Maneuvering Scenarios Research Articles

Stabilizing Voltage and Managing Power Loss in Medium Voltage Distribution Systems Through Strategic Maneuvers

Repairs and maintenance on the electrical power system often require power outages. To ensure safety and security during maintenance, limiting the extent of these outages is essential. This study aims to analyze the impact of network maneuvers on voltage drop and power loss in feeders in Medan, Indonesia. The research involves simulating the feeder circuit under various maneuver scenarios using ETAP 19.0.1 software to analyze voltage drop and power loss in the 20 kV distribution network. The end voltage on the backup feeder decreased from 20.31 kV before maneuvering to 20.10 kV after maneuvering. Despite the maneuvers causing an increase in voltage drop from 0.724 kV (3.24%) to 1.080 kV (4.23%) and in active power losses from 152.59 kW (2.29%) to 226.92 kW (3.02%), both remained within the acceptable standard of 5%. To mitigate the voltage, drop, the study proposes uprating the conductor by replacing the existing AAAC conductor with an XLPE-insulated cable. This upgrade can increase the end voltage from 20.09 kV to 20.49 kV, thus improving the overall performance of the feeder during maintenance activities.

Harnessing traditional controllers for fast-track training of deep reinforcement learning control strategies

ABSTRACT In recent years, Autonomous Ships have become a focal point for research, specifically emphasizing improving ship autonomy. Machine Learning Controllers, especially those based on Reinforcement Learning, have seen significant progress. However, addressing the substantial computational demands and intricate reward structures required for their training remains critical. This paper introduces a novel approach, “Harnessing Traditional Controllers for Fast-Track Training of Deep Reinforcement Learning Control Strategies,” aimed at bridging conventional maritime control methods with cutting-edge DRL techniques for vessels. This innovative approach explores the synergies between stable traditional controllers and adaptive DRL methodologies, known for their complexity handling capabilities. To tackle the time-intensive nature of DRL training, we propose a solution: utilizing existing traditional controllers to expedite DRL training by cloning behavior from these controllers to guide DRL exploration. We rigorously assess the effectiveness of this approach through various ship maneuvering scenarios, including different trajectories and external disturbances like winds. The results unequivocally demonstrate accelerated DRL training while maintaining stringent safety standards. This approach has the potential to bridge the gap between traditional maritime practices and contemporary DRL advancements, facilitating the seamless integration of autonomous systems into naval operations, with promising implications for enhanced vessel efficiency, cost-effectiveness, and overall safety.

Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter

An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.

A Method to Track Moving Targets Using a Doppler Radar Based on Converted State Kalman Filtering

Strong nonlinearity between Doppler measurement and target motion in Doppler radar target tracking leads to the inadequate utilization of measurement information and limited tracking accuracy. We solved this problem by combining converted state Kalman filtering and the Interacting Multiple Model. This maneuvering target tracking method is suitable for Doppler measurement. First, we converted the target motion in the Cartesian coordinate to the polar coordinate. Then, we expanded the measurement equation to include Doppler measurement, making target motion linearly related to the Doppler radar observation vectors and allowing efficient utilization of measurement information. Next, we used unscented transformation to calculate the statistical characteristics of the process noise in the polar coordinate. This process helps to reduce the noise error caused by the coordinate system transformation in the original converted state Kalman filter. Finally, the system effectively tracks targets that may perform maneuvers with unknown motion during actual tracking. Using the converted state Kalman filter with Doppler measurement as a sub-filter, an Interacting Multiple Model tracking method can be constructed to adjust the model probabilities without going through nonlinear transformation. Simulation results show that the technique can achieve effective target tracking in Doppler measurement application scenarios and has higher tracking accuracy in non-maneuvering and maneuvering scenarios.

Total Energy-Based Control Design and Optimization for Lift-Plus-Cruise Aircraft

This paper presents a methodology for optimizing a Total Energy–based control system architecture for a lift-plus-cruise vertical takeoff and landing urban air mobility concept. The Total Energy Control System algorithm, which was originally developed for fixed-wing applications, is extended to also be applicable to hovering and transitioning flight. Control system parameters are optimized using a genetic algorithm optimization scheme, subject to constraints on dynamic stability and control response characteristics. Control system optimization and flight simulations are performed using the Modular Aircraft Dynamics and Control Algorithm Simulation Platform. Results presented include simulations of the optimized flight control system operation for maneuvering scenarios representative of hover, transition, and forward flight conditions as well as during transitions between vertical and forward flight modes. The presented results demonstrate the effectiveness of the proposed control system architecture and the demonstrated optimization methodology.

