Optimal Guidance Research Articles

Cooperative optimal guidance of hypersonic glide vehicles by real-time optimization and deep learning

This paper investigates the cooperative optimal guidance of hypersonic glide vehicles (HGVs) in the terminal phase with consideration on time-varying velocity, aerodynamic forces, and practical constraints. The cooperative optimal guidance problem is formulated as an optimal control problem with time as the independent variable, which brings great convenience in controlling the impact time. We propose proper convexification techniques to convexify this problem and apply successive convex optimization to get the solution of the original problem. To achieve cooperative guidance of multiple HGVs with time coordination constraint, a deep learning–based approach is proposed to find an optimal common impact time assigned to all the HGVs. The training samples required by deep learning are obtained by convex optimization. An algorithm is then presented to summarize the cooperative optimal guidance strategy. In each guidance loop, the common impact time is updated according to mission conditions, and the guidance commands are generated by the successive solution procedure in real time. Numerical examples will be provided to demonstrate that the proposed cooperative optimal guidance algorithm is effective and efficient, and it can achieve better performance than a popular cooperative proportional navigation guidance law.

Active-set pseudospectral model predictive static programming for midcourse guidance

This paper focuses on the midcourse guidance law for the interceptor to engage the hypersonic gliding vehicles (HGVs). Firstly, an active-set pseudospectral model predictive static programming (APS-MPSP) algorithm is presented, which entails two major improvements: the application of pseudospectral discretion and handling of control constraint using active-set iteration strategy. Except inheriting the high efficiency of the MPSP, this new algorithm can address the control constraint directly. Secondly, the optimal predictive guidance design is established, which embeds the target prediction into the guidance framework by formulating a simultaneous nonlinear optimal problem. Employing the APS-MPSP algorithm to solve this optimal problem efficiently, the predictive guidance command that ensures interceptor reach an appropriate predicted interception point (PIP) can be obtained directly. This strategy has the merits of no need to precompute the PIP or time-to-go and high prediction accuracy. The reliability and efficiency of the proposed methods are verified by numerical examples and comparisons with already methods.

Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The existing techniques in satellite image classification have limitations of overfitting problems due to the convolutional neural network (CNN) model generating more features. This research proposes the optimal guidance-whale optimization algorithm (OG-WOA) technique to select the relevant features and reduce the overfitting problem. The optimal guidance technique increases the exploitation of the search technique by changing the position of the search agent related to the best fitness value. This increase in exploitation helps to select the relevant features and avoid overfitting problems. The input images are normalized and applied to AlexNet–ResNet50 model for feature extraction. The OG-WOA technique is applied in extracted features to select relevant features. Finally, the selected features are processed for classification using Bi-directional long short-term memory (Bi-LSTM). The proposed OG-WOA–Bi-LSTM technique has an accuracy of 97.12% on AID, 99.34% on UCM, and 96.73% on NWPU, SceneNet model has accuracy of 89.58% on AID, and 95.21 on the NWPU dataset.

Optimal reinforcement learning and probabilistic-risk-based path planning and following of autonomous vehicles with obstacle avoidance

In this paper, a novel algorithm is proposed for the motion planning and path following automated cars with the incorporation of a collision avoidance strategy. This approach is aligned with an optimal reinforcement learning (RL) coupled with a new risk assessment approach. For this purpose, a probabilistic function-based collision avoidance strategy is developed, and the proposed RL approach learns the probability distributions of the adjacent and leading vehicles. Subsequently, the nonlinear model predictive control (NMPC) algorithm approximates the optimal steering input and the required yaw moment to follow the safest and shortest path through the optimal RL-based probabilistic risk function framework. Additionally, it is attempted to maintain the travel speed for the ego vehicle stable such that the ride comfort is also offered for the vehicle occupants. For this purpose, the steering system dynamics are also incorporated to provide a thorough understanding of the vehicle dynamics characteristic. Different driving scenarios are employed in the present paper to evaluate the proposed algorithm’s effectiveness.

