Fractional Order Chaotic Model Based Enhanced Equilibrium Optimization Algorithm for Controller Design of 3 DOF Hover Flight System

2021 ◽  
Author(s):  
Abdullah Ates ◽  
YangQuan Chen
Keyword(s):  
Fractional Order ◽  
Optimization Algorithm ◽  
Controller Design ◽  
Initial Conditions ◽  
Random Coefficients ◽  
Feedback Gain ◽  
Initial Population ◽  
Chaotic Oscillator ◽  
Basic Equilibrium ◽  
Oscillator Models

Abstract In this study, the K feedback gain vector parameters that are used for the control of three degree of freedom four rotor quadcopter system (3 DOF Hover) are optimized with the Enhanced Equilibrium Optimization Algorithm (E2O). The E2O algorithm is proposed with using parameters obtained from fractional order chaotic oscillator models instead of random variables. The Basic EO algorithm is inspired by volume-mass balance. In EO algorithm, each particle is called a motion that searches a parameter vector space. However, random coefficients derived from uniform distribution are used in the parameters updating process or in the generation of the initial population. The E2O algorithm was proposed by using vectors obtained from fractional order chaotic oscillators instead of stochastic coefficients in the basic Equilibrium optimization algorithm. Genesio Tesi, Rössler, Lotka Volterra fractional-order chaotic oscillator models were used in the E2O algorithm to optimize K feedback gain vector of 3 DOF Hover. The order and initial conditions the fractional chaotic oscillator models were experimentally adjusted for the control of 3 DOF problem. Thus, suitable fractional-order chaotic models for the problem were obtained. The E2O algorithm results are compared with the Stochastic Multi Parameter Optimization (SMDO) and Discreet Stochastic Optimization (DSO) algorithms for the system’s pitch, roll and yaw angles.

Research on identification of irrational fractional-order systems in frequency domain based on optimization algorithm

2019 ◽  
Vol 41 (15) ◽  
pp. 4351-4357
Author(s):  
Chen Lanfeng ◽  
Xue Dingyu
Keyword(s):  
Frequency Domain ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Controller Design ◽  
Order System ◽  
Model Parameters ◽  
Fractional Order Systems ◽  
Convolution Integral ◽  
Fractional Order Calculus ◽  
Frequency Domain Response

Fractional-order calculus can obtain better results than the integer-order in control theory, so it has become a research hotspot in recent years. However, the structure of the irrational fractional-order system is complex, so its theoretical analysis and controller design are more difficult. In this paper, a method based on convolution integral is proposed to obtain the frequency domain response of the irrational model. Combined with the optimization algorithm, the model parameters are identified. Moreover, the rationalization of the irrational model is realized, which facilitates the analysis and application design of this kind models. Finally, two examples are given to illustrate the effectiveness and feasibility of the method by identifying parameters and rationalization.

scholarly journals Fractional-Order Hidden Attractor Based on the Extended Liu System

2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Yaoyu Wang ◽  
Ling Liu ◽  
Xinshan Cai ◽  
Chongxin Liu ◽  
Yan Wang ◽  
...  
Keyword(s):  
Equilibrium Point ◽  
Fractional Order ◽  
Finite Time ◽  
Initial Conditions ◽  
Equilibrium Points ◽  
Chaotic Oscillator ◽  
Hidden Attractor ◽  
Fractional System ◽  
Line Equilibrium ◽  
Liu System

In this paper, a new commensurate fractional-order chaotic oscillator is presented. The mathematical model with a weak feedback term, which is named hypogenetic flow, is proposed based on the Liu system. And with changing the parameters of the system, the hidden attractor can have no equilibrium points or line equilibrium. What is more interesting is that under the occasion that no equilibrium point can be obtained, the phase trajectory can converge to a minimal field under the lead of some initial conditions, similar to the fixed point. We call it the virtual equilibrium point. On the other hand, when the value of parameters can produce an infinite number of equilibrium points, the line equilibrium points are nonhyperbolic. Moreover than that, there are coexistence attractors, which can present hyperchaos, chaos, period, and virtual equilibrium point. The dynamic characteristics of the system are analyzed, and the parameter estimation is also studied. Then, an electronic circuit implementation of the system is built, which shows the feasibility of the system. At last, for the fractional system with hidden attractors, the finite-time synchronization control of the system is carried out based on the finite-time stability theory of the fractional system. And the effectiveness of the controller is verified by numerical simulation.

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

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