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Improving wastewater treatment processes is becoming increasingly important, due to more stringent effluent quality requirements, the need to reduce energy consumption and chemical dosing. This can be achieved by applying artificial intelligence. Machine learning is implemented in two domains: (1) predictive control and (2) advanced analytics. This is currently being piloted at the integrated validation plant of PUB, Singapore's National Water Agency. (1) Primarily, predictive control is applied for optimised nutrient removal. This is obtained by application of a self-learning feedforward algorithm, which uses load prediction and machine learning, fine–tuned with feedback on ammonium effluent. Operational results with predictive control show that the load prediction has an accuracy of ≈88%. It is also shown that an up to ≈15% reduction of aeration amount is achieved compared to conventional control. It is proven that this load prediction-based control leads to stable operation and meeting effluent quality requirements as an autopilot system. (2) Additionally, advanced analytics are being developed for operational support. This is obtained by application of quantile regression neural network modelling for anomaly detection. Preliminary results illustrate the ability to autodetect process and instrument anomalies. These can be used as early warnings to deliver data-driven operational support to process operators.

This paper presents a multipurpose UAV (unmanned aerial vehicle) for mountain rescue operations. The multi-rotors based flying platform and its embedded avionics are designed to meet environmental requirements for mountainous terrain such as low temperatures, high altitude and strong winds, assuring the capability of carrying different payloads (separately or together) such as: avalanche beacon (ARTVA) with automatic signal recognition and path following algorithms for the rapid location of snow-covered body; camera (visible and thermal) for search and rescue of missing persons on snow and in woods during the day or night; payload deployment to drop emergency kits or specific explosive cartridge for controlled avalanche detachment. The resulting small (less than 5 kg) UAV is capable of full autonomous flight (including take-off and landing) of a pre-programmed, or easily configurable, custom mission. Furthermore, the autopilot manages the sensors measurements (i.e. beacons or cameras) to update the flying mission automatically in flight. Specific functionalities such as terrain following were developed and implemented. Ground station programming of the UAV is not needed, except compulsory monitoring, as the rescue mission can be accomplished in a full automatic mode.

This paper presents a survey of the autopilot systems for small or micro unmanned aerial vehicles (UAVs). The objective is to provide a summary of the current commercial, open source and research autopilot systems for convenience of potential small UAV users. The UAV flight control basics are introduced first. The radio control system and autopilot control system are then explained from both the hardware and software viewpoints. Several typical off-the-shelf autopilot packages are compared in terms of sensor packages, observation approaches and controller strengths. Afterwards some open source autopilot systems are introduced. Conclusion is made with a summary of the current autopilot market and a remark on the future development.

In this paper, the application of artificial neural network in ship course control systems is investigated. Two-multilayered feed-forward neural network course control system is proposed. The first neural network plays the role of ship forward dynamic approximator. The second one is the course controller. Both neural networks are trained in a quasi-online regime using training data acquired from system functional process to cope with changing ship dynamics. A cost function is used in control action calculation. The performance of the proposed system is evaluated in different conditions. The system stability is verified via simulation. The simulation results show that the course control system is able to keep the predefined direction in various sea conditions and the proposed approach serves the consideration on developing and applying in designing real ship autopilot systems.

The aim of this research is to enhance adaptive autopilots for the effective management of aircraft systems, control maintenance, and the rejection of external disturbances. To achieve this objective, we propose the design of an autopilot integrated with the extremum-seeking control (ESC) algorithm. Although autopilots proficiently manage the lateral and longitudinal modes of aircraft control, they lack filtering or adaptive capabilities, thereby exposing the system to significant external threats. To mitigate these risks, the ESC method is employed. This adaptive approach can operate in a disturbance rejection manner by adjusting parameters for unknown inputs and restoring the system to its original controlled response. ESC represents a versatile control method suitable for effective application in simulations or experimental models. Through the incorporation of this method, the pitch attitude hold autopilot, altitude hold autopilot, and yaw autopilot acquire advanced disturbance rejection capabilities with adaptive ESC features. The novelty of the proposed method lies in providing advanced disturbance rejection properties to conventional autopilots, thereby rendering them innovative and superior disturbance rejection controllers. The newly developed autopilots are capable of eliminating severe disturbances from the system response, including ramp, sinusoidal, and step disturbances. The integration of autopilots with ESC offers significant advantages, such as superior disturbance rejection properties for the aircraft unmanned aerial vehicle (UAV) system. The proposed method successfully eliminates severe disturbances, as demonstrated in simulation results, surpassing previous methods in effectiveness. Furthermore, the Autopilot-ESC method enhances aircraft operation even under disturbances, minimizing energy consumption and ensuring stability and control. This novel method reduces operator workload and ensures reliable and efficient autonomous flight capabilities. Additionally, the adaptability of the Autopilot-ESC to changing environmental conditions make it well-suited in aircraft UAVs. This upgraded version of autopilot surpasses other robust controllers, such as Linear Quadratic Gaussian (LQG) regulator and Model Predictive Control (MPC), as it can effectively address ramp, sinusoidal, and step disturbances, which LQG and MPC cannot handle.

