Energy-Optimal Waypoint-Following Guidance Considering Autopilot Dynamics

He, Shaoming; Shin, Hyo‐Sang; Tsourdos, Antonios; Lee, Chang-Hun

doi:10.1109/taes.2019.2954149

Cited by 13 publications

(4 citation statements)

References 54 publications

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“…By adding the quadratic penalty function of the equality constraints x k − p w j T at each waypoint to the original cost function (7), the augmented parameterized cost function can be obtained as…”

Section: Constrained Parameterized Differential Dynamic Programmingmentioning

confidence: 99%

“…Planning a reasonable flight trajectory to quickly pass through these points is of great economic benefit to UAV missions and can improve the performance of mission execution. Therefore, planning the flight trajectory of UAVs to reach the necessary intermediate waypoints while optimizing the flight time between waypoints is the foundation for improving the effectiveness of UAVs for reconnaissance, courier, and other missions [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization

Zheng,

Xia,

Lin

et al. 2024

Aerospace

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Unmanned aerial vehicles (UAVs) are required to pass through multiple important waypoints as quickly as possible in courier delivery, enemy reconnaissance and other tasks to eventually reach the target position. There are two important problems to be solved in such tasks: constraining the trajectory to pass through intermediate waypoints and optimizing the flight time between these waypoints. A constrained parameterized differential dynamic programming (C-PDDP) algorithm is proposed for meeting multiple waypoint constraints and free-time constraints between waypoints to deal with these two issues. By considering the intermediate waypoint constraints as a kind of path state constraint, the penalty function method is adopted to constrain the trajectory to pass through the waypoints. For the free-time constraints, the flight times between waypoints are converted into time-invariant parameters and updated at the trajectory instants corresponding to the waypoints. The effectiveness of the proposed C-PDDP algorithm under waypoint constraints and free-time constraints is verified through numerical simulations of the UAV multi-point reconnaissance problem with five different waypoints. After comparing the proposed algorithm with fixed-time constrained DDP (C-DDP), it is found that C-PDDP can optimize the flight time of the trajectory with three segments to 7.35 s, 9.50 s and 6.71 s, respectively. In addition, the maximum error of the optimized trajectory waypoints of the C-PDDP algorithm is 1.06 m, which is much smaller than that (7 m) of the C-DDP algorithm used for comparison. A total of 500 Monte Carlo tests were simulated to demonstrate how the proposed algorithm remains robust to random initial guesses.

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Section: Constrained Parameterized Differential Dynamic Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization

Zheng,

Xia,

Lin

et al. 2024

Aerospace

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“…Key factors influencing the flight of the object, and the hardest to obtain accurately for modelling, are the aerodynamic characteristics, thrust curve, and initial conditions. To maximize the accuracy of the projectile, various types of control methods, algorithms, and target detection methods are utilized, such as H ∞ guidance law [1], proportional navigation guidance and its modifications [2,3], various optimal control methods [4][5][6][7] or model predictive approach [8].…”

Section: Introductionmentioning

confidence: 99%

Study of Model Uncertainties Influence on the Impact Point Dispersion for a Gasodynamicaly Controlled Projectile

Jacewicz

Lichota

Miedziński

et al. 2022

Sensors

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The article presents the analysis of the impact point dispersion reduction using lateral correction thrusters. Two types of control algorithms are used and four sources of uncertainties are taken into account: aerodynamic parameters, thrust curve, initial conditions and IMU errors. The Monte Carlo approach was used for simulations and Circular Error Probable was used as a measure of dispersion. Generic rocket mathematical and simulation model was created in MATLAB/Simulink 2020b environment. Results show that the use of control algorithms greatly reduces the impact point dispersion.

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“…Hence, a series of modern guidance laws based on advanced control theory have been proposed to address the problems. References [5,6] introduced optimal control theory into waypoint-following guidance problems and calculated the guidance law by solving optimal control problems. On the basis of H-infinite theory, reference [7,8] proposed robust guidance laws by regarding unpredictable target maneuvers as bounded unknowns.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function

Liu

et al. 2021

Applied Sciences

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For tactical missiles, sliding mode control and super-twisting algorithms have been widely studied in the area of guidance law design. However, these methods require the information of the target accelerations and the target acceleration derivatives, which is always unknown in practice. In addition, guidance laws utilizing these tools always have chattering phenomena and large acceleration commands. To solve these problems, this article introduces a barrier function based super twisting controller and expands the controller to a multivariable adaptive form. Consequently, a multivariable adaptive super-twisting guidance law based on barrier function is proposed. Moreover, the stability of the guidance law is analyzed, and the effectiveness and the robustness are demonstrated by three simulation examples. Compared with previous guidance laws using sliding mode control or super-twisting algorithm, the one proposed in this paper does not require the information of target accelerations, nor target acceleration derivatives; it has smaller super-twisting gains so that has smaller acceleration commands; it can increase and decrease the gains to follow the target accelerations and maintain the sliding mode, and it does not chatter.

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Energy-Optimal Waypoint-Following Guidance Considering Autopilot Dynamics

Cited by 13 publications

References 54 publications

Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization

Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization

Study of Model Uncertainties Influence on the Impact Point Dispersion for a Gasodynamicaly Controlled Projectile

Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function

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