STIF: A Spatial–Temporal Integrated Framework for End-to-End Micro-UAV Trajectory Tracking and Prediction With 4-D MIMO Radar

The early trajectory prediction of micro unmanned aerial vehicles (micro-UAVs) with random behavior intentions facilitates the elimination of potential safety hazards. However, due to the property of a small radar cross-section (RCS), the backscattered radar signals from micro-UAVs may be submerged under strong background clutters, leading to distorted tracking and false prediction. To this end, this paper presents a spatial-temporal integrated framework (STIF) for end-to-end micro-UAV trajectory tracking and prediction based on a four-dimensional (4D) multiple-input-multiple-output (MIMO) radar. Especially, to obtain accurate trajectories in low signal-to-noise ratio (SNR) conditions, the target detection and tracking are considered to be interdependent and addressed jointly in this work, rather than treating them as two separate processes in conventional methods. The advantage is that with the assistance of tracking, all consecutive spatial information encoded in raw radar streams can be incorporated to enhance the continuous detection performance, avoiding information loss using only one single scan. Subsequently, to accommodate high maneuvering scenarios, an intention-aware end-to-end transformer-based prediction framework is presented to simultaneously discover both spatial and temporal dependencies hiding in long-term estimated trajectories. Consequently, a 4D frequency modulated continuous wave (FMCW) radar is utilized to evaluate the proposed system. Numerous simulation and experimental results indicate that STIF outperforms competing state-of-the-art methods and achieve superior prediction performance with the accuracy of 0.3851m in low SNR conditions.

Subtask-masked curriculum learning for reinforcement learning with application to UAV maneuver decision-making

Unmanned Aerial Vehicle (UAV) maneuver strategy learning remains a challenge when using Reinforcement Learning (RL) in this sparse reward task. In this paper, we propose Subtask-Masked curriculum learning for RL (SubMas-RL), an efficient RL paradigm that implements curriculum learning and knowledge transfer for UAV maneuver scenarios involving multiple missiles. First, this study introduces a novel concept known as subtask mask to create source tasks from a target task by masking partial subtasks. Then, a subtask-masked curriculum generation method is proposed to generate a sequenced curriculum by alternately conducting task generation and task sequencing. To establish efficient knowledge transfer and avoid negative transfer, this paper employs two transfer techniques, policy distillation and policy reuse, along with an explicit transfer condition that masks irrelevant knowledge. Experimental results demonstrate that our method achieves a 94.8% success rate in the UAV maneuver scenario, where the direct use of reinforcement learning always fails. The proposed RL framework SubMas-RL is expected to learn an effective policy in complex tasks with sparse rewards.

Investigation of hybrid power plant configurations for an offshore vessel with co-simulation approach

This work presents a full-system simulator consisting of a generic power system integrated with a vessel model and real-time capabilities. The whole system simulation facilitates evaluating the overall system performance by considering components’ interactions according to the maneuvering and environmental effects. Indeed, flexibility in the configuration and size of the power system enables the investigation of different concepts according to various maneuvering scenarios. Co-simulation approach is employed to integrate the models with various domains effectively. In addition, the bond graph modeling strategy as a power based method is used. The developed power system contains a diesel genset, a PEMFC, a battery with average electrical components, and a power management system. The configuration of the power system and size of power sources are modifiable. Various hybrid configurations and power capacities can be designed with validated power sources against marine vendors. In addition, the integrated offshore supply vessel with Dynamic Positioning (DP) and cruise controller and sea state forces induces the corresponding load demand of the operation to the power system. In summary, different operation scenarios with sea states, the thrusters’ states and allocation algorithm, DC link voltage, power electrical converters controller, and fuel consummations are captured in one model framework. To demonstrate the application of the model and emphasis the importance of total system simulation, three power system configurations are designed and simulated with two operational modes of DP and cruise with various sea states.

Complicated nonlinear oscillations caused by maneuvering of a flexible spacecraft equipped with hinged solar arrays

We present a general methodology for the nonlinear dynamical modeling of a three-axis stabilized spacecraft equipped with flexible solar arrays. The large-span multi-panel solar arrays are modeled as flexible thin plates that are connected to the rigid central body of the spacecraft by means of nonlinear flexible hinges. We construct a low-dimensional yet accurate dynamical model by using a Galerkin expansion in terms of global modes of the system that we compute first by using the Rayleigh–Ritz method. The hinged connections between the rigid and flexible parts of the system are imposed employing Lagrange multipliers. The model is used to study the spacecraft response triggered by various maneuvering scenarios. We in particular focus on the coupling between vibrations of the flexible components and the rigid motion of the spacecraft induced by hinge nonlinearities during these orbital and attitude maneuvering operations. In all cases considered, it is found that four global modes are sufficient to accurately compute the system’s response. We also observe other complicated nonlinear dynamical phenomena, such as hysteresis and superharmonic resonance that may be of concern in spacecraft design. Our modeling approach can be applied to other multibody systems directly.

