František Dušek

State-Space Constrained Model Predictive Control.

Constrained State-space Model Predictive Control is presented in the paper. Predictive controller based on incremental linear state-space process model and quadratic criterion is derived. Typical types of constraints are considered – limits on manipulated, state and controlled variables. Control experiments with nonlinear model of multivariable laboratory process are simulated first and real experiment is realized afterwards.

MATLAB/Simulink support for GUNT control units

Paper deals with a Dynamic Linked Library calling in real time from MATLAB and Simulink. S-function as user Simulink block is created to enable communication with GUNT control units equipped with LabJack U12 data acquisition device and to extend significantly their potential for the purpose of automation subjects teaching, student thesis and research activities.

Sensor Fusion: An Application to Localization and Obstacle Avoidance in Robotics Using Multiple IR Sensors

Sensor fusion brings the advantage of combining data from various sensors and there by generating a more accurate prediction or estimation of data. Over dependency of sensor and estimation from unreliable data are the most challenging tasks in mobile robotics. In this paper, a framework of sensor fusion technique is presented. The data from the multiple sensors are fused together and the parameters and crash time are estimated. The experiment results show that the sensor fusion technique provides solution to over dependency of sensor and problems with estimation of data from unreliable data. The technique finds application in obstacle avoidance and localization of mobile robots.

Comparitive study of predictive controllers for trajectory tracking of non-holonomic mobile robot

The paper deals with predictive control of non-holonomic mobile robot. The basic nonlinear kinematic equation is linearized into two different linear time varying models based on frame of reference — world coordinates and local coordinate of mobile robot. The non-linear model predictive control is applied to the trajectory tracking problem of a non-holonomic mobile robot with these models. The control law is derived from a cost function which penalizes the state tracking error, control effort and terminal state deviation error. Various simulation experiments are conducted and a comparative analysis has been made with respect to state-of-the-art approaches.

Model Predictive Control of Trajectory Tracking of Differentially Steered Mobile Robot

The paper is focused on trajectory tracking of differential drive mobile robot. The mathematical model of dynamics and kinematics of the mobile robot is considered, based on first principle approach. The dynamic behaviour of engines and chassis, coupling between engines and wheels and basic geometric dimensions are taken into consideration. Reference tracking of linear and angular velocities are achieved by model predictive control of supply voltage of both the drive motors by considering constraints on controlled variable, manipulated variable as well as state variables. Simulation results are provided to demonstrate the performance of proposed control strategy in MATLAB simulation environment.

Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors an approach based on Kalman filter

Kalman filters have gained immense research attention in robotics, throughout the last decades. Among the applications, localization of robots through Kalman filters proved promising results. This paper presents an application of sensor fusion for prediction of orientation and depth to wall/obstacle by fusing the inputs from three IR range finders. The experimental result demonstrates the capability of Kalman filter to predict the parameters precisely, from noisy sensor inputs. The technique find application in determining the position and orientation from wall which will be helpful in obstacle avoidance decision making, automatic parking of automobiles etc.

GUNT RT 010 Experimental Unit Modelling and Predictive Control Application

First principle process model of GUNT RT 010 experimental unit is designed for the purpose of various modern control methods laboratory testing and applications. Unknown parameters are estimated from experimental data. Model Predictive Control is applied to the system to verify quality of the model and to demonstrate its use. Two approaches are considered according to process model – transfer function and state-space model. Known future set-point and constraints on input, state and output variable are part of the controller – optimal control actions are calculated by quadratic programming.

Novel Multivariable Laboratory Plant.

A novel multivariable laboratory plant is presented. The process is a variation of known “four interconnected water tanks”. The novelty of the process lies in a way how to ensure multi-variability. Pneumatic volumes above the water levels are connected together and orifices are placed into those circuits. Cross interactions exists only in transient states so the working area is not reduced. First principle nonlinear mathematical model is derived and presented together with its linearized form and experimental identification of unknown parameters.

Inverted Pendulum Optimal Control Based on First Principle Model

This paper describes the design procedure of nonlinear dynamical model of a real system—inverted pendulum—cart with pendulum. The aim of the paper is to create a mathematical model based on known constructional, mechanical and electrical characteristics of the physical system. Such a model is linearized into standard linear time-invariant state-space model where the input is motor power voltage and the outputs are cart position and pendulum angle. A linear model is used for discrete-time LQ controller design—state variables are estimated and the cart position is controlled with pendulum in upright metastable position.

Modelling of ball and plate system based on first principle model and optimal control

This paper presents modelling of ball and plate systems based on first principles by considering balance of forces and torques. A non-linear model is derived considering the dynamics of motors, gears, ball and plate. The non-linear model is linearized near the operating region to obtain a standard state space model. This linear model is used for discrete optimal control of the ball and plate system — the trajectory of the ball is controlled by control voltages to the motor.