Design of Self-Organized Robust Wise Control Systems Based on Quantum Fuzzy Inference

2006 ◽  
Author(s):  
Liudmila V. Litvintseva ◽  
Sergey V. Ulyanov
Keyword(s):  
Control Systems ◽  
Fuzzy Inference ◽  
Self Organized

scholarly journals Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2642
Author(s):  
Godwin Asaamoning ◽  
Paulo Mendes ◽  
Denis Rosário ◽  
Eduardo Cerqueira
Keyword(s):  
Control Systems ◽  
Networked Control Systems ◽  
Cooperative Behavior ◽  
Networked Control ◽  
Multi Agent Systems ◽  
Wireless Communication Network ◽  
Self Organized ◽  
Design Choice ◽  
Multi Agent ◽  
Computational Systems

The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

scholarly journals Brain-like functor control machine for general humanoid biodynamics

2005 ◽  
Vol 2005 (11) ◽  
pp. 1759-1779 ◽  
Author(s):  
Vladimir Ivancevic ◽  
Nicholas Beagley
Keyword(s):  
Feedback Control ◽  
Control Systems ◽  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Granule Cells ◽  
Nonlinear Feedback ◽  
Spinal Level ◽  
Cortical Level ◽  
Self Organized ◽  
Neuro Fuzzy

A novel, brain-like, hierarchical (affine-neuro-fuzzy-topological) control for biomechanically realistic humanoid-robot biodynamics (HB), formulated previously in [15, 16], is proposed in the form of a tensor-invariant, “meta-cybernetic” functor machine. It represents a physiologically inspired, three-level, nonlinear feedback controller of muscular-like joint actuators. On the spinal level, nominal joint-trajectory tracking is formulated as an affine Hamiltonian control system, resembling the spinal (autogenetic-reflex) “motor servo.” On the cerebellar level, a feedback-control map is proposed in the form of self-organized, oscillatory, neurodynamical system, resembling the associative interaction of excitatory granule cells and inhibitory Purkinje cells. On the cortical level, a topological “hyper-joystick” command space is formulated with a fuzzy-logic feedback-control map defined on it, resembling the regulation of locomotor conditioned reflexes. Finally, both the cerebellar and the cortical control systems are extended to provide translational force control for moving6-degree-of-freedom chains of inverse kinematics.

A New Fuzzy Inference Method for Symbolic Stability Analysis of Multigranular Intelligent Control System

2000 ◽  
Vol 12 (6) ◽  
pp. 664-674
Author(s):  
Hidehiro Yamamoto ◽  
◽  
Takeshi Furuhashi
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Inference ◽  
Continuous System ◽  
Asymptotically Stable ◽  
Membership Functions ◽  
Symbolic System ◽  
Inference Method

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

Fuzzy inference and its applicability to control systems

1992 ◽  
Vol 48 (1) ◽  
pp. 99-111 ◽  
Author(s):  
Abraham Kandel ◽  
Lihong Li ◽  
Zhiqiang Cao
Keyword(s):  
Control Systems ◽  
Fuzzy Inference

scholarly journals A Fuzzy Inference System for Management Control Tools

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2145
Author(s):  
Carolina Nicolas ◽  
Javiera Müller ◽  
Francisco-Javier Arroyo-Cañada
Keyword(s):  
Decision Making ◽  
Organizational Performance ◽  
Control Systems ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Small And Medium Enterprises ◽  
Belief Systems ◽  
Management Control ◽  
Inference System ◽  
Medium Enterprises

Despite the importance of the role of small and medium enterprises (SMEs) in developing and growing economies, little is known regarding the use of management control tools in them. In management control in SMEs, a holistic system needs to be modeled to enable a careful study of how each lever (belief systems, boundary systems, interactive control systems, and diagnostic control systems) affects the organizational performance of SMEs. In this article, a fuzzy logic approach is proposed for the decision-making system in management control in small and medium enterprises. C. Mamdani fuzzy inference system (MFIS) was applied as a decision-making technique to explore the influence of the use of management control tools on the organizational performance of SMEs. Perceptions data analysis is obtained through empirical research.

Behavior Based Robot Navigation: Resolving Behavior Conflicts Using Fuzzy Inference System

Volume 2 ◽  
2004 ◽  
Author(s):  
S. Parasuraman ◽  
V. Ganapathy ◽  
Bijan Shirinzadeh
Keyword(s):  
Control Systems ◽  
Robot Control ◽  
Fuzzy Inference ◽  
Robot Navigation ◽  
Sensory Information ◽  
System Control ◽  
Inference System ◽  
Control Decision ◽  
Behavior Based ◽  
Perception Action

Conflict resolution is the control decision process, which should be taken as a result of the firing among several fuzzy behavior rules. In the Behavior-based Robot Navigation System, control of a robot is shared between a set of perception-action units, called behaviors selection. In other words, the behavior selection is the way that an agent selects the most appropriate or the most relevant next action to take at a particular moment, when facing a particular problem. Based on selective sensory information, each behavior produces immediate reaction to control the robot with respect to a particular objective, i.e., a narrow aspect of the robot’s overall task such as obstacle avoidance or goal seek. Behaviors with different and possibly incommensurable objectives may produce conflicting actions that are seemingly irreconcilable. The main issue in the design of behavior based robot control systems is the formulation of effective mechanism to coordinate the behavior’s activities without any behavior conflicts during navigation. This paper presents the techniques to design the behaviors and resolve the behaviors conflicts, which are based on the Situation Context of Applicability (SCA) of the environments.

Professor Ebrahim Mamdani Memorial Issue

2012 ◽  
Vol 16 (5) ◽  
pp. 591-591
Author(s):  
Yoshinori Arai ◽  
Toshihiko Watanabe
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Special Issue ◽  
Fuzzy Algorithms ◽  
Dynamic Plant ◽  
Memorial Issue ◽  
The Many

On February 22, 2010, Prof. Ebrahim H. Mamdani who devised Mamdani fuzzy inference has passed away. His work in fuzzy inference, which rapidly paved the way to its practical use, helped disseminate Prof. Lotfi Zadehfs fuzzy logic and the development of fuzzy research. Prof. Mamdanifs two papers on fuzzy inference ? gApplication of fuzzy algorithms for control of simple dynamic planth (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) and gAn experiment in linguistic synthesis with a fuzzy logic controllerh (Int. J. of Man-Machine Studies, Vol.7, No.1, pp. 1-13, 1975) with S. Assilian ? enabled fuzzy inference technology to develop dramatically both indicatively and indirectly to where it has been applied, including fuzzy control systems. This special issue honors Prof. Mamdani for his invaluable efforts in these and many other fields. We have asked for submissions by researchers influenced by Prof. Mamdanifs achievements, including his work in fuzzy inference, and have narrowed down to one review and seven full papers. The review by Hirosato Seki and Kai Meng Tay provides an incisive overview on the many aspects of fuzzy inference that Prof. Mamdani brought to light. Prof. Mamdanifs fuzzy inference has become a deterministic technology that can be chosen naturally and that will continue to be influential and survivable well into the future.

scholarly journals Experimental Study of Control Systems Using Fuzzy Inference for Absorption Chillers/Heaters

1993 ◽  
Vol 6 (10) ◽  
pp. 471-478
Author(s):  
Masashi YASUDA ◽  
Atsushi OGAWA ◽  
Kazuhiro HITOMI ◽  
Yoshio OZAWA ◽  
Masahiro MAEKAWA
Keyword(s):  
Experimental Study ◽  
Control Systems ◽  
Fuzzy Inference ◽  
Absorption Chillers
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