The subject of discussion is a tank gun horizontal stabiliser. In order to simplify identification, the system was divided into appropriate functional parts. Then, via laboratory tests, dynamic and static characteristics of those parts were obtained, and numerical values of coefficients of suitable mathematical model of the system were determined. The structural scheme of the overall system was derived on the basis of the obtained static characteristics and transfer functions of individual parts of the system, and based on the knowledge about the system feedbacks. For the investigation of the considered control system, one applied a method of computer simulations. The mathematical model and its numerical implementation was experimentally verified. To this aim: • the results of model testing (for open-loop system) were compared with the existing results of experimental tests carried-out on a real tank; • tests of the complete closed-loop system were carried -out and their results were compared with the results of numerical computations. The results of experimental and model simulation investigations showed that the mathematical model and its numerical implementation was worked-out correctly.

This paper proposes an autonomous obstacle avoidance method combining improved A-star (A*) and improved artificial potential field (APF) to solve the planning and tracking problems of autonomous vehicles in a road environment. The A*APF algorithm to perform path planning tasks, and based on the longitudinal braking distance model, a dynamically changing obstacle influence range is designed. When there is no obstacle affecting the controlled vehicle, the improved A* algorithm with angle constraint combined with steering cost can quickly generate the optimal route and reduce turning points. If the controlled vehicle enters the influence domain of obstacle, the improved artificial potential field algorithm will generate lane changing paths and optimize the local optimal locations based on simulated annealing. Pondering the influence of surrounding participants, the four-mode obstacle avoidance process is established, and the corresponding safe distance condition is analyzed. A particular index is introduced to comprehensively evaluate speed, risk warning, and safe distance factors, so the proposed method is designed based on the fuzzy control theory. In the tracking task, a model predictive controller in the light of the kinematics model is devised to make the longitudinal and lateral process of lane changing meet comfort requirements, generating a feasible autonomous lane-change path. Finally, the simulation was performed in the Matlab/Simulink and Carsim combined environment. The proposed fusion path generation algorithm can overcome the shortcomings of the traditional single method and better adapt to the dynamic environment. The feasibility of the obstacle avoidance algorithm is verified in the three-lane simulation scenario to meet safety and comfort requirements.

The subject of this paper is a real stabilising and tracking control system-namely, the tank gun horizontal stabiliser. The simulation investigations of the influence of regulation potentiometers settlings on stabilisation exactness and transient processes quality were carried-out using a verified mathematical model of the system. The author analysed the possibilities of improving performance characteristics of the stabiliser via altering of feedback's gain coefficients as well as the influence of disturbing inputs amplitude and frequency (propagated from the hull on the gunturret) on stabilisation exactness of a given position. In the result of model investigations, it was found that it would be impossible to improve significantly the stabiliser performance quality with its present structure. For this reason, one investigated the possibilities of adding new feedbacks and their influence on the stabilisation quality. The introduced feedbacks improved performance parameters of the stabiliser by about thirty to fifty percent.

In this paper we propose a sensor-based navigation method for navigation of wheeled mobile robot, based on the Kohonen self-organising map (SOM). We discuss a sensor-based approach to path design and control of wheeled mobile robot in an unknown 2-D environment with static obstacles. A strategy of reactive navigation is developed including two main behaviours: a reaching the middle of a collision-free space behaviour, and a goal-seeking behaviour. Each low-level behaviour has been designed at design stage and then fused to determine a proper actions acting on the environment at running stage. The combiner can fuse low-level behaviours so that the mobile robot can go for the goal position without colliding with obstacles one for the convex obstacles and one for the concave ones. The combiner is a softswitch, based on the idea of artificial potential fields, that chooses more then one action to be active with diRerent degrees at each time step. The output of the navigation level is fed into a neural tracking controller that takes into account the dynamics of the mobile robot. The purpose of the neural controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. Computer simulation has been conducted to illustrate the performance of the proposed solution by a series of experiments on the emulator of wheeled mobile robot Pioneer-2DX.

In this work, a novel approach to designing an on-line tracking controller for a nonholonomic wheeled mobile robot (WMR) is presented. The controller consists of nonlinear neural feedback compensator, PD control law and supervisory element, which assure stability of the system. Neural network for feedback compensation is learned through approximate dynamic programming (ADP). To obtain stability in the learning phase and robustness in face of disturbances, an additional control signal derived from Lyapunov stability theorem based on the variable structure systems theory is provided. Verification of the proposed control algorithm was realized on a wheeled mobile robot Pioneer–2DX, and confirmed the assumed behavior of the control system.

The Bulletin of the Polish Academy of Sciences: Technical Sciences (Bull.Pol. Ac.: Tech.) is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred: Artificial and Computational Intelligence, Biomedical Engineering and Biotechnology, Civil Engineering, Control, Informatics and Robotics, Electronics, Telecommunication and Optoelectronics, Mechanical and Aeronautical Engineering, Thermodynamics, Material Science and Nanotechnology, Power Systems and Power Electronics.

Journal Metrics: JCR Impact Factor 2018: 1.361, 5 Year Impact Factor: 1.323, SCImago Journal Rank (SJR) 2017: 0.319, Source Normalized Impact per Paper (SNIP) 2017: 1.005, CiteScore 2017: 1.27, The Polish Ministry of Science and Higher Education 2017: 25 points.

Abbreviations/Acronym: Journal citation: Bull. Pol. Ac.: Tech., ISO: Bull. Pol. Acad. Sci.-Tech. Sci., JCR Abbrev: B POL ACAD SCI-TECH Acronym in the Editorial System: BPASTS.

This paper presents a control concept for a single-axle mobile robot moving on the horizontal plane. A mathematical model of the nonholonomic mechanical system is derived using Hamel’s equations of motion. Subsequently, a concept for a tracking controller is described in detail. This controller keeps the mobile robot on a given reference trajectory while maintaining it in an upright position. The control objective is reached by a cascade control structure. By an appropriate input transformation, we are able to utilize an input-output linearization of a subsystem. For the remaining dynamics a linear set-point control law is presented. Finally, the performance of the implemented control law is illustrated by simulation results.