A method for constructing a small-sized unmanned vessel and its automatic wiring

In the article, the author proposes a method for constructing a small-sized unmanned vessel with a communication system with its operator, receiving data on control actions for the purpose of navigating the vessel in automatic and manual modes along a given trajectory, transmitting the necessary kinematic and navigation data for identifying mathematical models of the movement of sea-surface vessels, setting up the automatic steering in real time, as well as entering proofreading into sensor readings via the data transmission protocol via TCP-IP stack on public Wi-Fi radio frequencies. The method of constructing a small-sized unmanned vessel and its automatic wiring allows us to work out the principles of creating full-scale, fully functional unmanned cargo, passenger and military vessels. It allows you to work out the principles of creating automatic control systems for a given trajectory for fully functional crewed and unmanned vessels for various purposes. This method can be used to study the principles of construction and identification of mathematical models of vessel movement. The method proposed in the article was implemented at the competition “Talent and Success” of the “Sirius” educational center, where it successfully won the championship in the category “Marine Intelligence”.

Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

Motion Sensors in Automatic Steering of Hearing Aids

A requirement for modern hearing aids is to evaluate a listening environment for the user and automatically apply appropriate gain and feature settings for optimal hearing in that listening environment. This has been predominantly achieved by the hearing aids' acoustic sensors, which measure acoustic characteristics such as the amplitude and modulation of the incoming sound sources. However, acoustic information alone is not always sufficient for providing a clear indication of the soundscape and user's listening needs. User activity such as being stationary or being in motion can drastically change these listening needs. Recently, hearing aids have begun utilizing integrated motion sensors to provide further information to the hearing aid's decision-making process when determining the listening environment. Specifically, accelerometer technology has proven to be an appropriate solution for motion sensor integration in hearing aids. Recent investigations have shown benefits with integrated motion sensors for both laboratory and real-world ecological momentary assessment measurements. The combination of acoustic and motion sensors provides the hearing aids with data to better optimize the hearing aid features in anticipation of the hearing aid user's listening needs.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

Towards the Autonomy: Control Systems for the Ship in Confined and Open Waters

The concept of the Marine Autonomous Surface Ship (MASS) requires new solutions in many areas: from law, through economics, social sciences, environmental issues to the technology and even ethics. It also plays a central role in the work of numerous research teams dealing with the ship motion control systems. This article presents the results of the experiments with application of the selected control methods in automatic steering of the movement of an autonomous ship in the two regimes: during the maneuvers at low speed (in a harbor confined waters) and during the lake trials in open water conditions. In the first case, multidimensional state controller synthesized with Linear Matrix Inequalities (LMI) algorithms was used, while, in the second case, Model Predictive Control (MPC) control was adopted. The object for which the experiments were carried out was 1:24 scale model of the Liquefied Natural Gas (LNG) carrier. The paper presents also the design of the measurement and control system and the user interface. The experiments were conducted in the natural conditions on the lake. The results of the experiments indicate the fundamental role of the measurement system in the process of controlling an autonomous ship.

Robust Dynamic Toolface Calculation for Automatic Steering Control with Rotary Steerable Systems Under Extreme Conditions

Reliable toolface calculation is essential for achieving robust automatic steering control with rotary steerable systems (RSS). For RSS with fully rotating sensor packages, this task becomes particularly challenging under extreme conditions, where signal-to-noise ratio (SNR) of measurements from one or more sensors reduce significantly (e.g., while drilling near-vertical wells, along dip, towards magnetic north, in the vicinity of casing and/or under severe vibration and stick-slip). To ensure robust toolface control for fully rotating RSS under these conditions, this paper proposes a novel dynamic toolface calculation method. The proposed dynamic toolface calculation method of the new-generation fully rotating RSS overcomes the challenge of achieving robust toolface control despite extreme drilling conditions, by bringing together real-time health monitoring, online sensor calibration and novel sensor fusion techniques. Considering that robust toolface control is the heart of any drilling automation architecture with RSS, this technology is key to enable advanced drilling control strategies in the future.