR Duration: 01/9/2014-30/06/2015 Nonlinear System Identification with advanced local linear models Dejan Dovžan, Andrej Zdešar, Igor Škrjanc, Faculty of electrical engineering, University of Ljubljana, Slovenia Martin Mayer, Evon GmbH, Austria Implementation of fuzzy model Implementation of fuzzy model learning / identification algorithm (SUHICLUST) ri…local regions - clusters μi…membership functions on input space defining the local regions - clusters yi…local linear models Is an advance process control environment in C# for the design of model- based and other control concepts for plant optimization. Has its own engine for data gathering. Tools for simple model generation based on historical data. LINEAR PLANT Historic data Linear model identification MPC control, optimization, other control design NONLINEAR PLANT Historic data MPC control, optimization, other control design Linear model identification FUZZY MODEL PRINCIPLE Input-output space is partitioned into smaller regions On each region linear one model is valid Merging the outputs of local models = Nonlinear system approximation FUZZY MODEL FEATURES Simple to understand EffectiveTransparent Locally linear models Simple transition from existing MPC algorithms in XAMControl to MPC for nonlinear systems based on fuzzy model XAMControl Developmental Environment Example of simulation model running in XAMControl (EAF simulator) Initial step: create first cluster and identify local model using least squares method. Identifcation of local regions - using hierarhical partitioning Check the model error. If error higher than threshold continue with splitting else stop. Find the cluster with the highest error Split cluster in the direction of the highest variance Refining local positions of new clusters with Gustaffson-Kessel clustering on local data Refining global positions of all clusters with Gustaffson-Kessel clustering on all data Identification of local model parameters: 𝑦 𝑘 = 𝑎1 𝑦 𝑘 − 1 + ⋯ + 𝑎 𝑛 𝑦 𝑘 − 𝑛 + 𝑏1 𝑢 𝑘 − 1 + ⋯ + 𝑏 𝑚 𝑢 𝑘 − 𝑚 + 𝑟 Calculate membership degrees for all data samples (on input space) Use weighted least squares method for estimation of the local models‘ parameters (membership degrees are weights) Error reached. Return the generated fuzzy model. FUZZY MODEL SUHICLUST – method for fuzzy model identification Implementation of fuzzy model into XAMControl Implementation of SUHICLUST into XAMControl More powerfull XAMControl More optimal MPC control of proceses Better approximation of nonlinear plants Better prediction models for timeseries problems More modelling options