Optimal Guidance Research Articles

Cooperative guidance laws for interception of active maneuvering target under information symmetric and asymmetric conditions

This paper proposes novel cooperative guidance strategies with dynamic game properties for a group of interceptors to intercept an active intelligent maneuvering target under both information symmetric and asymmetric conditions. A novel differential game guidance law is proposed to intercept the target with an active maneuvering strategy based on relative distances, relative velocities, and remaining flight time under information symmetric conditions. In the case where the team of interceptors has information superiority, a reasonable evading strategy is designed for the target, and a cooperative optimal guidance law is proposed for interceptors taking the target's evading strategy into consideration. When neither side has the knowledge of the opponent's maneuvering strategy, a novel robust adaptive interception guidance law is designed based on the reduction of the surrounding area regarding the target's maneuver as unknown disturbances. Numerical simulations are carried out to illustrate the effectiveness and superiority of proposed cooperative guidance laws.

The National Hurricane Center Tropical Cyclone Model Guidance Suite

Abstract The National Hurricane Center (NHC) uses a variety of guidance models for its operational tropical cyclone track, intensity, and wind structure forecasts, and as baselines for the evaluation of forecast skill. A set of the simpler models, collectively known as the NHC guidance suite, is maintained by NHC. The models comprising the guidance suite are briefly described and evaluated, with details provided for those that have not been documented previously. Decay-SHIFOR is a modified version of the Statistical Hurricane Intensity Forecast (SHIFOR) model that includes decay over land; this modification improves the SHIFOR forecasts through about 96 h. T-CLIPER, a climatology and persistence model that predicts track and intensity using a trajectory approach, has error characteristics similar to those of CLIPER and D-SHIFOR but can be run to any forecast length. The Trajectory and Beta model (TAB), another trajectory track model, applies a gridpoint spatial filter to smooth winds from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model. TAB model errors were 10%–15% lower than those of the Beta and Advection model (BAM), the model it replaced in 2017. Optimizing TAB’s vertical weights shows that the lower troposphere’s environmental flow provides a better match to observed tropical cyclone motion than does the upper troposphere’s, and that the optimal steering layer is shallower for higher-latitude and weaker tropical cyclones. The advantages and disadvantages of the D-SHIFOR, T-CLIPER, and TAB models relative to their earlier counterparts are discussed. Significance Statement This paper provides a comprehensive summary and evaluation of a set of simpler forecast models used as guidance for NHC’s operational tropical cyclone forecasts, and as baselines for the evaluation of forecast skill; these include newer techniques that extend forecasts to 7 days and beyond.

Sliding-Mode-Control–Based Instantaneously Optimal Guidance for Precision Soft Landing on Asteroid

Precision soft landing guidance problem of a spacecraft on asteroid is addressed in this paper. Most of the methods in existing literature require linearized engagement dynamics or accurate time-to-go estimate, which is not easy to obtain in practice. To overcome these drawbacks, a heading error-dependent sliding-mode-control–based guidance approach was recently introduced in literature. However, it too suffered from disadvantages in terms of optimality and constant gravity setup-based formulation. To obviate these limitations, and, moreover, to accommodate the effect of asteroid’s angular rotation on the engagement dynamics, a sliding-mode-control–based instantaneously optimal (SMC-IO) guidance is presented in this paper, under a variable gravity setup, in which a heading-error-based sliding variable and a predefined range-dependent maximum velocity envelope profile are leveraged. A static optimization problem with square of instantaneous guidance command as the performance index and the sliding variable dynamics, velocity profile, equations of engagement, and thrust limit as constraints is solved to obtain the guidance command at every time instant. Simulation studies are presented to demonstrate the effectiveness of the presented SMC-IO guidance. An extensive comparative simulation study suggests superior performance of the SMC-IO guidance over widely referred existing landing guidance in terms of overall control effort and fuel usage.

