Monika Bakošová

Robust stabilization of a chemical reactor

Robust static output feedback control was applied to a continuous stirred tank reactor with parametric uncertainty and multiple steady states in which exothermic reaction takes place. The problem of robust controller design was converted to a solution of linear matrix inequalities and a computationally simple non-iterative algorithm is presented. The possibility of using robust static output feedback for stabilization of reactors with uncertainty and comparison of robust P and PI controllers with an optimal controller is demonstrated by simulation results.

Neural Network Predictive Control Of A Chemical Reactor.

Model Predictive Control (MPC) refers to a class of algorithms that compute a sequence of manipulated variable adjustments in order to optimi ze the future behaviour of a plant. MPC technology can now be found in a wide variety of ap plication areas. The neural network predictive controller that is discussed in this pap er uses a neural network model of a nonlinear plant to predict future plant performance. The cont roller calculates the control input that will optimize plant performance over a specified future time horizon. In the paper, simulation of the neural network based predictive control of the continuous stirred tank reactor is presented. The simulation results are compared with fuzzy and PID control.

DESIGN OF ROBUST PI CONTROLLERS AND THEIR APPLICATION TO A NONLINEAR ELECTRONIC SYSTEM

Design of Robust PI Controllers and their Application to a Nonlinear Electronic System The principal aim of the paper is to present a possible approach to the design of simple Proportional-Integral (PI) robust controllers and subsequently to demonstrate their applicability during control of a laboratory model with uncertain parameters through the Programmable Logic Controller (PLC) SIMATIC S7-300 by Siemens Company. The proposed and utilized synthesis consists of two steps. The former one is determination of controller parameters area, which ensures the robustly stable control loop and is based on computing/plotting the stability boundary locus while the latter one lies in the final choice of the controller itself relying on algebraic techniques. The basic theoretical parts are followed by laboratory experiments in which the 3rd order nonlinear electronic model has been successfully controlled in various working points.

Robust model predictive control of a laboratory two-tank system

Robust model predictive control was applied for the laboratory two-tank system. The controlled process is modeled as the uncertain polytopic system for the controller design. Two robust model predictive control design strategies were used. The first strategy uses the single Lyapunov function and the second one assumes the parameter-dependent Lyapunov functions. The design problem is converted to the solution of the system of linear matrix inequalities and a simple algorithm is also presented. The controllers designed using both strategies were tested on the laboratory process. The results confirmed that using the parameter-dependent Lyapunov function in the robust model predictive controller design improved the control performance in comparison with the single Lyapunov function based strategy.

Robust and optimal control approach for exothermic reactor stabilization

In this paper, the possibility to stabilise open-loop unstable exothermic reactors with temperature measurements is studied. Moreover, the reactors are considered to be multi-input multi-output systems with parametric uncertainties. Robust static output feedback and optimal controllers are designed for stabilization of the exothermic reactors into their open-loop unstable steady states. Stabilization of reactors is simulated using designed controllers. The possibility of using both types of controllers for energy savings is studied and measured by coolant consumption. The approach is tested with a representative example. Obtained simulations results confirm that the robust static output feedback controllers outperform optimal controllers when systems with parametric uncertainties are controlled.

PIDDESIGN—Software for PID control education

Abstract The aim of this paper is to present the software PIDDESIGN. The software has been developed using MATLAB — Simulink programming environment and uses its graphical user interface to make software more interactive and user friendly. The software represents useful tool for simple step-response-based identification of a controlled process, fast PID controller tuning using various methods and effective evaluation of control performance using simulation of control. The software enables to focus the attention on control performance analysis to increase the quality of control. The properties of the software determine its application especially for educational purposes.

Robust controller design for a chemical reactor

Abstract In this paper, the design of a robust static output feedback controller is presented. This problem is transformed to solving of linear matrix inequality (LMI) problems. A computationally simple LMI based non-iterative algorithm is used. The design procedure guarantees with sufficient conditions the robust quadratic stability and guaranteed cost. The presented approach is applied for robust controller design for an exothermic continuous-time stirred tank reactor (CSTR). The designed robust controller is able to stabilize the CSTR with four uncertain parameters in the entire operating area.

Model predictive control-based robust stabilization of a chemical reactor

The paper addresses an approach to robust stabilization of chemical continuous stirred tank reactors. State feedback was used for the stabilization and the feedback controller was designed using the robust model-based predictive control algorithm in which the symmetric constraints on input and output variables are taken into account. The known strategy was modified by adding integral action to the controller. Parameters of robust feedback controllers with and without integral action were found as solutions of a constrained optimization problem solved on the infinite prediction horizon. The possibility to stabilize chemical reactors with uncertainty using the robust model-based predictive control has been verified by simulations and compared with the optimal linear quadratic control and the model-based predictive control. The obtained results confirm that the robust model-based predictive control provides better results than other approaches.

The Kronecker summation method for robust stabilization applied to a chemical reactor

The paper focuses on robust stabilization where the suitable parameters of a simple continuous-time PI controller are determined through a combination of the Kronecker summation method, sixteen plant theorem, and an algebraic approach to control design in the ring of proper and stable rational functions. The initial theoretical background is followed by an illustrative experiment which includes computation of the controller and verification of control results for a continuous stirred tank reactor with exothermic reaction modelled as a fourth-order interval system.

Adaptive nonlinear control of a continuous stirred tank reactor

The paper deals with continuous-time nonlinear adaptive control of a continuous stirred tank reactor (CSTR). Control strategy is based on the application of a controller consisting of a linear and a nonlinear part. The static nonlinear part is derived as an inversion and exponential approximation of measured or simulated input-output data. Design of the dynamic linear part is based on an approximation of nonlinear elements in the control loop by a continuous-time external linear model with directly estimated parameters. In the control design procedure, polynomial approach with the pole assignment method was used. The nonlinear adaptive control was tested by simulations on a nonlinear model of a CSTR with a consecutive exothermic reaction.