Research on the Unmanned Aerial Vehicle Adaptive Control System based on Fuzzy Control and Chaos Mechanics

In this paper, we conduct research on the unmanned aerial vehicle adaptive control system based on fuzzy control and chaosmechanics. Four rotor aircraft is a kind of nonlinear systems with underactuated, strong coupling characteristic. Although in existing research,through the design of the control algorithm effectively inhibits both for fl ight control effect, but not fundamentally eliminate the effect of aircraft.Dynamic model of unmanned helicopter fl ight control system design is very approximate, need to gradually improve the modeling accuracy, soas to get the exact autonomous fl ight control, so you need to practice constantly required to modeling in the fl ight information, so the unmannedhelicopter fl ight control system to have the ability to retrieve information modeling. This paper proposes the new idea on the issues that will bemeaningful.

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

A Survey of Newtonian Core-Shell Systems with Pseudo High Order Symplectic Integrator and Fast Lyapunov Indicator

Newtonian core-shell systems, as limiting cases of relativistic core-shell models under the two conditions of weak field and slow motion, could account for massive circumstellar dust shells and rings around certain types of star remnants. Because this kind of systems have Hamiltonians that can be split into a main part and a small perturbing part, a good choice of the numerical tool is the pseudo 8th order symplectic integrator of Laskar ＆ Robutel, and, to match the symplectic calculations, a good choice of chaos indicator is the fast Lyapunov indicator （FLI） with two nearby trajectories proposed by Wu, Huang ＆ Zhang. Numerical results show that the FLI is very powerful when describing not only the transition from regular motion to chaos but also the global structure of the phase space of the system.