Energy management for hybrid electric vehicles based on imitation reinforcement learning

An effective energy management strategy (EMS) in hybrid electric vehicles (HEVs) is indispensable to promote consumption efficiency due to time-varying load conditions. Currently, learning based algorithms have been widely applied in energy controlling performance of HEVs. However, the enormous computation intensity, massive data training and rigid requirement of prediction of future operation state hinder their substantial exploitation. To mitigate these concerns, an imitation reinforcement learning-based algorithm with optimal guidance is proposed in this paper for energy control of hybrid vehicles to accelerate the solving process and meanwhile achieve preferable control performance. Firstly, offline global optimization is firstly conducted considering various driving conditions to search power allocation trajectories. Then, the battery depletion boundaries with respect to driving distance are introduced to generate a narrowed state space, in which the optimal trajectory is fused into the training process of reinforcement learning to guide the high-efficiency strategy production. The simulation validations reveal that the proposed method provides preferable energy reduction for HEVs in arbitrary driving scenarios, and suggests an efficient solution instruction for similar problems in mechanical and electrical systems with constraints and optimal information.

Electric vehicle charging guidance based on weighted complex network

The charging station malfunction may deny the charging service for the electric vehicles at the station. As a result, the vehicles need to select other stations. How to make an optimal selection is difficult for the owners. An optimal charging guidance strategy based on a weighted complex network is proposed for the owners to select the optimal station. All the charging stations are modelled as a complex network in which the stations and the roads among them are defined as nodes and edges, respectively. Furthermore, each edge is weighted by the state of charge (SOC) of the vehicle, the charging price, and the distance and traffic conditions between these two stations. The bigger edge weight indicates the smaller probability that the owner at one node of the edge select the other node of the edge for charging, and vice versa. Based on the weighted complex network model, the local load redistribution method is presented to guide the charging of vehicles at the malfunctioning station. Consequently, the optimal scheduling of the vehicles is realized under the guidance strategy proposed in this paper. Finally, some contrast experiments are carried out to illustrate the effectiveness and the superiority of the proposed method.

Manipulation and enhancement of asymmetric steering via down-converted nondegenerate photons

We investigate the asymmetric Gaussian steering with the nondegenerate parametric amplifier in a three-mode optomechanical system composed of two optical cavities and a mechanical oscillator. In the presence of the nondegenerate parametric amplifier, we find that the Gaussian steering between the auxiliary cavity and the mechanical resonator without direct interaction is significantly enhanced. By cooling the delocalized Bogoliubov modes over the auxiliary cavity and the mechanical oscillator, the optimal optomechanical entanglement and Gaussian steering can be realized and enhanced. Furthermore, we observe a wider range of parameters for the Gaussian steering with the case of cooling double delocalized modes. In addition, the magnitudes of the asymmetric Gaussian steering in two different directions can be adjusted by altering the decay rate of the auxiliary optical mode. Therefore, our proposal provides an effective method to manipulate and enhance the one-way Gaussian steering between the two modes.

AR assistance for efficient dynamic target search

When searching for a dynamic target in an unknown real world scene, search efficiency is greatly reduced if users lack information about the spatial structure of the scene. Most target search studies, especially in robotics, focus on determining either the shortest path when the target’s position is known, or a strategy to find the target as quickly as possible when the target’s position is unknown. However, the target’s position is often known intermittently in the real world, e.g., in the case of using surveillance cameras. Our goal is to help user find a dynamic target efficiently in the real world when the target’s position is intermittently known. In order to achieve this purpose, we have designed an AR guidance assistance system to provide optimal current directional guidance to users, based on searching a prediction graph. We assume that a certain number of depth cameras are fixed in a real scene to obtain dynamic target’s position. The system automatically analyzes all possible meetings between the user and the target, and generates optimal directional guidance to help the user catch up with the target. A user study was used to evaluate our method, and its results showed that compared to free search and a top-view method, our method significantly improves target search efficiency.