Computational Optimal Impact Angle Control Guidance Laws Weighted by Arbitrary Functions

This paper presents a computational impact angle control guidance law based on the energy cost weighted by arbitrary functions in order to shape the acceleration command as desired. The optimal guidance problem is established in the impact angle frame and is solved by the Gauss orthogonal collocation method. The proposed guidance law is formulated from the generalized optimal control framework, thus a new guidance law that allows achieving a specific guidance goal can be easily obtained in the way of devising a proper weighting function (smooth, piecewise, nonsmooth, or even discontinuous). This property provides more degrees of freedom in the guidance law design to accomplish a specified guidance objective. A hardware experiment is conducted to evaluate the real-time computational capacity of the proposed computational guidance law. Illustrative examples with several types of weighting functions, including smooth, piecewise, nonsmooth, and discontinuous functions, are provided to demonstrate the advantages and capacity of the proposed guidance law.

Problematika Implementasi Kurikulum Merdeka di SMP Negeri 5 Padang Panjang

This research was conducted with the aim of knowing and describing the problems of implementing the independent curriculum that occurred at SMPN 5 Padang Panjang. This research departs from the background of the many phenomena surrounding the implementation of the independent curriculum and various policies regarding the independent curriculum. The research question raised is how the problems of implementing the independent curriculum at SMPN 5 Padang Panjang are problematic. The research was conducted with a qualitative type of field research. Data was collected by interviewing school principals, curriculum representatives, and teacher councils as well as making observations at SMPN 5 Padang Panjang. The results of these interviews were analyzed and then presented in a descriptive form. The results of this analysis can answer the questions formulated in the research questions. The results of this analysis are in the form of problems in the implementation of the independent curriculum originating from the government in the form of less than optimal guidance and assistance in the implementation of the independent curriculum. In addition, there are also problems originating from teachers in the form of a lack of teacher ability to utilize independent teaching platforms and utilize technology to learn and understand independent curriculum.

An Intelligent Rebalance System for Tidal Phenomenon of Dockless Bicycle-Sharing

With the advantages of flexible parking locations and convenient cycling, Dockless Bicycle-sharing (DBS) has become increasingly popular worldwide. However, with the massive increase of DBSs and electric fences, DBS systems face several challenges: (1) the hardness of identifying the DBS tidal zones; (2) the difficulty of accurately evaluating and identifying overload fences; (3) the issues of rebalancing DBS in time. To deal with these challenges, we propose a Dockless Bicycle-sharing Dynamic Rebalance (DBSDR) system to dynamically provide the optimal bicycle guidance for the DBS network. The DBSDR system contains three modules: DBS tidal zone identification, evaluation framework of overload fences, and DBS dynamic guidance. For DBS tidal zone identification, tidal zone identification and location from each fence with bicycle flows are provided with the HDBSCAN clustering method. The evaluation framework, covering DBS flows and the parking demand density, is proposed to assess the characteristics of overload fences. Finally, a DBS dynamic guidance method is provided to balance DBS for the tidal phenomenon with guiding users to the optimal target fence. Extensive experiments conducted on real-world DBS datasets show the effectiveness and accuracy of rebalancing the tidal phenomenon in the DBS system.

Unsaturated Optimal Guidance Law for Impact Angle and Acceleration Constraints

Unsaturated Optimal Guidance Law for Impact Angle and Acceleration Constraints

Minimizing Look Angle Rate Guidance Law Considering Impact Angle and Field-of-View Limit

This study proposes a look angle rate minimizing optimal guidance law with impact angle control and look angle limit. The optimal control theory is adopted to solve the look angle rate minimization problem which has the state inequality on the look angle limit and constraint for impact angle control at the final time. The performance of the proposed guidance law is shown demonstrated by simulations for engagement scenarios of several look angle limits and impact angle constraints. The comparative study shows that the proposed guidance law outperforms on the energy and look angle managing ability.