Maneuverability, which is closely related to operational performance and safety, is one of the important hydrodynamic properties of an underwater vehicle (UV), and its accurate prediction is essential for preliminary design. The purpose of this study is to analyze the turning ability of a UV while rising or submerging; the computational fluid dynamics (CFD) method was used to numerically predict the six-DOF self-propelled maneuvers of submarine model BB2, including steady turning maneuvers and space spiral maneuvers. In this study, the overset mesh method was used to deal with multi-body motion, the body force method was used to describe the thrust distribution of the propeller at the model scale, and the numerical prediction also included the dynamic deflection of the control planes, where the command was issued by the autopilot. Then, this study used the published model test results of the tank to verify the effectiveness of the CFD prediction of steady turning maneuvers, and the prediction of space spiral maneuvers was carried out on this basis. The numerical results show that the turning motion has a great influence on the depth and pitch attitude of the submarine, and a “stern heavier” phenomenon occurs to a submarine after steering. The underwater turning of a submarine can not only reduce the speed to brake but also limit the dangerous depth. The conclusion is of certain reference significance for submarine emergency maneuvers.

Gain-scheduled autopilots have emerged as a dominant strategy to achieve adaptive control of coupled, non-linear engineering complexities, owing to an ability to adapt to changing operational conditions and uncertainties. This study focuses on utilizing bilinear interpolation of gain-scheduled autopilots, emphasizing enhanced system performance and robustness. Through a comprehensive investigation and comparative analysis using three disparate cases, advantages over conventional methods are revealed. Strengths and weaknesses of both simple and specialized variants (such as linear, and real-time gain-scheduling) are introduced. Three missile guidance case–studies utilize simulation time and miss distance figures of merit. Comparing the performance of bilinear interpolation and automatic instantiations to index–search, over comparable traveled distances, missile miss distances were improved 179% and 196% respectively with slightly improved computational burden.

Concurrent with mental processes that require rigorous computation and control, a series of automated decisions and actions govern our daily lives, providing efficient and adaptive responses to environmental demands. Using a cognitive flexibility task, we show that a set of brain regions collectively known as the default mode network plays a crucial role in such “autopilot” behavior, i.e., when rapidly selecting appropriate responses under predictable behavioral contexts. While applying learned rules, the default mode network shows both greater activity and connectivity. Furthermore, functional interactions between this network and hippocampal and parahippocampal areas as well as primary visual cortex correlate with the speed of accurate responses. These findings indicate a memory-based “autopilot role” for the default mode network, which may have important implications for our current understanding of healthy and adaptive brain processing.

Previous studies have shown that the perceived times of voluntary actions and their effects are perceived as shifted towards each other, so that the interval between action and outcome seems shortened. This has been referred to as 'intentional binding' (IB). However, the generality of this effect remains unclear. Here we demonstrate that Intentional Binding also occurs in complex control situations. Using an aircraft supervision task with different autopilot settings, our results first indicated a strong relation between measures of IB and different levels of system automation. Second, measures of IB were related to explicit agency judgement in this applied setting. We discuss the implications for the underlying mechanisms, and for sense of agency in automated environments.

A modified path-following control system using the vector field method for an underactuated autonomous surface ship model is proposed in the presence of static obstacles. With this integrated system, autonomous ships are capable of following the predefined path, while avoiding the obstacles automatically. It is different from the methods in most published papers, which usually study path-following and obstacle collision avoidance, separately. This paper considers the coupled path following and collision avoidance task as a whole. Meanwhile, the paper also shows the heading control design method in the presence of static obstacles. To obtain a strong stability property, a nonlinear autopilot is designed based on the manoeuvring tests of the free-running ship model. The equilibrium point of the controller is globally exponentially stable. For the guidance system, a novel vector field method was proposed, and the proof shows the coupled guidance and control system is uniform semi-global exponentially stable (USGES). To prevent the obstacles near the predefined path, the proposed guidance law is augmented by integrating the repelling field of obstacles so that it can control the ship travel toward the predefined path through the obstacles safely. The repelling field function is given considering the obstacle shape and collision risk using the velocity obstacle (VO) algorithm. The simulations and ship model test were performed to validate the integrated system of autonomous ships.