Abstract Symbiotic 10 brings four substantial improvements. First, we extended our clone ofKleecalledJetKleewithlazy memory initialization. With this extension,JetKleecan symbolically execute a function without knowing its context. In SV-COMP, we use it to handle variables. Second, we have implemented the technique calledcompact symbolic executiontoSlowbeast. Third, we have implemented a non-trivialmay-happen-in-parallelanalysis, which improves slicing of parallel programs. Finally, we have implemented support for violation witnesses in the newwitness format 2.0.

Abstract The setup of Symbiotic 8 for Test-Comp 2021 brings radical changes in the test generation for property. Similarly as in Symbiotic 7, we generate tests by running our fork of symbolic executor Klee on the analyzed program. Symbiotic 8, however, runs several instances of Klee in parallel. We run one instance of Klee on the original program and, simultaneously, we create one (intentionally unsound) program slice for every program-terminating instruction in the program and run Klee on these slices. Apart from this principal change, we also improved other components of the tool, mainly the program slicer. Further, our fork of Klee now supports symbolic pointer arithmetics and comparison of symbolic addresses.

Abstract Symbiotic 8 extends the traditional combination of static analyses, instrumentation, program slicing, and symbolic execution with one substantial novelty, namely a technique mixing symbolic execution with k-induction. This technique can prove the correctness of programs with possibly unbounded loops, which cannot be done by classic symbolic execution.Symbiotic 8 delivers also several other improvements. In particular, we have modified our fork of the symbolic executorKleeto support the comparison of symbolic pointers. Further, we have tuned the shape analysis toolPredator(integrated already inSymbiotic 7) to perform better onllvmbitcode. We have also developed a light-weight analysis of relations between variables that can prove the absence of out-of-bound accesses to arrays.

The paper is focused on creating a mathematical model of a magnetic levitation plant and usage of the model for a design of a predictive controller. The magnetic levitation laboratory plant CE 152 by Humusoft Company is used to determine values of model parameters and for real time control experiments. From the control point of view, the CE152 represents a nonlinear and very fast system. Both the mathematical model and the model predictive controller are created using MATLAB/Simulink environment. This environment extended by Real time toolbox is used for real time experiments with the laboratory plant. © ECMS Zita Zoltay Paprika, Péter Horák, Kata Váradi,Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics (Editors).

Stiction is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. In this work, we develop a mixed-integer MPC (Model Predictive Control) formulation including valve dynamics for a sticky valve in order to improve control loop performance. The introduction of the valve nonlinearity into the model prevents the MPC from requesting physically unrealistic control actions due to valve stiction. Simulation studies using a two-tank systems show that, if the deadband value is known apriori, the Mixed-Integer Quadratic Programming (MIQP) can effectively improve the closed-loop performance in the presence of valve stiction. © ECMS Zita Zoltay Paprika, Péter Horák, Kata Váradi,Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics (Editors).

The paper deals with modelling of a magnetic levitation laboratory plant. The goal of the work was to create a nonlinear model in a MATLAB/Simulink environment representing behaviour of a real-time CE152 laboratory plant. The CE152 is a magnetic levitation model developed by Humusoft company. From the control point of view, the CE152 magnetic levitation plant is a nonlinear very fast system. The model of the plant is developed using first principle modelling and subsequently made more precise using real-time experiments. The behaviour of the resulting Simulink model is compared with the behaviour real-time plant. The Simulink model can be further used in the process of controller design. © ECMS Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose (Editors).

The combination of the automatic control theory courses, simulation verification and practical implementation of the designed controller algorithms in real-time conditions is very important for training of the control engineers. This contribution present structure and usage of Self-tuning Controllers Simulink Library (STCSL) for real time control. The STCSL was created for design, simulation verification and especially real-time implementation of single input - single output (SISO) digital self-tuning controllers. The proposed adaptive controllers, which are included in the library, can be divided into three groups (PID controllers, controllers based on the polynomial approach and the controllers derived on the other approaches (minimum variance etc.). This Library is very successfully used in Adaptive Control Course in education practice for design and verification of self-tuning control systems in simulation and real-time conditions. It is suitable also for design and verification of industrial digital controllers. The highly nonlinear laboratory model, the DR300 Speed Control with Variable Load, has been chosen as example for real-time control. The STCSL is available free of charge at the Tomas Bata University internet site - http://www.utb.cz/stctool/.

The paper deals with the problem of modelling and control using the Local Model Network (LMN). The idea is based on development of multiple local models for the whole operating range of the controlled process. The local models are then smoothly connected using the validity or weighting functions to provide a nonlinear global model of the plant. For saving the computational load, linear model is obtained by interpolating the parameters of local models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behaviour of the process. The supervisory program, based on a nonlinear global model, computes desired values of manipulated variables leading to minimum utility consumption. The approach is verified in a control of model of a heat exchanger.