The design of particle swarm optimization guidance using a line-of-sight evaluation method

Chen, Kuei-Yi; Lee, Yung-Lung; Liao, Sheng-Ju; Kung, Chien-Chun

doi:10.1016/j.compeleceng.2016.01.023

Cited by 8 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, classical guidance theory and advanced guidance theory require specific formulas to calculate guidance commands, while intelligent algorithms do not require specific guidance command calculation formulas. Heuristic intelligent algorithms such as genetic algorithm [12], ant colony [13][14][15] algorithm, and particle swarm optimization (PSO) [16][17][18][19] algorithm have been utilized to calculate the guidance commands, which further improve the performance of both traditional and modern guidance laws.…”

Section: Introductionmentioning

confidence: 99%

“…A particle swarm optimization guidance (PSOG) method for the nonlinear and dynamic pursuit-evasion optimization problem is designed in [16], and the relative distance is taken to be objective function, which is solved by PSO algorithm. The improved particle swarm optimization guidance (IPSOG) is proposed in [17], and the objective function is changed from relative distance to line-of-sight rate, and the proposed IPSOG algorithm reduces the acceleration requirements of missile compared with PSOG. In [18], a combined PN-IPSOG guidance algorithm is presented, and PN guidance is adopted in the initial stage and then transferred to IPSOG, which can solve the shortcoming of IPSOG that the overload changes greatly in the initial stage.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Particle Swarm Optimization Guidance for Hypersonic Target Interception with Impact Angle Constraint

Chen

Bao

2022

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Considering that the terminal impact angle constraint can improve the interception performance of hypersonic target, a novel particle swarm optimization guidance (NPSOG) algorithm is proposed to satisfy the impact angle constraint. Two-dimensional dynamics engagement mode for hypersonic target interception is formulated. The performance index is positively correlated with the line-of-sight (LOS), LOS rate, and the relative distance between missile and target. The weight coefficients among the three are adaptively adjusted by the fuzzy logic controller. The particle swarm optimization (PSO) algorithm is utilized to generate the guidance commands. Numerical examples are given to verify the performance of the proposed guidance law in various engagement scenarios, and the performance of the algorithm is validated comparing with several heuristic guidance methods and nonheuristic guidance methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Particle Swarm Optimization Guidance for Hypersonic Target Interception with Impact Angle Constraint

Chen

Bao

2022

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

show abstract

“…Differential game guidance regards the target as an intelligent agent in which the target executes the best maneuver strategy to avoid interception [6]. Moreover, heuristic algorithms such as genetic algorithms, ant colony algorithm, and particle swarm optimization (PSO) algorithm [7][8][9][10][11][12][13] have been utilized to calculate the guidance commands, which further improve the performance of both traditional and modern guidance laws.…”

Section: Introductionmentioning

confidence: 99%

Predictive Differential Game Guidance Approach for Hypersonic Target Interception Based on CQPSO

Chen

Bao

2022

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

In the descent phase of the hypersonic target flight trajectory, the hypersonic speed of the target makes the reaction time shorter. Due to overload restrictions, it is challenging for the interceptor to achieve successful interception. To address this problem, a predictive differential game guidance approach based on dynamic optimization algorithm is proposed to relax the interceptor acceleration requirements. Two-dimensional kinematics and dynamics engagement mode for hypersonic target interception is formulated as a nonlinear differential control model in the presence of matched uncertainties. The nonlinear differential model is transformed into a differential game model by combining a performance index. The performance index is positively correlated with the Line-Of-Sight (LOS) rate and control effort of interceptor and negatively correlated with the target maneuver. The Chaotic Quantum Particle Swarm Optimization (CQPSO) algorithm is utilized to address the nonlinear differential game problem to generate the instantaneous and predicted guidance commands for the interceptor and target. Numerical examples are given to verify the performance of the proposed guidance law in various engagement scenarios, including different target maneuvers, initial heading angles and target overload. Moreover, the performance of the algorithm is validated comparing with traditional and state-of-the-art guidance laws.

show abstract

“…However, due to the lack of the guidance subsystem, the control lag caused by the large inertia of UAV cannot be solved. Line of sight (LOS) guidance is the most widely used among guidance methods due to its simplicity and ease of implementation and many control strategies combined with LOS has been proposed to meet various practical needs, including unmanned surface vehicle (Jiang et al, 2020; Liu et al, 2015; Rout et al, 2020; Woo et al, 2019) underwater vehicles (Sahu and Subudhi, 2017; Wang et al, 2020) and unmanned aerial vehicle (Chen et al, 2016; Chen et al, 2019; He et al, 2017; Hu et al, 2020; Wang et al, 2019; Zheng and Zou, 2016; Zuo et al, 2019), these LOS-based control strategies can compensate for control lag, but the optimized indicators are generally designed as the shortest settling time and the minimum path deviation, which inevitably leads to overshoot of the following path. This paper therefore introduces a novel guidance and control system based on the IPSO algorithm, the LOS guidance law and the PI controller, which can meet the needs of fast non-overshoot control.…”

Section: Introductionmentioning

confidence: 99%

A non-overshooting controller for vehicle path following

Wang

Zhang

2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Unmanned pavement construction is of great significance in China, and one of the most important issues is how to follow the designed path near the boundary of the pavement construction area to avoid curbs or railings. In this paper, we raise a simple yet effective controller, named the proportional-integral-radius and improved particle swarm optimization (PIR-IPSO) controller, for fast non-overshooting path-following control of an unmanned articulated vehicle (UAV). Firstly, UAV kinematics model is introduced and segmented UAV steering dynamics model is built through field experiments; then, the raw data collected by differential global positioning system (DGPS) is used to build the measurement error distribution model that simulates positioning errors. Next, line of sight (LOS) guidance law is introduced and the LOS initial parameter is assigned based on human driving behavior. Besides, the initial control parameters tuned by the Ziegler-Nichols (ZN) method are used as the initial iterative parameters of the PSO controller. An improved PSO fitness function is also designed to achieve fast non-overshoot control performance. Experiments show that compared with the PSO, ZN and ZN-PSO controller, the PIR-PSO-based controller has significantly less settling time and almost no overshoot in various UAV initial states. Furthermore, compared with other controllers, the proposed PIR-IPSO-based controller achieves precise non-overshoot control, relatively less settling time and centimeter-level positioning error in various initial deviations.

show abstract

The design of particle swarm optimization guidance using a line-of-sight evaluation method

Cited by 8 publications

References 7 publications

Novel Particle Swarm Optimization Guidance for Hypersonic Target Interception with Impact Angle Constraint

Novel Particle Swarm Optimization Guidance for Hypersonic Target Interception with Impact Angle Constraint

Predictive Differential Game Guidance Approach for Hypersonic Target Interception Based on CQPSO

A non-overshooting controller for vehicle path following

Contact Info

Product

Resources

About