Steering Scenarios Research Articles

Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling

In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.

Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning

Model predictive control (MPC) is a prominent control paradigm providing accurate state prediction and subsequent control actions for intricate dynamical systems with applications ranging from autonomous driving to star tracking. However, there is an apparent discrepancy between the model’s mathematical description and its behavior in real-world conditions, affecting its performance in real-time. In this work, we propose a novel neuromorphic (brain-inspired) spiking neural network for continuous adaptive non-linear MPC. Utilizing real-time learning, our design significantly reduces dynamic error and augments model accuracy, while simultaneously addressing unforeseen situations. We evaluated our framework using real-world scenarios in autonomous driving, implemented in a physics-driven simulation. We tested our design with various vehicles (from a Tesla Model 3 to an Ambulance) experiencing malfunctioning and swift steering scenarios. We demonstrate significant improvements in dynamic error rate compared with traditional MPC implementation with up to 89.15% median prediction error reduction with 5 spiking neurons and up to 96.08% with 5,000 neurons. Our results may pave the way for novel applications in real-time control and stimulate further studies in the adaptive control realm with spiking neural networks.

Simulation and Validation of an 8 × 8 Scaled Electric Combat Vehicle

In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios.

Quantum Steering with Imprecise Measurements.

We study quantum steering experiments without assuming that the trusted party can perfectly control their measurement device. Instead, we introduce a scenario in which these measurements are subject to small imprecision. We show that small measurement imprecision can have a large detrimental influence in terms of false positives for steering inequalities, and that this effect can become even more relevant for high-dimensional systems. We then introduce a method for taking generic measurement imprecision into account in tests of bipartite steering inequalities. The revised steering bounds returned by this method are analytical, easily computable, and are even optimal for well-known families of arbitrary-dimensional steering tests. Furthermore, it applies equally well to generalized quantum steering scenarios, where the shared quantum state does not need to be separable, but is instead limited by some other entanglement property.

Research on driving behavior characteristics of older drivers based on drivers’ behavior graphs analysis

Considerable evidence suggests that the decline in physiological abilities prevalent in older drivers leads to a reduction in the visual and psychomotor functions required for safe driving. The purpose of this study is to further investigate the differences in driving behavior between older and younger drivers and to describe the change process of driving behavior. In this study, 19 younger and older drivers each were recruited for a driving simulation experiment that included five scenarios. Driving operation data, eye movement data, and physiological data of drivers in five conflict scenarios were collected. The differences in driving behaviors between the two groups were also compared and analyzed, on which the thresholds of different driving behavior nodes were determined and driving behavior graphs were established. The results show that the eye movement nodes of older drivers appear later in five scenarios, the operational nodes of older people appear later in two steering scenarios, and are closer to those of younger drivers in three straight ahead scenarios, indicating that older drivers were later in observing and collecting traffic information, and later in applying brakes and steering to avoid conflicts when steering. The study provides a reference for the analysis of driving behavior and driving safety of older people.

Spatially resolved generation profiles for building, land and water-bound PV: a case study of four Dutch energy transition scenarios

Abstract. Alongside a transition from steerable and centralized traditional electricity generation to intermittent and more decentralized renewable electricity generation from solar panels and wind turbines, Dutch energy transition scenarios project a widespread deployment of heat pumps and electric vehicles towards 2050. While clearly contributing to the decarbonization of the Dutch energy system, these developments impose challenges regarding electricity supply-demand mismatch and grid congestion. Spatially resolved electricity demand and supply profiles are required to gain a better insight into where and when such problems are likely to occur within the different scenarios. The present paper focuses on Dutch solar energy supply and features the construction of geodatabases of scenario-specific, spatially resolved electricity generation profiles for building, land and water-bound PV. Country-level PV capacities are geographically distributed based on spatial variance in roof PV potential and availability of suitable land and water use areas. Corresponding electricity generation profiles are constructed using historical meteorological measurements, a diffuse fraction model and a anisotropic transposition model. Empirically found performance ratio profiles are applied to account for a multitude of performance loss factors, including shading, dust and inverter efficiency. In 2050, building-bound capacity is projected to show only limited overlap with both land-bound and water-bound PV capacity. On the other hand, regions with considerable water-bound PV capacity also tend to show considerable land-bound PV capacity. Compared to the present-day situation, yearly country-level PV electricity generation is projected to be a factor 18.5, 15.7, or 7.7 higher in 2050 when respectively following the Regional, National or International Steering scenarios.

Complete Hierarchy for High-Dimensional Steering Certification.

