Fuzzy state grammar and fuzzy deep pushdown automaton

2016 ◽  
Vol 31 (1) ◽  
pp. 249-258 ◽  
Author(s):  
Nidhi Kalra ◽  
Ajay Kumar
Keyword(s):  
Pushdown Automaton ◽  
Fuzzy State ◽  
State Grammar

fuzzy state-feedback control design for nonlinear systems with -stability constraints: An LMI approach

2008 ◽  
Vol 78 (4) ◽  
pp. 514-531 ◽  
Author(s):  
Wudhichai Assawinchaichote ◽  
Sing Kiong Nguang ◽  
Peng Shi ◽  
El-Kébir Boukas
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
State Feedback ◽  
Control Design ◽  
State Feedback Control ◽  
Lmi Approach ◽  
Fuzzy State

Development of a fuzzy-state feedback regulator for stabilizing a flexible inverted pendulum system

2021 ◽  
pp. 107754632110429
Author(s):  
Pouriya Pourgholam ◽  
Hamid Moeenfard
Keyword(s):  
Fuzzy System ◽  
State Feedback ◽  
Equations Of Motion ◽  
Pole Placement ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Reduced Order Observer ◽  
Pole Placement Control ◽  
Fuzzy State

Accurate modeling and efficient control of inverted pendulums have always been a challenge for researchers. So, the current research aims to achieve the following objectives: (I) proposing a comprehensive dynamic model for the inverted pendulums which accounts for the flexibility of the pendulum bar and (II) suggesting an appropriate supervisory fuzzy-pole placement control strategy for stabilizing the pendulum system. Using a Lagrangian formulation, the equations of motion are derived and linearized. Then, a state feedback controller with a reduced-order observer is designed to stabilize the system. Closed-loop simulations reveal that at least six modes shall be considered in the dynamic equations. To improve the quality of the transient response, a novel fuzzy system is developed for real-time assignment of the controller poles. Simulation results demonstrate that the control quality is significantly improved by adding a supervisory fuzzy system to the control loop. The developed approach for dynamic modeling of the system, and the idea of multi-level fuzzy-pole placement control architecture developed in this paper, may be successfully applied to improve the response specifications in other dynamic systems.

HIGH-PRECISION IN-FLIGHT CALIBRATION USING UNKNOWN LANDMARKS

2021 ◽  
Vol 4 ◽  
pp. 117-124
Author(s):  
Alexander Tkachenko ◽  
Keyword(s):  
Good Accuracy ◽  
Star Tracker ◽  
Coordinate Frame ◽  
Geometric Calibration ◽  
The Earth ◽  
Imaging Camera ◽  
Calibration Accuracy ◽  
Combined Algorithm ◽  
Fuzzy State ◽  
Fuzzy State Observer

An in-flight geometric calibration (further — calibration) is interpreted here as a procedure of making more preceise mutual attitude parameters of the onboard imaging camera and the star tracker. The problem of calibration is solved with using of observations of the landmarks from the orbit. In this work, the landmarks are considered as unknown in the sense that they may be identified on the several snapshots, they may be associated with synchronous data of the star tracker and GPS, but their location in the Earth coordinate frame is unknown. While unknown markers are used, it is more complicated to provide high accuracy of calibration than when geo-referenced markers are observed. In such a situation, improvement of the onboard devices and gauges and increasing of their accuracy strenghtens advisability of agreement of attainable accuracy of calculations while in-flight geometric calibration with accessible measurings accuracy. It concerns properly calibration so as geo-referencing of space snaps using results of calibration. In particular, it is important to consider how accuracy of calibration depends on the accuracy of specific measurings and initial data. Actuality of the considered problem is indisputable. Without its solution, attraction of high-accurate measurings is senseless. A main means of investigation is computer simulanion and analysis of its results. The combined algorithm is proposed for the processing of the calibration measuring equations. It consists of two independent parts. The first one belongs to author of this work and is based on photogrammetric condition of collinearity The second part belongs to D.V. Lebedev and is based on photogrammetric condition of coplanarity. The method of state estimation with high convergence characteristics — fuzzy state observer — is used for resolving of measuring equations. The results of above-mentioned calibration are fully fit for the geo-referencing of the unknown ground objects with acceptable accuracy. Computer simulation had demonsrated good accuracy of the proposed method of the in-flight geometric calibration using unknown landmarks in a combination with high-precise characteristics of used technical means. The simulation had shown the calibration accuracy on the level of 5 arc sec and accuracy of the geo-referencing on the level of 10–20 m. It is fully comparable with accuracy when geo-referenced markers are observated.

Jazz/Rock/Pop - Das Dresdner Modell

2021 ◽  
Keyword(s):  
Grammar School ◽  
Work Experiences ◽  
Artistic Practice ◽  
Berlin Wall ◽  
Field Of Study ◽  
International Recognition ◽  
State Grammar

The jazz/rock/pop programme at the Dresden College of Music developed into a multifaceted educational complex during the GDR era, despite reservations by cultural politicians, and gained international recognition after the fall of the Berlin Wall. Contemporary witnesses, current teachers and graduates report in 25 essays on their work, experiences, individual views and the interaction between artistic practice and pedagogical activity. This richly illustrated volume provides unique insights into the structure and goals of this field of study in all its breadth, from the children's class and the cooperation with the Saxon State Grammar School for Music to the Bachelor's, Master's and graduate programmes.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

FUZZY DYNAMIC LOGIC

2006 ◽  
Vol 02 (01) ◽  
pp. 43-55 ◽  
Author(s):  
LEONID I. PERLOVSKY
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Logic ◽  
Learning System ◽  
Dynamic Adaptation ◽  
Fuzzy Models ◽  
Uncertain Knowledge ◽  
Model Improvement ◽  
The Mind ◽  
Fuzzy State ◽  
Theory Of Forms

Fuzzy logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as a joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an image recognition example of patterns below clutter. The paper discusses relationships to formal logic, fuzzy logic, complexity and draws tentative connections to Aristotelian theory of forms and working of the mind.

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