CONTROL SYSTEM DESIGN AND AUTONOMY FOR TWO-WHEELED MOBILE ROBOT

Abstract

Model Predictive Control is an advanced process control method that used while meeting a set of constraints. From an engineering point of view, the MPC method of designing control systems is attractive, because is relatively simple in design, including for solving complex production problems. This method is similar to the classical synthesis of a control system based on a linearquadratic controller (LQR). The key difference between MPC and LQR is that predictive control solves the optimization problem within a sliding time horizon, while the linear quadratic method used to solve the same problem over a fixed time window. The paper considers a method for constructing two-wheeled mobile robot control system using Model Predictive Control. The process of building a mathematical model of the mechanical system of the robot is given, as well as the linearization of the resulting model is performed. The basic principles of constructing a control system based on MPC for linear systems without external disturbances, as well as using an observer to assess the state of the model under the influence of additive white Gaussian noises, are presented. A variant of the synthesis of a control system with imposed restrictions on the input signal is considered. Also presented is a method for determining the position of a two-wheeled robot in space using a vision system, which is based on the use of a neural network. The architecture of the used model is given, as well as a stereo camera, which used to build an image depth map. In addition to the above, the work describes in detail the principle of the deep learning model – YOLOv3, which based on several blocks of input data processing. A detailed description of the implementation of a stereo camera in conjunction with an artificial neural network model using the Python programming language and libraries for working with video data and a stereo camera is presented.