High-dimensional quantum steering can be seen as a test for the dimensionality of entanglement, where the devices at one side are not characterized. As such, it is an important component in quantum informational protocols that make use of high-dimensional entanglement. Although it has been recently observed experimentally, the phenomenon of high-dimensional steering is lacking a general certification procedure. We provide necessary and sufficient conditions to certify the entanglement dimension in a steering scenario. These conditions are stated in terms of a hierarchy of semidefinite programs, which can also be used to quantify the phenomenon using the steering dimension robustness. To demonstrate the practical viability of our method, we characterize the dimensionality of entanglement in steering scenarios prepared with maximally entangled states measured in mutually unbiased bases. Our methods give significantly stronger bounds on the noise robustness necessary to experimentally certify high-dimensional entanglement.

Certifying quantumness beyond steering and nonlocality and its implications on quantum information processing

Superunsteerability is a particular kind of spatial quantum correlation that can be observed in a steering scenario in the presence of limited shared randomness. In this work, we define an experimentally measurable quantity in a steering scenario to certify superunsteerability. In the context of certification of randomness with this scenario, we demonstrate that such certification of superunsteerability provides a bound on the amount of genuine randomness generation. On the other hand, superlocality is another kind of spatial quantum correlation that can be observed in a Bell scenario in the presence of limited shared randomness. We identify inequalities to certify superlocality in the Bell scenarios that can be adopted to implement $2$-to-$1$ and $3$-to-$1$ random-access codes. We observe that such certification of superlocality acts as resource for the random-access codes in the presence of limited shared randomness. As a by-product of our certification of superunsteerability and superlocality, we identify a new classification of separable states having quantumness.

Self-Testing of any Pure Entangled State with the Minimal Number of Measurements and Optimal Randomness Certification in a One-Sided Device-Independent Scenario

Certification of quantum systems and their properties has become a field of intensive studies. Here, taking advantage of the one-sided device-independent scenario (known also as quantum steering scenario), we propose a self-testing scheme for all bipartite entangled states using a single family of steering inequalities with the minimal number of two measurements per party. Building on this scheme we then show how to certify all rank-one extremal measurements, including non-projective $d^2$-outcome measurements, which in turn can be used for certification of the maximal amount of randomness from every entangled bipartite state of local dimension $d$, that is, $2\log_2d$ bits. Finally, in a particular case of $d=3$, we extend our self-testing results to the fully device-independent setting.

Certified random-number generation from quantum steering

The ultimate random number generators are those certified to be unpredictable -- including to an adversary. The use of simple quantum processes promises to provide numbers that no physical observer could predict but, in practice, unwanted noise and imperfect devices can compromise fundamental randomness and protocol security. Certified randomness protocols have been developed which remove the need for trust in devices by taking advantage of nonlocality. Here, we use a photonic platform to implement our protocol, which operates in the quantum steering scenario where one can certify randomness in a one-sided device independent framework. We demonstrate an approach for a steering-based generator of public or private randomness, and the first generation of certified random bits, with the detection loophole closed, in the steering scenario.

Spatially resolved generation profiles for onshore and offshore wind turbines: A case study of four Dutch energy transition scenarios

In line with the Dutch Climate Agreement, multiple energy transition scenarios have been constructed for 2030 and 2050. To various extents, they project a shift towards decentralized and intermittent renewable electricity generation (wind and solar) and widespread deployment of electric vehicles and heat pumps. These developments impose challenges regarding electricity supply-demand mismatch and grid congestion. In order to gain an understanding of when and where such problems are likely to occur, temporally and spatially resolved interpretations of the energy transition scenarios are required. This paper focuses on Dutch wind energy supply and shows construction of geodatabases of scenario-specific, hourly onshore and offshore wind electricity generation profiles on an individual turbine level. For the geographical distribution of turbine capacity, datasets on historically operational turbines, planned wind parks and suggested future turbine distributions are utilized. Turbine electricity generation profiles are constructed using a high resolution 3D meteorological dataset and power curves of commercially available turbine models. They are corrected for air pressure deviations and a multitude of loss factors, including wake effects. Compared to the present-day situation, yearly country-level electricity generation is projected to be a factor 16.6, 24.6 or 12.8 higher in 2050 when following the Regional, National or International Steering scenarios, respectively. In comparison to both the present-day and 2030 situation, onshore electricity generation is projected to be more evenly spread over different parts of the country in 2050. All offshore wind exploration areas considered in this research are projected to be completely utilized by 2050.