Optimal guidance for hypersonic vehicle using analytical solutions and an intelligent reversal strategy

This paper presents a novel three-dimensional guidance law to quickly generate optimal commands in the gliding phase. Owing to the complicated trajectory constraints and highly uncertain disturbances, the novel gliding profile is developed for the longitudinal plane. Subsequently, this longitudinal profile is utilized to obtain analytical equations of motion for performance index and path constraints prediction. Moreover, stringent terminal constraints, which comprise the altitude, the flight path angle, and the position of transition from gliding phase to terminal diving phase, are integrated into the longitudinal profile. The proposed analytical expressions simplify the onboard generation of optimal gliding trajectory as it does not involve complicated nonlinear programming problems. In addition, to regulate the terminal velocity onboard and simultaneously eliminate the heading error, two bank-reversal opportunities are used in the lateral profile and determined using a reinforcement learning algorithm. In the proposed guidance law, the terminal deviations are first predicted in real-time using the analytical expressions; and then the three-dimensional trajectory is regenerated by addressing a simple optimization problem and planning bank-reversal positions. The computation of the guidance law is facilitated using analytical solutions and a reinforcement learning method; therefore, the frequency of replanning facilitates the robustness, accuracy, and optimality. We evaluated the effectiveness of our method using some representative scenarios.

Mapping of Orthopaedic Fractures for Optimal Surgical Guidance.

Orthopaedic fractures may be difficult to treat surgically if accurate information on the fracture propagation and its exit points are not known. Even with two-dimensional (2D) radiographic images, it is difficult to be completely certain of the exact location of the fracture site, the fracture propagation pattern and the exit points of the fracture. Three-dimensional (3D) computerised tomographic models are better in providing surgeons with the extent of bone fractures, but they may still not be sufficient to allow surgeons to plan open reduction and internal fixation (ORIF) surgery.Fracture patterns and fracture maps are developed to be visual tools in 2D and 3D. These tools can be developed using fractured bones either before or after fracture reduction. Aside from being beneficial to surgeons during pre-surgical planning, these maps aid bioengineers who design fracture fixation plates and implants for these fractures, as well as represent fracture classifications.Fracture maps can be either created ex silico or in silico. Ex silico models are created using 3D printed bone models, onto which fracture patterns are marked. In silico fracture models are created by tracing the fracture lines from a fractured bone to a healthy bone template on a computer. The points of interest in both of these representations are the path of fracture propagation on the bone's surface and exit zones, which eventually determine the surgeon's choice of plate and fracture reduction. Both ex silico and in silico fracture maps are used for pre-surgical planning by the surgeons where they can plan the best way to reduce the fracture as well as template various implants in a low-risk environment before performing the surgery.Recently, fracture maps have been further digitised into heat maps. These heat maps provide visual representations of critical regions of fractures propagating through the bone and identify the weaker zones in the bone structure. These heat maps can allow engineers to develop optimal surgical plates to fix an array of fracture patterns propagating through the bone. Correlation of fractured regions with the mechanisms of injury, age, gender, etc. may improve fracture predictability in the future and optimise the intervention, along with making sure that surgeons do not miss fractures of the bone that may otherwise be hidden from plain sight.

State-Following-Kernel-Based Online Reinforcement Learning Guidance Law Against Maneuvering Target

In this article, a state-following-kernel-based reinforcement learning method with an extended disturbance observer is proposed, whose application to a missile-target interception system is considered. First, the missile-target engagement is formulated as a vertical planar pursuit–evasion problem. The target maneuver is then estimated by an extended disturbance observer in real time, which leads to an infinite-horizon optimal regulation problem. Next, utilizing the local state approximation ability of state-following kernels, the critic neural network (NN) and actor NN for synchronous iteration are constructed to calculate the approximate optimal guidance policy. The states and NN weights are proven to be uniformly ultimately bounded using the Lyapunov method. Finally, numerical simulations against different types of nonstationary targets are effectively tested, and the results highlight the role of state-following kernels in the value function and policy approximation.

Inverse Optimal Zero Effort Miss Guidance Based on Disturbance Observer

To intercept a maneuvering target in a two-dimensional plane, the inverse optimal guidance law based on zero effort miss (ZEM) and disturbance observer (DO) is studied in this paper. Firstly, the relative kinematics equation is simplified to obtain the missile-target ZEM and its dynamics. In order to enhance the robustness of the inverse optimal guidance law, the integral of the ZEM is introduced as a new state to form an augmented system with the original system based on the idea of proportional integral (PI) control. Then, the target maneuver acceleration is assumed as the unknown external disturbance of the guidance augmented system, which is estimated by the DO. Based on the estimated value of DO and the backstepping method, the inverse optimal guidance law is designed to reduce the adverse effect of the disturbance on the guidance system. Finally, simulations are designed to verify the effectiveness of the inverse optimal guidance method based on DO.