Optimal trajectory-tracking guidance for reusable launch vehicle based on adaptive dynamic programming

An optimal trajectory-tracking guidance method for Reusable Launch Vehicle (RLV) based on neural Adaptive Dynamic Programming (ADP) is proposed. Firstly, a reentry reference trajectory and the corresponding steady-state control are generated based on the Gauss Pseudo-spectral Method (GPM) with saturation constraints on the amplitude, rate and acceleration of flow angles. A single-critic ADP controller is designed for optimal feedback control, which is combined with the steady-state control to realize the trajectory-tracking guidance. An innovative weight iteration algorithm for the critic neural network is proposed to reduce training computation and improve guidance accuracy. Simulation results show that with initial state errors and system uncertainty, the terminal position error is within 2.1 km in a reentry flight whose range covers more than 5500 km. Involved with the innovative weight iteration method, the training computation is reduced by 95% compared with the traditional gradient descent method and the guidance performance is also improved.

Energy optimal guidance for proximity approach with obstacle avoidance

This paper presents an energy optimal guidance algorithm for the approach of a damaged spacecraft with the consideration of the arbitrary number of moving obstacles. The proposed optimal guidance laws are derived by projecting the control function in L2 Space to obtain the minimum norm controller. Unlike conventional trajectory optimization, this algorithm constructs an L2 space based on the state transfer matrix and describes the path constraints as inner products in L2 space. Following this, the proximity approach and obstacle avoidance/rendezvous are combined into a single but global optimal step by the Projection Theorem while obtaining the analytical energy consumption. Examples for multiple static and moving obstacles are presented to demonstrate the advantages of the proposed guidance over traditional trajectory optimization methods. The optimization result of the proposed method is slightly better than GPOPS and the computation time is about five-thousandths of GPOPS.

High-precision computational guidance in terminal phase with impact angle, lead angle and lateral acceleration constraints

This paper proposes a high-precision three-dimensional nonlinear optimal computational guidance law in the terminal phase of an interceptor that ensures near-zero miss-distance as well as the desired impact angle. Additionally, it achieves these ambitious objectives while ensuring that the lead angle and lateral acceleration constraints are not violated throughout its trajectory. This ensures (i) the target does not escape the field of view of its seeker at any point in time (a state constraint) and (ii) it does not demand unreasonable lateral acceleration that cannot be generated (a control constraint). The guidance problem is formulated and solved using newly proposed Path-constrained Model Predictive Static Programming (PC-MPSP) framework. All constraints, both equality and inequality, are equivalently represented as linear constraints in terms of the errors in the control history, thereby reducing the complexity and dimensionality of the problem significantly. Coupled with a quadratic cost function in control, the problem is then reduced to a standard quadratic optimization problem with linear constraints, which is then solved using the computationally efficient interior-point method. Results clearly demonstrate the advantage of the proposed guidance scheme over the conventional Biased PN as well as the recently proposed GENeralized EXplicit (GENEX) guidance techniques. Numerical simulations with variation in initial conditions and Monte–Carlo simulations with parametric uncertainty demonstrate the robustness of the proposed guidance scheme.

Collision Avoidance Method for Autonomous Ships Based on Modified Velocity Obstacle and Collision Risk Index

A novel real-time collision avoidance method for autonomous ships based on modified velocity obstacle (VO) algorithm and grey cloud model is proposed. A typical VO algorithm is used to judge whether there is a collision risk for ships in the potential collision area (PCA). Then, in order to quantify the collision risk of ships in different encounter situations within the PCA and trigger a prompt warning of danger of collision, this study sets up a novel collision risk assessment method based on asymmetric grey cloud model (AGC). It can effectively consider the randomness, ambiguity, and incompleteness of the information in the ship collision risk evaluation process. Moreover, reachable collision-free velocity sets under different encounter situations and optimal steering angle model are constructed. A real-time collision avoidance method based on modified VO algorithm and manoeuvring motion characteristics of vessels is put forward. In this model, various constraints are considered including the International Regulations for Preventing Collisions at Sea (COLREGs), ship manoeuvrability, and ordinary practice of seaman. Finally, several case studies are carried out to verify the performance and reliability of the collision avoidance model. The results show that the proposed method can not only effectively identify and quantify the collision risk in real-time but also offer proper collision-free solutions for autonomous ships.