The Algorithm of Determining an Anti-Collision Manoeuvre Trajectory Based on the Interpolation of Ship's State Vector.

The determination of a ship’s safe trajectory in collision situations at sea is one of the basic functions in autonomous navigation of ships. While planning a collision avoiding manoeuvre in open waters, the navigator has to take into account the ships manoeuvrability and hydrometeorological conditions. To this end, the ship’s state vector is predicted—position coordinates, speed, heading, and other movement parameters—at fixed time intervals for different steering scenarios. One possible way to solve this problem is a method using the interpolation of the ship’s state vector based on the data from measurements conducted during the sea trials of the ship. This article presents the interpolating function within any convex quadrilateral with the nodes being its vertices. The proposed function interpolates the parameters of the ship’s state vector for the specified point of a plane, where the values in the interpolation nodes are data obtained from measurements performed during a series of turning circle tests, conducted for different starting conditions and various rudder settings. The proposed method of interpolation was used in the process of determining the anti-collision manoeuvre trajectory. The mechanism is based on the principles of a modified Dijkstra algorithm, in which the graph takes the form of a regular network of points. The transition between the graph vertices depends on the safe passing level of other objects and the degree of departure from the planned route. The determined shortest path between the starting vertex and the target vertex is the optimal solution for the discrete space of solutions. The algorithm for determining the trajectory of the anti-collision manoeuvre was implemented in autonomous sea-going vessel technology. This article presents the results of laboratory tests and tests conducted under quasi-real conditions using physical ship models. The experiments confirmed the effective operation of the developed algorithm of the determination of the anti-collision manoeuvre trajectory in the technological framework of autonomous ship navigation.

Broadband and Wide-Angle Scanning Capability in Low-Coupled mm-Wave Phased-Arrays Incorporating ILA With HIS Fabricated on FR-4 PCB

This paper presents a compact millimeter-wave (mm-Wave) phased array incorporating inverted-L antenna (ILA) with high-impedance electromagnetic surfaces (HIS) based on single-layer FR-4 PCB for user equipment (UE). In order to realize mm-Wave UE in low production cost, the design methodology of phased array antenna in single-layer FR-4 PCB is proposed without any additional packaging process. The presented antenna consists of low-coupled antenna element and low loss feeding networks in compressed linear array configuration with 0.39λ0 inter-element spacing. First, in order to realize broadband characteristics, the ILA is integrated with one-dimensional electromagnetic bandgap (1-D EBG) structures featuring HIS via slow-wave behavior. Due to high-impedance properties of the embedded via wall structures in common ground plane between each antenna element, the mutual coupling path through surface-current can be reduced. Second, in order to minimize leakage power caused by surface-waves, low loss coplanar waveguide ground (CPWG) feeding networks with via wall structures and island-shape pads are realized. The fabricated 4-elements and 8-elements phased array antennas including antenna element and feeding network based on FR-4 PCB achieve wide 3 dB beam coverage while featuring low beam squint in the Ka-band. In order to investigate over-the-air (OTA) system performance at three beam steering scenarios (boresight and ±60° scan angle), the measured maximum error vector magnitude (EVM) in QPSK modulation scheme is 16.6%. The coverage efficiency beyond 50% for the realized gain of −1.5 dBi from 26 to 36 GHz is demonstrated.

LPV/LFT Control Design Equipped with a Command Governor for Different Steering Scenarios

Abstract This paper presents a Linear Parameter Varying (LPV) based approach to handle different steering scenarios for autonomous vehicles. The proposed methodology consists of the design of a fast and performant LPV/LFT controller which can track the generated yaw rate reference, for speed operating range, for lane tracking and smooth/fast lane changes. The handling of a dynamic maneuver is achieved by a Command Governor (CG), which feeds the updated LPV/LFT closed loop system with a “virtual” yaw rate reference. This virtual signal is the solution of an online receding horizon optimization problem, meeting simultaneously safety bounds for the steering of the vehicle. The efficiency of the suggested methodology is illustrated by simulation results for parameters of an automated Renault Zoe.

