Vladimír Bobál

Adaptive predictive control of time-delay systems

Design of an optimal controller for higher-order or time-delay systems often leads to complex control algorithms. One of the possibilities of control of such processes is their approximation by a lower-order model with a time-delay (dead time). These time-delay processes can be effectively handled by the Model-based Predictive Control (MPC) method. The paper deals with design of an algorithm for adaptive predictive control of higher-order processes, which are approximated by a second-order model of the process with a time-delay. Most processes in industrial practice are characterized by nonlinear behavior and contain uncertainties. The adaptive control strategy is one of the possible approaches to optimal control of such systems. The proposed adaptive predictive controller for control of the time-delay system was tested and verified by simulation of a model of a laboratory heat exchanger which was obtained from measured experimental data.

Predictive control of the heat exchanger using Local Model Network

The paper deals with the problem of modeling and control of nonlinear processes using the Local Model Network (LMN). The idea is based on development of the local linear models for the whole operating range of the controlled process. The nonlinear plant is then approximated by a set of locally valid sub-models, which are smoothly connected using the validity function. For saving the computational load, linear model is obtained by interpolating these linear models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behavior of the process. The approach is verified in a real-time control of Multifunction Process Control Teaching System (MPCTS) - the Armfield PCT 40.

Self-tuning control of nonlinear servo system: Comparison of LQ and predictive approach

The majority of processes met in the industrial practice have stochastic characteristics and eventually they embody nonlinear behaviour. Traditional controllers with fixed parameters are often unsuitable for such processes because their parameters change. The changes of process parameters are caused by changes in the manufacturing process, in the nature of the input materials, fuel, machinery use (wear) etc. Fixed controllers cannot deal with this. One possible alternative for improving the quality of control for such processes is the use of adaptive control systems. Different approaches were proposed and utilized. One successful approach is represented by self-tuning controller (STC). This approach is also called system with indirect adaptation (with direct identification). The main idea of an STC is based on the combination of a recursive identification procedure and a selected controller synthesis. In this paper, the standard STC (non-predictive) approach is verified and compared with STC based on the Model Predictive Control (MPC). The verification of both methods was implemented by the real-time control of a highly nonlinear laboratory model, the DR300 Speed Control with Variable Load.

Identification and self-tuning predictive control of heat exchanger

Heat exchange belongs to the class of basic thermal processes which occur in a range of industrial technologies, particularly in the energetic, chemical, polymer and rubber industry. The process of heat exchange is often implemented by through-flow heat exchangers. It is apparent that for an exact theoretical description of dynamics of heat exchange processes it is necessary to use partial differential equations. Heat exchange is namely a process with distributed parameters. It is also necessary to take into account its nonlinear and stochastic character. In spite of these facts, most of thermal equipment is controlled by digital modifications of PID controllers at present. This paper deals with identification of a dynamic behavior of a through-flow heat exchanger and a design of an self-tuning predictive controller for its control. The designed controller was verified by a real-time control of experimental laboratory heat exchanger.

Modelling and Predictive Control of a Nonlinear System Using Local Model Network

Abstract In this work the optimization of the local model network structure and predictive control that utilize the local model network to predict the future response of a plant is studied. The main idea is based on development of the local linear models for the whole operating range of the controlled process. The local models are identified from measured data using clustering and local least squares method. The nonlinear plant is then approximated by a set of locally valid sub-models, which are smoothly connected using the validity function. The manipulated variable adjustments are computed through optimization at each sampling interval. The parameters of the plant at each sampling point are derived from the linearization of local model network. The proposed identification and control method is illustrated by the simulation study on the nonlinear process.

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.

Design of controllers for processes with time delay by polynomial method

The paper deals with design of controllers for integrating and unstable time delay systems. The proposed method is based on the time delay approximation and the polynomial approach. A simple control structure with two feedback controllers is considered. Resulting continuous-time controllers obtained via polynomial equations and the LQ control technique ensure asymptotic tracking of step references as well as step load disturbances attenuation. Simulation results are presented to illustrate the proposed method.

NEW LABORATORY COURSE FOR CONTROL EDUCATION

Abstract This paper presents new control laboratory course designed at the Faculty of Applied Informatics for the undergraduate courses in Automatic control. Experience obtained during the building process of Control Laboratory and the organization of student assignments for laboratory course is described. The objectives of the laboratory course are to integrate the fundamental skills from theoretical courses to solve engineering problems in dynamics and control and to encourage the problem-based learning.

Measurable Error Compensation with GPC in a Heat-Exchanger with a Traffic Delay

This paper demonstrates use of Model Predictive Control (MPC) to system control with delay. Generalized Predictive Control (GPC) method was selected to demonstrate the ability to both control the system and compensate the measurable disturbance while bot the system and the disturbance are delayed. For the realization the MATLAB/SIMULINK program environment was used with system parameters based on the laboratory system. GPC method was chosen and its ability to compensate the outer disturbance with delay was verified by simulation of system control based on real laboratory model. Control algorithm and simulation were realized in MATLAB/SIMULINK program environment. Results have proven capabilities of GPC method to control and compensate error in stable, oscillatory and non-minimum phase systems with traffic delay. Additionally, real model parameters were selected to test a possibility of realization.

Automatic Cost-Optimal Power Balance Control

The article presents application of evolutionary algorithm for cost-optimal dispatch of ancillary services in power transmission system of the Czech Republic. The problem is formulated as a nonlinear optimization problem taking into account both continuous and discrete control variables. The optimal setting of control variables such as schedule for activation of regulation reserves, which minimizes the regulation energy costs while maintaining required control performance, are determined via evolution algorithm. The proposed control scheme is tested on historical data from the transmission system operator.