Treatise on Analytic Nonlinear Optimal Guidance and Control Amplification of Strictly Analytic (Non-Numerical) Methods.

Optimal control is seen by researchers from a different perspective than that from which the industry practitioners see it. Either type of user can easily become confounded when deciding which manner of optimal control should be used for guidance and control of mechanics. Such optimization methods are useful for autonomous navigation, guidance, and control, but their performance is hampered by noisy multi-sensor technologies and poorly modeled system equations, and real-time on-board utilization is generally computationally burdensome. Some methods proposed here use noisy sensor data to learn the optimal guidance and control solutions in real-time (online), where non-iterative instantiations are preferred to reduce computational burdens. This study aimed to highlight the efficacy and limitations of several common methods for optimizing guidance and control while proposing a few more, where all methods are applied to the full, nonlinear, coupled equations of motion including cross-products of motion from the transport theorem. While the reviewed literature introduces quantitative studies that include parametric uncertainty in nonlinear terms, this article proposes accommodating such uncertainty with time-varying solutions to Hamiltonian systems of equations solved in real-time. Five disparate types of optimum guidance and control algorithms are presented and compared to a classical benchmark. Comparative analysis is based on tracking errors (both states and rates), fuel usage, and computational burden. Real-time optimization with singular switching plus nonlinear transport theorem decoupling is newly introduced and proves superior by matching open-loop solutions to the constrained optimization problem (in terms of state and rate errors and fuel usage), while robustness is validated in the utilization of mixed, noisy state and rate sensors and uniformly varying mass and mass moments of inertia. Compared to benchmark, state-of-the-art methods state tracking errors are reduced one-hundred ten percent. Rate tracking errors are reduced one-hundred thirteen percent. Control utilization (fuel) is reduced eighty-four percent, while computational burden is reduced ten percent, simultaneously, where the proposed methods have no control gains and no linearization.

Stable Locomotion of Humanoid Robots on Uneven Terrain Employing Enhanced DAYANI Arc Contour Intelligent Algorithm

Abstract Humanoid robots must be capable of walking on complicated terrains and tackling a variety of obstacles leading to their wide range of possible implementations. To that aim, in this article, the issue of humanoid robots walking on uneven terrain and tackling static and dynamic obstacles is examined. It is inspected by implementing a novel Enhanced DAYANI Arc Contour Intelligent (EDACI) Algorithm that designs trajectory by searching feasible points in the environment. It provides an optimum steering angle, and step optimization is performed by Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi-Newton method that leads to guide the humanoid robot stably to the target. The leg length policy has been presented, and a reward-based system has been implemented in the walking pattern generator that provides the optimum gait parameters. One humanoid robot act as a dynamic obstacle to others if they are navigating on a single terrain. It may generate a situation of deadlock, which needs to be solved. In this article, a dining philosopher controller (DPC) is employed to deal with and solve this issue. Simulations are used to evaluate the proposed approach in several uneven terrains having two humanoid NAOs. The findings indicate that it can precisely and efficiently produce optimal collision-free paths, demonstrating its efficacy. Experiments in similar terrain are performed that validate the results with a deviation under 6%. The energy efficiency of the developed controller is evaluated in reference to the inbuilt controller of NAO based on energy consumption. In order to check the feasibility and accuracy of the developed controller, a comparison with an established technique is provided.

