Research of fuzzy predictive control based on T-S model

Zhang, Yong; Zhang, Zonglong

doi:10.1109/ccdc.2016.7531859

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dimension of uni-modal functions are set as 50 and the specific information of all benchmark functions are shown in Table 1. Some existing algorithms and their variations are compared with the proposed DGSHGS, such as HGS (Yang et al, 2021), COA (Pierezan et al, 2019), FPA (Yang, 2012), Grey Wolf Optimization (GWO) (Mirjalili et al, 2014), Selective Opposition Grey Wolf Optimization (SOGWO) (Dhargupta et al, 2020), Particle Swarm Optimization Based Grey Wolf Optimization (PSOGWO) (Gul et al, 2021), Multi-Strategy Ensemble Grey Wolf Optimizer (MEGWO) (Tu et al, 2019), and Grey Wolf Optimization Algorithm Based on Adaptive Normal Cloud Model (CGWO) (Zhang et al, 2020). In order to realize a fair comparison, the total times of utilizing the evaluation function during the running process of each algorithm are set as 6000.…”

Section: Benchmark Function Tests and Comparisonsmentioning

confidence: 99%

Bi-level intelligent dynamic path planning for an UAV in low-altitude complex urban environment

Wang

Cheng

et al. 2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

An offline and online bi-level structure-based dynamic path planning algorithm is proposed for an unmanned aerial vehicle (UAV) in low-altitude complex urban environment. First, an improved Hunger Games Search (HGS) algorithm is developed to generate an offline optimized path under the UAV’s performance constraints and the known static obstacles’ constraints. The individuals of the proposed algorithm will be divided into multiple groups to increase the population diversity. And then, a dynamic grouping strategy and a quantum-behaved behavior are proposed to solve the premature convergence’s problem and the imbalance problem between exploration and exploitation ability in HGS. To improve the dynamic obstacle avoidance efficiency of the algorithm, the dynamic obstacles are classified into three categories: newly added no-fly zone, known and unknown dynamic obstacles. Then, utilizing the information of the offline optimized path and the airborne sensors, three kinds of online planning strategies—an improved rapid-exploring random tree (RRT), a changing speed strategy, and a novel three-dimensional rolling windows—are introduced to dynamically update the path or speed of the UAV. Simulation results indicated that the improved HGS can enhance the performances of the traditional HGS and outperform other compared algorithms on the benchmark functions. Meanwhile, the online planning strategies can effectively achieve dynamic obstacle avoidance within the constraints of offline path. More specially, the planning time and angles of the local path to avoid the no-fly-zone’s influence are improved by 11.3% and 56.8% through utilizing the improved RRT.

show abstract