Daniel Honc

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.

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 and Identification of Magnetic Levitation Model CE 152/Revised

Paper describes procedure of first principle modelling and experimental identification of Magnetic Levitation Model CE 152. Author optimized and simplified dynamical model to a minimum what is needed to characterize given system for the simulation and control design purposes. Only few experiments are needed to estimate the unknown parameters. Model quality is verified in the feedback control loop where the real and simulated data are compared.

Analytic Model Predictive Controller in Simple Symbolic Form

Paper deals with an analytic solution of Model Predictive Controller in simple symbolic form. Process is approximated with a first order dynamical model. Special choice of prediction and control horizons is considered, so the symbolic solution is still applicable, and the controller has interesting “predictive” feature in case of known future set-point course. Such a controller can be used in simple devices like PLCs or microcontrollers without need of matrix operations. Its advantage is that the controller reacts to the process model parameters and penalty parameter change so the control can be very fast and efficient even in adaptive manner.

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.