Conventional Four-bar Linkage Steering System Adoption for Underslung Front Suspension

Recirculating ball (RCB) steering gear with four-bar linkage steering system is widely used in commercial vehicles with rigid front axle and over slung suspension owing to their architectural arrangement. The usage of RCB steering gear with four-bar linkage steering system with underslung suspension is not commercialized globally due to complexity of packaging suspension and steering linkages. Few automotive OEMs have studied and implemented Y link steering system in place of four-bar linkage steering system for underslung suspension arrangement. Y link steering system has got its inherent disadvantages such as higher bump steer, poor self-centering, complex linkages, more number of parts and less tire life. Further this arrangement is comparatively costlier than four-bar linkage steering system owing to their higher number of parts. In this paper, an extensive analysis has been made to implement the four-bar linkage steering system for underslung suspension vehicle. In this arrangement, the track rod (one of the links of a four-bar linkage system) is packaged above the leaf spring unlike in vehicles with overslung spring. Due to this unique arrangement, there exists some challenges in critical parameter optimization and packaging which are addressed in this paper. A mathematical model was developed to arrive at optimum steering and suspension geometry inorder to reduce the Ackerman error for better tire life and improve vehicle handling characteristics. By solving this mathematical model, optimum hardpoints can be arrived to achieve less Ackerman error, lower steering effort, lower bump steer, better steering returnability and best in class tire life. The present paper shows a possible approach to define the optimized steering and suspension linkages from the existing complex geometry with underslung suspension. This attempt is first of its kind in automotive industry and if commercialized successfully will go a long way in improving steering performance and cost savings for the vehicles with underslung suspension.

Optimal Steering of Matrix Product States and Quantum Many-Body Scars

Ongoing development of quantum simulators allows for a progressively finer degree of control of quantum many-body systems. This motivates the development of efficient approaches to facilitate the control of such systems and enable the preparation of non-trivial quantum states. Here we formulate an approach to control quantum systems based on matrix product states~(MPS). We compare counter-diabatic and leakage minimization approaches to the so-called local steering problem, that consists in finding the best value of the control parameters for generating a unitary evolution of the specific MPS state in a given direction. In order to benchmark the different approaches, we apply them to the generalization of the PXP model known to exhibit coherent quantum dynamics due to quantum many-body scars. We find that the leakage-based approach generally outperforms the counter-diabatic framework and use it to construct a Floquet model with quantum scars. We perform the first steps towards global trajectory optimization and demonstrate entanglement steering capabilities in the generalized PXP model. Finally we apply our leakage minimization approach to construct quantum scars in the periodically driven non-integrable Ising model.

Self-snapping hydrogel-based electroactive microchannels as nerve guidance conduits.

Peripheral nerve regeneration with large defects needs innovative design of nerve guidance conduits (NGCs) which possess anisotropic guidance, electrical induction and right mechanical properties in one. Herein, we present, for the first time, facile fabrication and efficient neural differentiation guidance of anisotropic, conductive, self-snapping, hydrogel-based NGCs. The hydrogels were fabricated via crosslinking of graphitic carbon nitride (g-C3N4) upon exposure with blue light, incorporated with graphene oxide (GO). Incorporation of GO and in situ reduction greatly enhanced surface charges, while decayed light penetration endowed the hydrogel with an intriguing self-snapping feature by the virtue of a crosslinking gradient. The hydrogels were in the optimal mechanical stiffness range for peripheral nerve regeneration and supported normal viability and proliferation of neural cells. The PC12 ​cells differentiated on the electroactive g-C3N4 H/rGO3 (3 ​mg/mL GO loading) hydrogel presented 47% longer neurite length than that of the pristine g-C3N4 H hydrogel. Furthermore, the NGC with aligned microchannels was successfully fabricated using sacrificial melt electrowriting (MEW) moulding, the anisotropic microchannels of the 10 ​μm width showed optimal neurite guidance. Such anisotropic, electroactive, self-snapping NGCs may possess great potential for repairing peripheral nerve injuries.

