An adaptive fuzzy logic controller based on real coded quantum-inspired evolutionary algorithm

Shill, Pintu Chandra; Hossain, Amjad; Amin, Md. Faijul; Murase, Kazuyuki

doi:10.1109/fuzzy.2011.6007374

Cited by 4 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous methods such as the genetic algorithm [25], [26], neural networks [27], particle swarm optimization (PSO) [28], [29] harmony search algorithm [30], imperialist competitive algorithm [31], RCQEA [32]- [36], firefly algorithm and galactic swarm optimization [37], bee colony algorithm [38], slime mould algorithm [39], and shark smell optimization [40] have been used in IT2FLC optimization to improve its behaviour on the system.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [56] used a different method for the magnitude of the QRG rotation angle. The RCQEA implementation method to optimize the FLC in the literature and shortcomings [32]- [36] is presented below.…”

Section: Related Workmentioning

confidence: 99%

“…In [32]- [36], the whole domain of inputs and outputs in IT2FLC is divided into equal-sized intervals and MFs are generated in these intervals. The rules of IT2FLC should also be optimized for the system, which is controlled by IT2FLC designed with MFs generated at these intervals, to reach equilibrium quickly.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

Şatır¹,

Baser²

2022

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

In recent years, quantum computing has gained immense popularity with the production of real quantum computers. Researchers have developed quantum-inspired evolutionary algorithms (QIEAs) to solve combinatorial optimization problems and have obtained successful results. As a special case of QIEAs, real-coded quantum evolutionary algorithm (RCQEA) is used in the optimization of high-dimensional complex problems. In this study, a novel mechanism of the quantum rotation gate (QRG) that is used to determine the rotation angle of the qubit in the RCQEA is introduced and implemented to accelerate the evolutionary process and increase the possibility of finding the optimal solution. Moreover, the skeleton of RCQEA is modified by using a clonal selection mechanism, and the real-coded quantum clonal selection algorithm (RCQCSA) is developed. Our proposed QRG accelerates the convergence speed of the algorithm. The main purpose of this study is to present a more effective algorithm that inspires quantum computing principles for optimizing the interval type-2 fuzzy logic controller (IT2FLC) membership functions (MFs). In this study, four different comparisons are made with these two different algorithms that have the original version of QRG and our proposed QRG. Optimized IT2FLC provides stabilization of the inverted pendulum system. The results show that the RCQCSA having our proposed QRG outperforms RCQEA in stabilizing the inverted pendulum system by optimizing the IT2FLC parameters.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

Şatır¹,

Baser²

2022

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

show abstract

“…Evolutionary algorithms have been widely used to derive fuzzy rules and their associates MFs for efficient design of FLCs [14]. In our study, we will consider a specific GA [15] which evolves with a good trade-off between exploration and exploitation, and thus, being a good choice in problems with complex search spaces.…”

Section: B Evolutionary Algorithmmentioning

confidence: 99%

Design of a self-tuning hierarchical fuzzy logic controller for nonlinear swing up and stabilizing control of inverted pendulum

Shill

Amin

Murase

2012

2012 IEEE International Conference on Fuzzy Systems

Self Cite

View full text Add to dashboard Cite

Fuzzy logic controllers suffer from rule explosion problem as the number of rules increases exponentially with the number of input variables. Although several methods have been proposed for eliminating the combinatorial rule explosion problem, none of them is fully satisfactory. In this paper, we describe a new adaptive method for the design of cascaded layer based hierarchical fuzzy system with high input dimensions. This new adaptive hierarchical architecture could be applied to dimensionality reduction in fuzzy modeling. An evolutionary algorithm based off-line leaning algorithm is employed to generate the fuzzy rules and their corresponding membership functions. The evolutionary learning paradigm is a powerful tool to tune the fuzzy logic controllers since it requires no prior knowledge about the system's behavior in order to formulate a set of functional control rules through adaptive learning. The resulting hierarchical fuzzy system has not only an equivalent approximation capability, but less number of fuzzy rules compared with the conventional fuzzy logic system. Simulation studies exhibit competing results with high accuracy that illustrating the effectiveness of the approach.

show abstract

“…Due to its generality, the algorithm is able to tune the fuzzy controller parameters to near-optimal values. Recently, the quantum-inspired evolutionary algorithm has been used to tune the parameters of a fuzzy controller [28]. This algorithm possesses a wider diversity of potential solutions which may be advantageous in searching the optima.…”

Section: Introductionmentioning

confidence: 99%

Design of an Automatically Tuned Fuzzy Controller for a Truck and Multitrailer System

Ren

2013

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The use of a truck and multitrailer system is advantageous because of its ability to transport heavy and large parts with a single powered vehicle. On the other hand, when the system is deployed in an autonomous and unmanned scenario, it remains a challenging task to design a drive controller. Since the drive is only applied to the truck and motivated by successful cases of human expert drivers, a fuzzy controller is developed to generate speed and turn rate commands in order to steer the multi-trailer system to reach the target position with minimum position error. Furthermore, in order to make the controller design efficient and effective, the parameters in the fuzzy controller including the membership functions and rules are automatically tuned using the implementation of efficient particle swarm optimization algorithm instead of relying solely on human expert knowledge. Nearoptimal parameters are then derived and adopted in the controller, and drive commands are then generated. The performance of the truck-and-multi-trailer system under fuzzy control is verified through simulation studies, and satisfactory results are obtained.

show abstract

An adaptive fuzzy logic controller based on real coded quantum-inspired evolutionary algorithm

Cited by 4 publications

References 16 publications

Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

Design of a self-tuning hierarchical fuzzy logic controller for nonlinear swing up and stabilizing control of inverted pendulum

Design of an Automatically Tuned Fuzzy Controller for a Truck and Multitrailer System

Contact Info

Product

Resources

About