Mad Men (2007–15), not “Rad Men”: Or, from Brothel Pickpocket to Transcendental Advertiser

Mad Men is often assumed to be “subversive” in the academic literature whereas this investigation interprets the astutely promoted series as questioning capitalism before it ratifies market relations. Alongside convulsive change under capitalism that Mad Men captures, class-striated market societies require narratives that posit class division as compatible with meritocracy. Mad Men delivers such legitimizing narratives through Don Draper's and Peggy Olson's realization of class promotions—whereas Roger Sterling, Jr. and Pete Campbell present the privileges of inherited wealth. Don's performance in advertising illustrates Mad Men's often divided view of capitalism. Don melts down during one pitch and reveals his primordial experiences of capitalism as conditioned by poverty, theft and prostitution. However, by the final episode, Don's Coke ad affirms the market as a vehicle toward transcendental community. While Mad Men interrogates capitalism, it is solidly neoliberal in its disregard for State activity (regulation, implications of elected office). The narratively privileged moment of the series’ extended closing montage doubles-down on capitalism as the flawed but optimal steering mechanism for human aspiration. With the conspicuous exception of Betty Draper Francis who never participated in corporate work, core characters realize wealth and fulfillment by mastering market relations.

Optimizing Cd2+ adsorption performance of KOH modified biochar adopting response surface methodology

Chemical modification can enlarge biochar yield and increase its Cd adsorption capacity, so as to reduce production cost of the biochar and add its value. To efficiently use banana waste for preparation of biochar, response surface methodology based on central composite design (RSM-CCD) was used to establish a quadratic model of Cd adsorption capacity and yield in the present study. Analysis of variance (ANOVA) was used to examine accuracy and fitness of the models. The efficacy of variables and their interaction was illustrated by 3D surface plots and contour maps. The desirability function was used for multi-objective optimization to obtain the maximum Cd adsorption capacity and the maximum biochar yield simultaneously. The optimal parameters were 1.33 mol L−1, 721 ℃, 76 min. Under the conditions, the biochar yield ratio and Cd adsorption capacity were 36.41% and 480.94 mg g−1, respectively. The experimental results were consistent with the predicted results, which further verified the accuracy and fitness of the model. The results could provide the optimal process conditions and guidance for design and manufacture of biochar in the future.

Optimal Cooperative Guidance Strategies for Aircraft Defense with Impact Angle Constraints

Given the limitations of escape maneuvers and decoy deployment of combat aircraft under missile attacks; active defense dramatically improves the survival chances by launching active defense missiles to intercept incoming missiles. Different from previous work, this paper implemented impact angle constraints on the defense missile to achieve a better defense effect. The low-cost active defense missile with limited maneuverability is considered to cooperate with the aircraft through three mechanisms, namely two-way cooperation without any predetermined strategy, one-way cooperation with the defense missile employing a linear guidance strategy, and one-way cooperation with independent evasion maneuver for the target. Three optimal cooperative guidance strategies with impact angle constraints were investigated. Finally, a nonlinear two-dimensional model for agents with first-order autopilot dynamics was simulated to verify the performance of the proposed strategies. The simulation results indicated that the cooperative mechanism directly affects the available range of impact angles, and the constraints of a big impact angle can be realized by introducing the nonlinear model parameters and considering the angle variation between the velocity vector and the initial line-of-sight. Furthermore, the two-way cooperative mechanism achieves the best performance and more flexible solutions to accommodate different vehicle maximum overload limits.

