The paper presents the identification process of the mathematical model parameters of a differential-drive two-wheeled mobile robot. The values of the unknown parameters of the dynamics model were determined by carrying out their identification offline with the Levenberg-Marguardt method and identification online with the Recursive least-squares method. The authors compared the parameters identified by offline and online methods and proposed to support the recursive least squares method with the results obtained by offline identification. The correctness of the identification process of the robot dynamics model parameters, and the operation of the control system was verified by comparing the desired trajectories and those obtained through simulation studies and laboratory tests. Then an analysis of errors defined as the difference between the values of reference position, orientation and velocity, and those obtained from simulations and laboratory tests was carried out. On itd basis, the quality of regulation in the proposed algorithm was determined.

In the paper the three different mathematical models of motion of a spin-stabilized, conventional artillery projectile, possessing at least trigonal symmetry, have been introduced. The vector six-degrees-of-freedom (6-DOF) differential equations of motion are an updated edition of those published by Lieske and McCoy and are consistent with STANAG 4355 (Ed. 3). The mathematical models have been used to developing software for simulating the flight of the Denel 155mm Assegai M2000 series artillery projectile and to conduct comprehensive research of the influence of the applied model and integration step on the accuracy and time of computation of projectile trajectory.

This paper considers the problem of flying a UAV along a given trajectory at speeds close to the speed of sound and above. A novel pitch channel control system is presented using the example of a trajectory with rapid and large changes in flight height. The control system uses a proportional–integral–differential (PID) controller, whose gains were first determined using the Ziegler-Nichols II method. The determined gains were then optimised to minimise height error using a recurrent back-propagation neural network (PIDNN), with which new controller gains were determined, which is also a novelty of this study. Simulations were carried out for flights at subsonic speeds close to the speed of sound and supersonic speeds, at low and high altitudes. The simulations showed that determining controller gains using a recurrent neural network significantly minimises height errors and increases the flexibility of the PID controller.

The paper presents the step by step process of modelling the movement of dispersed group of mobile robots using the MATLAB Simulink and Simscape Multibody software. As a modelled object, a group of laboratory robots, TurtleBot2, was adopted. First, the construction of a robot CAD model was described. Next, the control system developed in Simulink was described. At the end, the robot components were defined in Simscape Multibody and a group movements analysis was carried out.

The main objective of this article is to obtain equations of motion of the spin–stabilized projectile in the presence of non–constant wind. Introducing models allowing utilization of inhomogeneous wind is dictated by new possibilities created by the use of e.g. lidars in the Fire Control Systems (FCS). Constant feed of wind data can replace meteorological messages, increasing the FCS effectiveness. Article contains results of projectile flight simulations which indicate the positive effect that the derived explicit form of the model has when considering software development for modern Fire Control Systems.