Analytic solution for perturbed Keplerian motion under small acceleration using averaging theory

A novel approach is developed for analytic orbit propagation based on asymptotic expansion with respect to a small perturbative acceleration. The method improves upon existing first order asymptotic expansions by leveraging on linear systems and averaging theories. The solution starts with the linearization of Gauss planetary equations with respect to both the small perturbation and the six orbital elements. Then, an approximate solution is obtained in terms of secular and short period components. The method is tested on a low-thrust maneuver scenario consisting of a Keplerian orbit perturbed by a constant tangential acceleration, for which a solution can be obtained in terms of elliptic integrals. Results show that the positional propagation error is about one order of magnitude smaller with respect to state-of-the-art methods. The position accuracy for a LEO orbit, apart from pathological cases, is typically in the range of tens of meters for a tangential acceleration of 10−7 km/s2 after 5 orbital periods propagation.

Secure and collision-free multi-platoon control of automated vehicles under data falsification attacks

This paper addresses the secure and safe distributed cooperative control problem of multiple platoons of automated vehicles under unknown data falsification attacks on driving commands. First, a general multi-platoon control framework is developed, which accommodates longitudinal and lateral vehicle dynamics, inter- and intra-platoon information exchanges, falsified driving commands, and unknown external disturbances. To deal with the unknown falsified driving commands on the platoon performance, a neural-network-based adaptive control strategy is developed to compensate their adverse effects. In order to avert both longitudinal and lateral collisions under various maneuvering scenarios, a built-in avoidance mechanism is then designed for each platoon vehicle. Furthermore, a secure and anti-collision multi-platoon control design approach is proposed to ensure the desired inter- and intra-platoon tracking performance with a collision-free guarantee. It is formally proved that the inter- and intra-platoon tracking errors converge to small neighborhood around zero. Finally, several comparative simulation cases are presented to verify the effectiveness and merits of the proposed multi-platoon control approach.

Online Robust Model Predictive Control Based Emergency Maneuvering System for Autonomous Vehicles

Abstract Nonlinear robust model predictive control (RMPC) provides a promising solution to the problem of automatic emergency maneuvering and it is capable of handling multiple, possibly conflicting objectives of robustness and performance. Even though RMPC gives a sub-optimal solution, the critical challenge in its real-time implementation is the high computational demand. This paper presents a real-time capable, robust tube MPC-based framework for steering control during emergency obstacle avoidance maneuvers. The novelty of this framework lies in the robust integration of path planning and path-following tasks of autonomous vehicles. A simulation study showcases the robust performance improvements of the proposed strategy over a non-robust MPC in different extreme maneuvering scenarios.

Iteratively weighted dynamic modeling of four degrees of freedom motion for marine surface vehicles for k-step ahead prediction

Estimating surge, sway, roll, and yaw motions in marine vehicles are essential for many different reasons, such as maneuvering, dynamic positioning, control, and safety. In this article, an iterative-weighted model is presented for predicting marine vehicle's motions. Surge, sway, roll, and yaw speeds are used as input/output pairs, rudder and throttle references are also used as inputs. By using these input-output relations as training data, an iteratively weighted modeling algorithm is developed. The iteratively weighted model is then used for k-step ahead prediction for different maneuvering scenarios in calm waters. Estimation results are validated with varying scenarios of maneuvering to show the generalization of iteratively progressed model results. The presented work aims to bring a different perspective from a practical use viewpoint of a dynamic model of the platform without knowing the structure of the model, where only needed information are the excitation inputs and required outputs. Therefore, the presented approach has the flexibility to define input and output variables so that it can be configured for the desired application and has the potential to be extended on purpose, increasing the precision and reliability of surface/underwater vehicle's control as well as unmanned vehicles.

Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.

Tracking of Multiple Maneuvering Random Hypersurface Extended Objects Using High Resolution Sensors

With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.

