Design and tuning of importance-based fuzzy logic controller for a flexible-link manipulator

Authors:
Linda Z. Shi;Mohamed B. Trabia
Affiliations:
University of Nevada, Las Vegas, Department of Mechanical Engineering, Las Vegas, NV;University of Nevada, Las Vegas, Department of Mechanical Engineering, Las Vegas, NV
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Year:
2006

Citing 4
Cited 1

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

Journal of Intelligent and Robotic Systems
Design and tuning of a distributed fuzzy logic controller for flexible-Link manipulators

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
On multistage fuzzy neural network modeling

IEEE Transactions on Fuzzy Systems
Fuzzy learning control for a flexible-link robot

IEEE Transactions on Fuzzy Systems

Soft computing methods applied to the control of a flexible robot manipulator

Applied Soft Computing

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Abstract

Fuzzy logic control presents a computationally efficient and robust alternative to conventional controllers for many systems. This paper presents a distributed fuzzy logic controller (FLC) structure for a flexible-link manipulator based on evaluating the importance degrees of the output variables of the system. The two velocity variables, which have higher importance degrees, are grouped together as the inputs of the Velocity FLC. The two displacement variables, which have lower importance degrees, are used as the inputs of the Displacement FLC. The outputs of those two FLCs are summed up to control the joint of the flexible link. The fuzzy rules of the distributed importance-based FLCs are written based on the expert knowledge, and the parameters of the membership functions of the two FLCs are tuned using nonlinear programming. The distributed importance-based FLC structure is further compared with two other commonly used structures: a Linear Quadratic Regulator and a distributed PD-like FLC. The robustness of the three controllers are tested and compared under various conditions.