Search

Research in the field of underactuated systems shows that control algorithms which take the natural dynamics of the system’s underactuated part into account are more energy-efficient than those utilizing fully-actuated systems. The purpose of this paper to apply the two-degrees-of-freedom (feedforward/feedback) control structure to design a swing-up manoeuver that involves tracking the desired trajectories so as to achieve and maintain the unstable equilibrium position of the pendulum on the cart system. The desired trajectories are obtained by solving the boundary value problem of the internal system dynamics, while the optimal state-feedback controller ensures that the desired trajectory is tracked with minimal deviations. The proposed algorithm is verified on the simulation model of the available laboratory model actuated by a linear synchronous motor, and the resulting program implementation is used to enhance the custom Simulink library Inverted Pendula Modeling and Control, developed by the authors of this paper.

This paper presents the development of a new Aerodynamic Ball Levitation Laboratory Plant at the Center of Modern Control Techniques and Industrial Informatics (CMCT&amp, II). The entire design process of the plant is described, including the component selection process, the physical construction of the plant, the design of a printed circuit board (PCB) powered by a microcontroller, and the implementation of its firmware. A parametric mathematical model of the laboratory plant is created, whose parameters are then estimated using a nonlinear least-squares method based on acquired experimental data. The Kalman filter and the optimal state-space feedback control are designed based on the obtained mathematical model. The designed controller is then validated using the physical plant.

This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

This paper deals with hybrid modelling and control of cyber-physical systems, which are part of a recently introduced phenomenon Industry 4.0. As a case study we look at a hydraulic system with hybrid dynamics represented by coupled tanks. The dynamical system contains two discrete modes, specifically with and without interaction. The nonlinear hybrid system was modelled using s-functions and then its linear description was created in the discrete PWA representation with the help of HYSDEL modelling framework. Both s-functions and HYSDEL are part of MATLAB/Simulink environment. After validation of the discrete PWA representation in comparison with the nonlinear hybrid system, LQR synthesis with reference trajectory tracking was designed for both discrete modes. Subsequently optimal control designed on the linear hybrid system in the discrete PWA representation was verified on the nonlinear hybrid system.

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.

This article deals with experimental identification and control of laboratory helicopter model CE 150 manufactured by company Humusoft. Structure of the identified system was approximated by linear black-box models. Discrete Input/Output Auto-Regressive Moving Average model with eXternal input (ARMAX) and its state space equivalent were used. Parameters of the models were estimated by regression techniques using System Identification Toolbox for Matlab. Acquired models were validated using simulations, residual analysis and real-time control. Input/output data necessary for identification were obtained by measurements from laboratory model and were processed using Real-Time Toolbox for Matlab. Based on acquired mathematical models input/output and state space controllers were designed (input/output pole placement with integration, state space pole placement with integration and observer). Designed controllers were implemented in Matlab environment using the Real Time Toolbox and their performance was verified by real-time control of the helicopter model.

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.