Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOptimization of the Linear Systems with Unknown Dynamics Using Intelligent Operations Research Techniques
تحقيق أمثليّة الأنظمة الخطية بحركيات غير معلومة باستخدام تقنيات بحوث العمليات الذكية
1086
0 40
0
(
0
)
Added by
Aِl-Baath University ورقة بحثية
Publication date
2016
fields
Mathematics
and research's language is
العربية
Created by
Shamra Editor
Ask ChatGPT about the research
نُقدم في هذه المقالة طريقة، لإيجاد متحكم تكيّفيّ أمثل بالشكل المباشر للأنظمة الخطية مستمرة الزمن، بدون معرفة المصفوفات الحركية للنظام. و تُوظف الطريقة المقترحة إحدى تقنيات بحوث العمميات الذكية، و هي تقنية البرمجة الديناميكية التكيفية لحل معادلة ريكاتي الجبرية بشكل تكراري، باستخدام معلومات مباشرة من الحالة و الدخل، و بدون الحاجة إلى معرفة مُسبقة لحركيات النظام. و يُمكن بالإضافة لذلك إجراء كل التكرارات باستخدام معلومات الحالة و الدخل ذاتها لمرات عديدة و على بعض الفترات الزمنية الثابتة. كما تم في هذه المقالة تطوير خوارزمية عملية مباشرة، و تم تطبيقها لتصميم متحكم أمثل بمحرك ديزل نفاث مع إعادة تدوير غاز العادم.
This paper presents a method for finding online adaptive optimal controllers for continuous-time linear systems without knowing the system dynamical matrices. The proposed method employs one of Intelligent Operations Research Techniques, this technique is the adaptive dynamic programming, to iteratively solve the algebraic Riccati equation using the online information of state and input, without requiring the a priori knowledge of the system dynamics. In addition, all iterations can be conducted by using repeatedly the same state and input information on some fixed time intervals. A practical online algorithm is developed in this paper, and is applied to the controller design for a turbocharged diesel engine with exhaust gas recirculation.
References used
Al-Tamimi, A., Lewis, F. L., & Abu-Khalaf, M. (2007). Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity control. Automatica, 43(3), 473–481
(Baird, L.C.III. (1994). Reinforcement learning in continuous time: advantage updating. In Proceedings of IEEE international conference on neural networks. (pp.2448–2453
Bhasin, S., Sharma, N., Patre, P., & Dixon, W. E. (2011). Asymptotic tracking by a reinforcement learning-based adaptive critic controller. Journal of Control Theory and Applications, 9(3), 400–409
rate research
Read More
In this paper we consider the properties of linear systems by means of
directed graphs and numerical structures. We also state efficient algorithms
for determining an approximate number of the non-zero terms within
determinants' expressions of the
ir matrices. The stated algorithms make use of
trees representing numerical structures which contains the indices of the nonzero
terms.
This paper yields interesting results used in practical engineering
applications which include linear systems with sparse matrices, for example:
networks, electronic circuits, earth velocities boxes (gearboxes), multi-works
systems ...etc.
Earthmoving is the process of moving and processing soil from one location to another to alter an existing land surface into a desired configuration. Highways, dams, and airports are typical examples of heavy earthmoving projects. Over the years, con
struction managers have devised ways to determine the quantities of material to be moved from one place to another. Various types of soil (soft earth, sand, hard clay, …, etc.) create different level of difficulty of the problem. Earthmoving problem has traditionally been solved using mass diagram method or variety of operational research techniques. However, existing models do not present realistic solution for the problem. Multiple soil types are usually found in cut sections and specific types of soil are required in fill sections. Some soil types in cut sections are not suitable to be used in fill sections and must be disposed of. In this paper a new mathematical programming model is developed to find-out the optimum allocation of earthmoving works. In developing the proposed model, different soil types are considered as well as variation of unit cost with earth quantities moved. Suggested borrow pits and/or disposal sites are introduced to minimize the overall earthmoving cost. The proposed model is entirely formulated using the programming capabilities of VB6 while LINDO is used to solve the formulated model to get the optimum solution. An example project is presented to show how the developed model can be implemented.
This research tackles autolanding a power-off fixed-wing Unmanned Aerial Vehicle (UAV) on a
level or uphill landing strip with limited dimensions. New approaches to path planning, guidance,
and control are proposed for the final approach and landin
g stages. These approaches address
mid-sized UAVs assuming that the aircraft has only standard control surfaces, i.e. the elevator,
the rudder, and the ailerons. This problem has not been covered by the existing flight control
literature.
In this research, we investigate a problem of controllable and observerable for linear Continuous-time systems.
We have founded controllable and observerable conditions for the linear continuous-time system. Moreover, we put out a new algorithm for
finding control vector of steps that can enable us to move the state vector from the initial stage x(0) into the final onex(tf) for finite time tf>0, the theoretical results is illustrated by an example. Finally,we put program to plot trajectory of state vector x(t) and observer vector y(t)
In this paper, it has
merged two techniques of the artificial intelligent, they are the
ants colony optimization algorithm and the genetic algorithm, to
The recurrent reinforcement learning trading system
optimization. The proposed trading system
is based on an ant
colony optimization algorithm and the genetic algorithm to
select an optimal group of technical indicators, and fundamental
indicators.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