Evaluation Research on Diniyah Formal Early Implementation in Pesantren

Introduction: The government and Islamic boarding schools strategically improve the quality of the implementation of Formal Diniyah Education, known as Formal Diniyah Education (PDF), professionally and effectively. In this regard, Islamic boarding schools and the government must prepare policies on PDF development. However, PDF in Islamic boarding schools is not optimal, so it is strategic to be evaluated as material for improving the quality of PDF in Islamic boarding schools in the future. Method: This evaluation research uses a qualitative approach by taking cases from several PDFs in the pilot project of Indonesian Islamic boarding schools. Result: Among the research findings, it is stated that: The level of PDF quality is primarily determined by the optimal level and whether PDF implementation in Islamic boarding schools is exemplary. Among the findings: the recognition of PDF in Islamic boarding schools must be accompanied by professional monitoring of the quality context of the application of PDF in Islamic boarding schools, the PDF curriculum still overlaps with Ma'had Ali's curriculum, lack of learning management and lack of optimal professional guidance for PDF, PDF achievements of non-Muslims. Academic (psychomotor) in pesantren is not too much attention. Conclusion: The government and Islamic boarding schools need to pay attention to the effectiveness of the application of PDF in Islamic boarding schools so that the quality of PDF in Islamic boarding schools is always maintained, through several strategic efforts, namely: in collaboration with the Ministry of Religion, Balitbang need to recommend to Pusdiklat and Pusdiklat, and pesantren need to pay attention to the optimal performance of students PDF at the boarding school.

Multi-phase and dual aero/propulsive rocket landing guidance using successive convex programming

This paper aims to suggest a new landing guidance algorithm for reusable launch vehicles (RLVs) to enable generation of fuel-efficient trajectories based on successive convex programming. To this end, a dual aero/propulsive landing guidance problem is first formulated to fully exploit the additional moment generated by the aerodynamic control to reduce the propulsion demand required for attitude control. As the result of the aerodynamic landing phase could greatly affect the fuel-optimal trajectory during the vertical landing phase, the formulation is further extended to the multi-phase optimal guidance problem using state-triggered constraints. The proposed guidance strategy is then obtained by solving the formulated optimal control problem based on the successive convex optimization framework using an interior point method The main contribution of this study lies in forming new RLV landing guidance problems to get an optimal trajectory and transforming the corresponding nonconvex problem into a convex optimization problem by introducing appropriate combinations for convexification techniques. Additionally, this paper introduces several practical constraints, such as the maximum slew rate of aerodynamic control fins and nozzle angles, which are not considered in previous works. In this paper, the performance of the proposed method with the potential for online computation is investigated through numerical simulations.

Abstract 10034: Novel Late-Gadolinium Enhanced Cardiac Magnetic Resonance (LGE-CMR)-Based Computational Heart Paradigm Predicts Optimal Ventricular Tachycardia (VT) Ablation Targets in Arrhythmogenic Right Ventricular (RV) Cardiomyopathy (ARVC)

Introduction: ARVC is an inherited cardiac disease that increases risk of life-threatening VTs in young adults. Catheter ablation, a mainstay of VT treatment in ARVC, remains challenging due to difficulties in identifying optimal VT ablation targets. Hypothesis: Right Ventricular Cardiomyopathy Ablation Targeting (RVCAT) is a novel digital-heart paradigm that accurately predicts the optimal VT ablation targets of ARVC patients. Methods: We retrospectively enrolled 11 ARVC patients; 8 presented with clinical VTs and underwent VT ablation. In all patients, RV hyperenhancement on LGE-CMR was identified. For this study, we developed RVCAT, the first computational heart model integrating RV personalized diffuse and dense fibrosis as VT substrates. Each region was assigned ARVC-specific electrophysiologic properties. VT inducibility was assessed via rapid pacing in each model. If inducible, optimal ablation targets, which rendered the RV substrate non-inducible for each VT, were determined. Targets were compared to electroanatomic voltage maps (EAM), locations of clinical ablations, and clinical records. Results: Personalized model predictions correlated well with clinical observations. All 8 models of the patients with a history of clinical VT had inducible VTs (Fig A). Furthermore, model VT circuit locations matched the clinical description. Substrate distribution in models matched low-voltage zones in EAM. The predicted ablation targets coincided with clinical ablations (Fig B). Results showed that the volume of model ablation lesions was significantly smaller than that of clinical ablations (1.02±0.68 versus 1.45±0.91, p<0.05, Fig C). For the 3 patients who did not have VTs, their corresponding heart models correctly predicted that no VTs would be induced. Conclusions: RVCAT is a novel digital-heart technology for optimal ARVC VT ablation guidance that can be potentially integrated into clinical workflows for augmenting therapeutic precision.

Optimal 3-dimension trajectory-tracking guidance for reusable launch vehicle based on back-stepping adaptive dynamic programming

Optimal 3-dimension trajectory-tracking guidance for reusable launch vehicle based on back-stepping adaptive dynamic programming