Course-keeping assessment of yachts’ upwind sailing in waves

A method for the efficient prediction of the course-keeping capability of sailing yachts in upwind sailing is presented, based on an integrated modular aero- and hydrodynamic model. The transient aerodynamic forces on the sails are calculated via the lifting surface theory and in particular the vortex lattice method for unsteady flows. The varying character of these forces due to sail shape deformation is accounted for via a finite element scheme for thin shells that is coupled to the force calculation module. The sails’ solver is coupled further to a potential flow solver for yacht hydrodynamics in waves combining panel and strip theory approaches and accounting for the instantaneous waterline. The integrated model can predict the motions in 6 degrees of freedom. Several maneuvering scenarios of the yacht are investigated by using the new model and conclusions are drawn for the operability limits of a cruiser yacht sustaining bow-quartering regular waves and unidirectional wind, with respect to maintaining, in the mean, the desired upwind heading. More specifically, course stability diagrams are produced for two types of course-keeping scenarios: in the first, the yacht performs tacking maneuvers; while in the second, the yacht is tried to maintain a prescribed constant heading with minimum corrections. The paper includes a comprehensive review of the state-of-art in order to demonstrate the niche of the proposed approach.

A New Triple Filtering Algorithm and Its Application for Aerial GNSS/INS‐Integrated Direct Georeferencing System

A global navigation satellite system and inertial navigation system‐ (GNSS/INS‐) integrated system is employed to provide direct georeferencing (DG) in aerial photogrammetry. However, GNSS/INS suffers from stochastic error, strong nonlinearity, and weak observability problems in high dynamic or less maneuver scenarios. In this paper, we proposed a new triple filtering algorithm for aerial GNSS/INS integration. The new algorithm implements filtering in the sequence of forward, backward, and forward directions. Each filter is initialized by a previous filter to get a quick convergence, and the final result is combination of the last two filtering to smooth error. The proposed triple filtering strategy avoids inaccuracy in the 1st forward filtering when the system has not reached convergence. Moreover, it facilitates engineering implementation because backward filtering can employ the same equations with forward filtering. To assess stochastic error of the inertial measurement unit, the Allan variance method is used and abbreviated stochastic model is built. A real aerial testing is conducted, and the result indicates that DG can achieve horizontal accuracy of 5 cm by the proposed algorithm, which has 63% improvement compared to standard extended Kalman filter.

Overtaking maneuver scenario building for autonomous vehicles with PreScan software

Autonomous Vehicles (AVs) are vehicles able to imitate human driving, control skills, and the ability to perceive the environment around them while driving on the road. Therefore, one of the techniques that should be studied in detail is the overtaking maneuver, which is one of the most dangerous scenarios for AVs on the road, since overtaking generates variations in the acceleration and deceleration of vehicles and changes in the normal circulation of vehicles when they change over to another lane. The overtaking maneuver consists of overtaking another vehicle which is traveling in the same direction at a slower speed. The article proposes a simulation of an overtaking maneuver scenario with AVs through the use of simulation software denominated PreScan, which incorporates an Adaptive Cruise Control (ACC) and Technology Independent Sensors (TIS) in order to carry out the overtaking maneuver. The results showed that AVs are capable of performing overtaking maneuvers in an urban environment that involves conventional vehicles. Therefore, using software that simulates AVs overtaking maneuvers becomes a useful tool to interpret AVs performance with the obstacles that exist on the road.

A Learning Gaussian Process Approach for Maneuvering Target Tracking and Smoothing

Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This article proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions. Recursive Gaussian process, and derivative-based Gaussian process approaches for target tracking, and smoothing are developed, with online training, and parameter learning. The performance evaluation over two highly maneuvering scenarios, shows that the proposed approach provides 80 and 62% performance improvement in the position, and 49 and 22% in the velocity estimation, respectively, as compared to the best model-based filter.

Parallelized Multiscale Off-Road Vehicle Mobility Simulation Algorithm and Full-Scale Vehicle Validation

Abstract In this work, a scalable parallel computing scheme for the hierarchical multiscale off-road vehicle mobility simulation capability is developed with the hybrid message passing interface (MPI)/OpenMP framework, and it is validated against full-scale vehicle test data. While the hierarchical multiscale modeling approach has been introduced to high-fidelity off-road mobility simulations to eliminate limitations of existing single-scale deformable terrain models, computational complexities associated with the large dimensionality of multibody vehicle equations, involving nonlinear finite element tires and multiscale terrain models, need to be addressed for use in full-scale vehicle mobility predictions. To this end, a co-simulation framework for the multiscale off-road vehicle mobility model is proposed by exploiting the moving soil patch technique. This allows for systematically extracting four moving soil patches for four tires in a vehicle model from the deformable terrain domain, and time integrations of the four tire–soil subsystems are performed concurrently to enable computational speedup. Furthermore, an automated updating scheme for multiscale moving soil patches for a full vehicle model is developed, considering the multipass effect in various vehicle maneuvering scenarios. To demonstrate the off-road mobility prediction capability using the proposed parallelized multiscale vehicle–terrain interaction simulation algorithm, full-scale vehicle validation is presented for the vehicle drawbar pull as well as variable grade hill climb tests on soft soil.