Dual-Polarized High-Isolation Antenna Design and Beam Steering Array Enabling Full-Duplex Communications for Operation Over a Wide Frequency Range

A new dual-polarized antenna and array are presented for In-Band Full-Duplex (IBFD) applications, offering high inter-port isolation. The single-element antenna system consists of four H-shape slots, stacked patches for wide bandwidth, and two external hybrid couplers. The multilayer antenna is well matched from about 2.1 to 2.4 GHz and provides high isolation in this frequency range in excess of 60 dB. Two different prototypes are simulated and measured to highlight the design process. In addition, a new and compact hybrid coupler with excellent phase and magnitude stability is also presented for improved antenna radiation and IBFD performances. When compared to other similar types of hybrid coupler and antenna systems, the proposed configurations are simple to manufacture, provide a higher isolation bandwidth (10%), and higher gain of 7.3 dBi with cross-polarization levels of 30 dB or lower. The single-element design was also extended to a 2×2 array and studied for different beam steering and feeding scenarios. The operating bandwidths and isolation values offered by these S-band antenna and array systems can support new data link possibilities for beam steering and future low-cost IBFD wireless networks by simple antenna fabrication.

Bipartite Postquantum Steering in Generalized Scenarios.

The study of stronger-than-quantum effects is a fruitful line of research that provides valuable insight into quantum theory. Unfortunately, traditional bipartite steering scenarios can always be explained by quantum theory. Here, we show that, by relaxing this traditional setup, bipartite steering incompatible with quantum theory is possible. The two scenarios we describe, which still feature Alice remotely steering Bob's system, are (i)one where Bob also has an input and operates on his subsystem, and (ii)the "instrumental steering" scenario. We show that such bipartite postquantum steering is a genuinely new type of postquantum nonlocality, which does not follow from postquantum Bell nonlocality. In addition, we present a method to bound quantum violations of steering inequalities in these scenarios.

Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials.

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

Fault-Tolerant Stability Control for Independent Four-Wheel Drive Electric Vehicle Under Actuator Fault Conditions

Various torque distribution and stability control algorithms have been studied along with the development of four-wheel drive vehicles. But, most of those algorithms focus only on performance improvement. Since the independent four-wheel drive system is based on four drive motors, faults can occur in a motor or an inverter. If a fault occurs, the vehicle stability is not guaranteed and a fatal accident can occur. In this study, the fault-tolerant stability control algorithm for remaining healthy motors is proposed. This fault-tolerant control (FTC) system contains torque distribution and stability control algorithms. For accurate control of motor allocation, driving conditions such as under/over-steering, and steering direction should be considered, as well as vehicle condition such as fault motor location and motor performance limits. The proposed FTC is evaluated with two fault conditions (e.g., front motor fault, rear motor fault) under straight acceleration and constant steering acceleration scenarios. The behavior characteristics and driving risks caused by failures of motors while driving are analyzed. Also, the FTC algorithm is accurately follow a desired path within a minimum error range that the driver can cover.

Contingent Nonlinear Model Predictive Control for Collision Imminent Steering in Uncertain Environments

A novel uncertainty based contingent model predictive control algorithm is presented for autonomous vehicles operating in uncertain environments. Nominal model predictive control relies on a model to predict future states over a horizon and hence requires accurate models and parameterization. In application, environmental conditions and parameters may be unknown or varying, posing robustness issues for model predictive control. This work presents a new selectively robust adaptive model predictive control algorithm that is applied to collision imminent steering controllers for automotive safety. In this context, uncertainties in the road coefficient of friction are estimated using unscented Kalman filtering and the controller is updated based upon the estimated uncertainties. The utility of the uncertainty based controller is demonstrated in a collision imminent steering scenario and compared to nominal deterministic model predictive control, as well as a baseline adaptive scheme. The results suggest the uncertainty based controller can improve the robustness of model predictive control by nearly 50% for deterministic model predictive control and over 10% for the baseline adaptive scheme.

Slow Beam Steering and NOMA for Indoor Multi-User Visible Light Communications

Visible light communication (VLC) is an emerging technology that enables broadband data rates using the visible spectrum. In this paper, considering slow beam steering where VLC beam directions are assumed to be fixed during a transmission frame, we find the steering angles that simultaneously serve multiple users within the frame duration and maximize the data rates. This is achieved by solving a non-convex optimization problem using a grid-based search and majorization-minimization (MM) procedure. Subsequently, we consider multiple steerable beams with a larger number of users in the network and propose an algorithm to cluster users and serve each cluster with a separate beam. We optimize the transmit power of each beam to maximize the data rates. Finally, we propose a non-orthogonal multiple access (NOMA) scheme for the beam steering and user clustering scenario, to further increase the data rates of the users. The simulation results show that the proposed beam steering method can efficiently serve a high number of users, and with power optimization, a sum rate gain up to thirteen times is possible. The simulation results for NOMA suggests an additional 10 Mbps sum rate gain for each NOMA user pair.