P08.03.A Conversation tool for brain tumor patients - tailor-made support and guidance for the patient and their proxies

Abstract Background An evaluation by the Dutch Neuro-Oncology Society (LWNO) showed that screening for psychosocial problems and the need for psychosocial care in glioma patients and their proxies is currently not optimal. Although tools to screen for psychosocial issues such as the Distress Thermometer exist, in daily practice they appear to be insufficient to discuss all disease-specific problems patients with brain tumors may encounter. We describe the development of a conversation tool to support the consultation between the patient with a brain tumor, their proxies and the health care professionals (HCPs). Material and Methods The development of the conversation tool for brain tumors was based on the tool used by the AYA Care Network in the Netherlands. Topics of importance for the entire care process were identified in a brainstorming session with 15 people (comprising HCPs, patients and proxies). Subsequently, the content of the conversation tool was determined by members of the LWNO and members of the LWNO-working group of nurses specialized in neuro-oncology (LWNO-v). Each topic in the conversation tool is supported by a visual, which was developed by a graphic design company in close collaboration with patients, proxies and HCPs. Results The conversation tool contains a total of 35 different topics covering six domains: physical health, daily life activities, psychological health, social relationships, loss of health and life, practical issues, all illustrated by a visual. The conversation tool can be given to the patient in booklet form before an appointment with the HCP, so they can prepare the conversation upfront. In addition, cards per domain will be available in the consultation room to be used during the appointment. Conclusion: By using the conversation tool, optimal individual guidance and support of patients with brain tumors is facilitated, as this patient population has unique issues that are often not covered by exiting tools. Using the conversation tool also promotes a nationally uniform way of working. Currently, an interactive tool for HCPs is being developed in which an overview of available interventions and best practices for the topics in the conversation tool are described, to ensure the needs of patients can be adequately addressed. The process of development of this tool can serve as an example for other cancer types.

Interagent Distance Control in a Cooperative Multiagent Target–Missile–Defender Engagement

A cooperative interception scenario between a maneuvering target, its defenders, and missiles attacking the said target is considered. The straightforward approach for optimal guidance laws synthesis for the scenario participants is to adopt objectives from the single agent target–missile–defender engagement analysis. This leads to a multiobjective optimization problem in which the cost function consists out of the squared miss distance and the control effort. In this article, we suggest to alter the aforementioned cost function in order to address the need to control the proximity level between the agents, a concept that is usually not accounted for in guidance law development. This approach utilizes the same quadratic framework, making it easy to add/include into traditional systems. Defensive (defenders collision avoidance) and offensive (missiles collision encouragement) cooperative distance control laws are developed for the end game phase using the same information used to implement the guidance law. Simulation results show that the desired distance control behavior can be achieved via the proposed method.

Barriers and facilitators to older adults’ engagement in healthy aging initiatives

Objectives. To identify the facilitators and barriers to older adults’ participation in healthy aging or cardiovascular rehabilitation programs. Methods. We conducted a scoping review to identify healthy aging program evaluations which identified participant barriers and facilitators. We developed a search strategy in the following databases: MEDLINE, Embase, APA PsycInfo, and Cochrane CENTRAL, all on the Ovid platform and Ebsco CINAHL.Results. We included 17 articles in this review. Our team categorized the barriers and facilitators of older adults’ participation in healthy aging programs into seven themes: attitudes, organizational structure, accessibility, social structure, knowledge, demographics, and program specifics.Conclusions. Understanding the facilitators and barriers that older adults face when deciding whether or not to participate or to continue participating in, healthy aging programs to promote in, cardiovascular health can help healthcare professionals provide optimal guidance for their patients and clients.

Optimal Look-Angle Guidance with Field-of-View and Impact Angle Constraints for Strapdown Munition

A strapdown seeker with limited field-of-view (FOV) can simplify and decrease cost of the guidance system for a munition. However, the inherent defect of the strapdown seeker is that it could not provide the necessary line-of-sight (LOS) angular information for typical guidance application. This paper proposed an optimal look-angle guidance law (OLAGL) which directly utilized seeker angle information and satisfies the constraints of FOV and terminal impact angle. The coefficient determination, effects of switching threshold, and gains for the guidance law are analyzed and detailed. The simulation results compared with typical trajectory shaping guidance for a ground target show that the presented guidance law can satisfy the impact angle constraint under the limitation of FOV and provide a well engineering reference.