The contribution presents a class of Single Input Single Output (SISO) discrete self-tuning controllers suitable for industrial applications. The proposed adaptive controllers can be divided into three groups. The "rst group covers PID adaptive algorithms with using of traditional methods. The second group is based on polynomial solutions of control problems and the third group is derived from the use of the minimization of linear quadratic criterion. All types of algorithms were uni"ed and incorporated into a Matlab -like Toolbox for self-tuning control.

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.

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The paper deals with adaptive control of a continuous stirred tank reactor (CSTR). A nonlinear model of the process is approximated by a continuous-time external linear model. The parameters of the CT external linear model of the process are estimated using a corresponding delta model. The control system with two feedback controllers is considered. The controller design is based on the polynomial approach. The resulting proper controllers ensure stability of the control system as well as asymptotic tracking of step references and step load disturbance attenuation. The adaptive control is tested on the nonlinear model of the CSTR with a consecutive exothermic reaction.

The paper is focused on creating a model of Inverted pendulum system and subsequent usage of this model to design a predictive controller of inverted pendulum system. The model is obtained on base of mathematical physical analysis of the system. Unknown parameters of the model are obtained from real-time experiments on the PS600 Inverted pendulum system. The model is designed in MATLAB/Simulink environment. The model was created with respect to most nonlinearities contained in the system. Nonlinearities are caused by fundamental principles of the system and by friction between individual parts of the system. Thus, the model is highly non-linear and therefore linearization around working point was performed and continuous linearized model was calculated as well as its discrete version. The discrete linear model was used to design predictive controller which was also verified by real time experiments.