Adaptive Stabilizing Control by Output-Feedback for a Class of Single-Link Robot System

This paper presents a precision fault reconstruction scheme for a class of nonlinear systems involving unknown input disturbances. First, using the coordinate transformation algorithm, the disturbances and faults of the system are fully decoupled. Therefore, it is possible to eliminate the influence of disturbances to the system, namely, better disturbances robustness. On this basis, the design of a sliding mode state observer makes the most genuine reconstruction realizable, instead of estimation of faults. Furthermore, with the equivalent principle of sliding mode variable structure, the precision reconstruction of arbitrary nonlinear faults is achieved. Finally, the applications of fault reconstruction in a third-order nonlinear theoretical model with disturbances and in a single-link robot system, respectively, have demonstrated the validity of the proposed scheme.

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Evolutionary computing approaches to optimum design of fuzzy logic controller for a flexible robot system

Archives of Control Sciences ◽

10.2478/acsc-2013-0024 ◽

2013 ◽

Vol 23 (4) ◽

pp. 395-412 ◽

Cited By ~ 1

Author(s):

Bidyadhar Subudhi ◽

Subhakanta Ranasingh

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Position Control ◽

Fitness Function ◽

Flexible Manipulator ◽

Position Tracking ◽

Flexible Robot ◽

Robot System ◽

Single Link ◽

Tip Position

Abstract This paper presents the design of a Fuzzy Logic Controller (FLC) whose parameters are optimized by using Genetic Algorithm (GA) and Bacteria Foraging Optimization (BFO) for tip position control of a single link flexible manipulator. The proposed FLC is designed by minimizing the fitness function, which is defined as a function of tip position error, through GA and BFO optimization algorithms achieving perfect tip position tracking of the single link flexible manipulator. Then the tip position responses obtained by using both the above controllers are compared to suggest the best controller for the tip position tracking.

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Global stabilization of memristor-based fractional-order neural networks with delay via output-feedback control

Modern Physics Letters B ◽

10.1142/s0217984917500312 ◽

2017 ◽

Vol 31 (05) ◽

pp. 1750031 ◽

Cited By ~ 13

Author(s):

Jiyang Chen ◽

Chuandong Li ◽

Tingwen Huang ◽

Xujun Yang

Keyword(s):

Neural Networks ◽

Fractional Order ◽

Output Feedback ◽

Differential Inclusions ◽

Direct Method ◽

Sufficient Conditions ◽

Output Feedback Control ◽

Global Stabilization ◽

Lyapunov Direct Method ◽

Stabilizing Control

In this paper, the memristor-based fractional-order neural networks (MFNN) with delay and with two types of stabilizing control are described in detail. Based on the Lyapunov direct method, the theories of set-value maps, differential inclusions and comparison principle, some sufficient conditions and assumptions for global stabilization of this neural network model are established. Finally, two numerical examples are presented to demonstrate the effectiveness and practicability of the obtained results.

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