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The purpose of this paper is to present the Single inverted pendulum laboratory model with LSM as a cyber-physical system (CPS). The implementation of the model application into the Distributed Control System infrastructure at the DCAI, FEEI, TU is first described. Pre-prepared program modules from the custom Simulink library Inverted Pendula Modeling and Control, developed by the authors of this paper, are used in subsequent modeling and control design. The contribution to the simulation model level of the DCS infrastructure is described in detail - the nonlinear mathematical model obtained from the library is modified to include the velocity loop, and the necessary parameters are obtained via experimental identification. Finally, a control strategy based on a hybrid control structure is designed and swing-up/stabilizing control algorithms are verified on the laboratory model.

The papers presents an implementation of the predictive state space control algorithm, called Model Predictive Control (MPC). This control algorithm is verified on the Ball and Plate laboratory model, called B&P_KYB, for the reference trajectory tracking. The control algorithm is first verified using the derived nonlinear simulation model in Matlab/Simulink. Since simulation results are acceptable, an experiment is realized on the real laboratory model. The results of the experiment are demonstrated as the time response of the ball position and the voltage.

This paper presents using the theory of optimal control and linear-quadratic approach to the deterministic controller design for nonlinear dynamical system (robot -manipulator). In the first stage are determined the optimal programme trajectories that serve as the required inputs for the feedback tracking control of robot; whereby the feedback is determined from the linearized robot model. The results of this clasical approach are used to design optimal tracking neuro-controller (OTNC) with quadratic cost function. The proposed OTNC scheme will be demonstrated on a typical nonlinear plant (two-link) manipulator.The computer simulation of control is done by the simulation language MATLAB and the presented results are analyzed.

The aim of this paper is to design of control algorithm for the Magnetic levitation system using an exact input — output feedback linearization method. In this paper is given nonlinear simulation model of the Magnetic levitation based on mathematical model of the Magnetic levitation system. Designed control algorithm together with simulation model of the Magnetic levitation is implemented into control structure with purpose of control on steady state defined by a reference trajectory, which is verified in Matlab/Simulink language.

The paper deals with the design of optimization predictive control algorithms, which are based on the input-output ARX and CARIMA models of the dynamic systems. The paper also deals with the simulation verification of predictive control algorithms on a nonlinear hydraulic system with different settings of various control algorithms parameters, with a system subject to noise, or disturbance. There is mentioned a brief theoretical derivation and the particular formulas for concrete predictive control methods in the paper. The paper includes a flow chart diagram for predictive control methods programming and the control structure, where predictive control algorithms were used in nonlinear hydraulic system control. Nonlinear differential equations, which express hydraulic system’s dynamic are presented, too. The results of generalized predictive control algorithms comparison are expressed by the control action and the hydraulic system’s output in a graphs form.

The paper provides two approaches for design of generalized predictive control (GPC) algorithm for nonlinear and time-variant dynamic system. In classical approach of GPC strategy is considered method of instantaneous linearization for calculating of linearized model parameters from known analytic description of nonlinear system. The other purpose of this paper is to show an intelligent approach in which a feed-forward neural network (multi layer perceptron-MLP) is used for modeling and predictive control of the non-linear system. The possibility of an on-line estimation of actual parameters from off-line trained neural model of the non-linear system using the gain matrix is considered in the algorithm of GPC. The neural model is linearized by means method of instantaneous linearization in each sample and an estimated parameters from neural NARX model of the non-linear system are used for design of GPC algorithm. The validity of classical and neural GPC strategy is tested by computer simulations in Matlab/Simulink language using architecture of S-functions of the library PredicLib.