Genetic-Neuro-Fuzzy Controllers for Second Order Control Systems

2011 ◽  
Author(s):  
Danilo Pelusi
Keyword(s):  
Control Systems ◽  
Second Order ◽  
Fuzzy Controllers ◽  
Neuro Fuzzy

Synthesis and optimization of fuzzy controllers in the control systems of pyrolysis reactors

2017 ◽  
Vol 0 (4) ◽  
Author(s):  
Oleksiy V. Kozlov ◽  
Galyna V. Kondratenko ◽  
Yuriy P. Kondratenko
Keyword(s):  
Control Systems ◽  
Fuzzy Controllers

Analysis of Reset Control Systems Consisting of a FORE and Second-Order Loop1

2001 ◽  
Vol 123 (2) ◽  
pp. 279-283 ◽  
Author(s):  
Qian Chen ◽  
Yossi Chait ◽  
C. V. Hollot
Keyword(s):  
Control Systems ◽  
Closed Loop ◽  
Second Order ◽  
Step Response ◽  
Steady State Response ◽  
First Order ◽  
State Response ◽  
Loop Transfer Function ◽  
Performance Specifications ◽  
Reset Control

Reset controllers consist of two parts—a linear compensator and a reset element. The linear compensator is designed, in the usual ways, to meet all closed-loop performance specifications while relaxing the overshoot constraint. Then, the reset element is chosen to meet this remaining step-response specification. In this paper, we consider the case when such linear compensation results in a second-order (loop) transfer function and where a first-order reset element (FORE) is employed. We analyze the closed-loop reset control system addressing performance issues such as stability, steady-state response, and transient performance.

Applying neuro-fuzzy model dFasArt in control systems

2010 ◽  
Vol 23 (7) ◽  
pp. 1053-1063 ◽  
Author(s):  
J. Cano-Izquierdo ◽  
M. Almonacid ◽  
J.J. Ibarrola
Keyword(s):  
Control Systems ◽  
Fuzzy Model ◽  
Neuro Fuzzy

scholarly journals Control solutions in mechatronics systems

2005 ◽  
Vol 18 (3) ◽  
pp. 379-394
Author(s):  
Radu-Emil Precup ◽  
Stefan Preitl
Keyword(s):  
Fuzzy Control ◽  
Control Systems ◽  
Third Order ◽  
Fuzzy Controllers ◽  
Real World Application ◽  
Conventional Solution ◽  
Extended Symmetrical Optimum Method ◽  
Theoretical Results ◽  
Iterative Feedback ◽  
Fuzzy Solutions

This paper presents control solutions dedicated to a class of controlled plants widely used in mechatronics systems, characterized by simplified mathematical models of second-order and third-order plus integral type. The conventional control solution is focused on the Extended Symmetrical Optimum method proposed by the authors in 1996. There are proposed six fuzzy control solutions employing PI-fuzzy controllers. These solutions are based on the approximate equivalence in certain conditions between fuzzy control systems and linear ones, on the application of the modal equivalence principle, and on the transfer of results from the continuous-time conventional solution to the fuzzy solutions via a discrete-time expression of the controller where Prof. Milic R. Stojic's book [1] is used. There is performed the sensitivity analysis of the fuzzy control systems with respect to the parametric variations of the controlled plant, which enables the development of the fuzzy controllers. In addition, the paper presents aspects concerning Iterative Feedback Tuning and Iterative Learning Control in the framework of fuzzy control systems. The theoretical results are validated by considering a real-world application.

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.

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