Optimal Design and Tuning of PID-Type Interval Type-2 Fuzzy Logic Controllers for Delta Parallel Robots

Abstract

In this work, we propose a new method for the optimal design and tuning of a Proportional-Integral-Derivative type (PID-type) interval type-2 fuzzy logic controller (IT2 FLC) for Delta parallel robot trajectory tracking control. The presented methodology starts with an optimal design problem of IT2 FLC. A group of IT2 FLCs are obtained by blurring the membership functions using a variable called blurring degree. By comparing the performance of the controllers, the optimal structure of IT2 FLC is obtained. Then, a multi-objective optimization problem is formulated to tune the scaling factors of the PID-type IT2 FLC. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. Simulation results of the optimized controller are presented and discussed regarding application in the Delta parallel robot. The proposed method provides an effective way to design and tune the PID-type IT2 FLC with a desired control performance.

Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

“…Lu et al have proposed an optimization procedure to design and tune Interval Type-2 Fuzzy Logic Controller (IT2FLC) and PID IT2FLC for the tracking control of Delta parallel robot. 31,32 The aim was to improve the robot control accuracy; however, it was also considered that a good control program must-have characteristic such as simplicity, applicability, robustness, and stability.…”

Type-2 fuzzy logic controller optimized by wavelet networks for mobile robot navigation

“…This problem prompted researchers to propose methods for the automatic optimization of certain parameters of the fuzzy controller, we can quote the work of [24] who designed an evolutionary algorithm to optimize the type 2 fuzzy controller and used it for tracking control of autonomous mobile robots trajectory, there is also the work of [25] who proposed a new method namely the uncontrolled genetic sorting algorithm for optimizing a proportional-integral-derivative type-2fuzzy logic controller for the follow-up control of trajectory of a Delta parallel robot. In the work [26], the authors controlled the cooperation of the robots and the tasks of reaching the target when navigating for several mobile robots using a type-2 fuzzy logic controller optimized by the PSO method.…”

Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system: A comparative assessment

“…6 Various optimization algorithms have been used to work with IT2FLC. These include spiral dynamic algorithm (SDA), 7–9 genetic algorithm (GA), 10–16 particle swarm optimization (PSO), 17–21 artificial bee colony (ABC), 22,23 ant colony optimization (ACO), 24 grey wolf optimizer (GWO) 25,26 and hybrid optimization technique. 27,28 The optimization algorithm is used to obtain the best parameters for IT2FLC.…”