Neural Dynamic Trajectory Design for Reentry Vehicles PDF Download
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Author: Ajay Verma
Publisher:
ISBN:
Category : Guided missiles
Languages : en
Pages : 14
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Book Description
The next generation of reentry vehicles is envisioned to have onboard autonomous capability of real-time trajectory planning to provide capability of responsive launch and delivering payload anywhere with precise flight termination. This capability is also desired to overcome, if possible, in-flight vehicle damage or control effector failure resulting in degraded vehicle performance. An aerial vehicle is modeled as a nonlinear multi-input-multi-output (MIMO) system. An ideal optimal trajectory control design system generates a series of control commands to achieve a desired trajectory under various disturbances and vehicle model uncertainties including aerodynamic perturbations caused by geometric damage to the vehicle. Conventional approaches suffer from the nonlinearity of the MIMO system, and the high-dimensionality of the system state space. In this paper, we apply a Neural Dynamic Optimization (NDO) based approach to overcome these difficulties. The core of an NDO model is a multilayer perceptron (MLP) neural network, which generates the control parameters online. The advantage of the NDO system is that it is very fast and gives the trajectory almost instantaneously. The bulk of the time consuming computation is required only during off-line training. The inputs of the MLP are the time-variant states of the MIMO systems. The outputs of the MLP are the near optimal control parameters.
Author: Ajay Verma
Publisher:
ISBN:
Category : Guided missiles
Languages : en
Pages : 14
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Book Description
The next generation of reentry vehicles is envisioned to have onboard autonomous capability of real-time trajectory planning to provide capability of responsive launch and delivering payload anywhere with precise flight termination. This capability is also desired to overcome, if possible, in-flight vehicle damage or control effector failure resulting in degraded vehicle performance. An aerial vehicle is modeled as a nonlinear multi-input-multi-output (MIMO) system. An ideal optimal trajectory control design system generates a series of control commands to achieve a desired trajectory under various disturbances and vehicle model uncertainties including aerodynamic perturbations caused by geometric damage to the vehicle. Conventional approaches suffer from the nonlinearity of the MIMO system, and the high-dimensionality of the system state space. In this paper, we apply a Neural Dynamic Optimization (NDO) based approach to overcome these difficulties. The core of an NDO model is a multilayer perceptron (MLP) neural network, which generates the control parameters online. The advantage of the NDO system is that it is very fast and gives the trajectory almost instantaneously. The bulk of the time consuming computation is required only during off-line training. The inputs of the MLP are the time-variant states of the MIMO systems. The outputs of the MLP are the near optimal control parameters.
Author: Javad Taghia
Publisher: Springer Nature
ISBN: 303042359X
Category : Technology & Engineering
Languages : en
Pages : 137
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Book Description
This book provides methodologies for designing and implementing guidance algorithms for autonomous vehicles. These algorithms make important decision regarding how to steer and drive a ground vehicle in order to safely stay on an intended path, thereby making the vehicle driverless. The design tools provided in this book enable the reader to develop highly practical and real-world implementable guidance algorithms that will deliver high-accuracy driving for field vehicles. (They are equally applicable for on-road vehicles.) The book covers a variety of vehicle types, including wheeled vehicles, tracked vehicles, wheeled and tracked vehicles towing trailers, and four-wheel-steer and four-wheel-drive vehicles. It also covers active trailers that are driven and steered. Vehicles used in agriculture, mining and road construction are subjected to unpredictable and significant disturbances. The robust control methodologies presented can successfully compensate for these disturbances, as confirmed by the experimental results presented. Though the majority of the methodologies presented are based on sliding-mode controllers, other robust control methodologies are also discussed. To help the reader decide which controller is best suited for his/her choice of vehicle, experimental results are presented in a comparative format.
Author: Axel J. Roenneke
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Sampo Kuutti
Publisher: Springer Nature
ISBN: 3031015029
Category : Technology & Engineering
Languages : en
Pages : 70
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Book Description
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
Author: Hubmann, Constantin
Publisher: KIT Scientific Publishing
ISBN: 3731510391
Category : Technology & Engineering
Languages : en
Pages : 178
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Book Description
This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.
Author: Axel J. Roenneke
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Sarah Nicole D'Souza
Publisher:
ISBN: 9781303538094
Category :
Languages : en
Pages :
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Book Description
In order to address a gap in modeling flight feasible trajectories for vehicle shape optimization, a novel guidance algorithm was developed for this application. The baseline geometry and trajectory profile for guidance testing was based on an Orion like vehicle flying a skip entry upon lunar return. The reference trajectory generator for this guidance algorithm numerically solves the three degree of freedom Rotating, Spherical Planet equations of motion using a predictor- corrector and analytical functions, that represent the reference bank profile, to drive the solution. The automation of transition event determination provides the guidance initialization loads and a framework for adaptability. The initialization loads include the guidance activation/termination condition, the entry bank angle, and the linear transition velocity. The targeting scheme for this guidance algorithm leverage's the optimal control equations from Energy State Approximation methods to find a single control point that is blended to the reference trajectory to guide out the range excursion, caused by various dispersions. The set of dispersion used in this work simulate the change in aerodynamics that occur in a vehicle optimization study. The preliminary targeting formulation exhibits the potential guide out aerodynamic dispersions, expanding the range of aerodynamic dispersions -43% as compared to -20% for the Apollo Derived Final Phase Guidance.
Author: Frank J. Regan
Publisher: AIAA
ISBN: 9781600860461
Category : Space vehicles
Languages : en
Pages : 616
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Book Description
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 940
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Book Description
Author: Nguyen Vinh
Publisher: Elsevier
ISBN: 0444601457
Category : Technology & Engineering
Languages : en
Pages : 421
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Book Description
Optimal Trajectories in Atmospheric Flight deals with the optimization of trajectories in atmospheric flight. The book begins with a simple treatment of functional optimization followed by a discussion of switching theory. It then presents the derivation of the general equations of motion along with the basic knowledge in aerodynamics and propulsion necessary for the analysis of atmospheric flight trajectories. It goes on to the study of optimal trajectories by providing the general properties of the optimal aerodynamic controls and the integrals of motion. This is followed by discussions of high subsonic and supersonic flight, and approximation techniques to reduce the order of the problem for a fast computation of the optimal trajectory. The final chapters present analyses of optimal reentry trajectories and orbital maneuvers. This book is intended as a reference text for scientists and engineers wanting to get into the subject of optimal trajectories in atmospheric flight. If used for teaching purposes, the book is written in a self-contained way so that a selective use of the material is at the discretion of the lecturer. The first 11 chapters are sufficient for a one-semester course with emphasis on optimal maneuvers of high performance aircraft.
